copying to personal repo

.gitignore

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+*.pyc
+*.npz
+*.lock
+*.bak
+*.dat
+cache
+i386
+cnmodel/mechanisms/*.c
+cnmodel/mechanisms/*.o
+cnmodel/an_model/model/*.mexmaci64
+x86_64
+cnmodel/an_model
+doc/build
+build
+dist/
+cnmodel.egg*
+.cache*
+.pytest*
+Figure6*
+project/__pycache__
+project/testing.py
+how
+*.dll
+.idea/
+project/run_data/
+*__pycache__/
+.gitattributes
+
+
+
+

LICENSE

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+BSD 3-Clause License
+
+Copyright (c) 2017 Paul B. Manis, Luke Campagnola, University of North Carolina
+at Chapel Hill
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

README.rst

@@ -0,0 +1,343 @@
+Changes
+=======
+
+This version of cnmodel runs under Python3.6 or later, using Neuron 7.6. New features include a method for changing the data tables on the fly without editing the original tables, and a tool for fitting Exp2Syn "simple" PSCs to the multisite PSC data (or, potentially, to experimental data) to get parameters for the synapse description table.
+
+About CNModel
+=============
+
+CNModel is a Python-based interface to NEURON models of cochlear nucleus neurons, synapses, network connectivity. To drive the model with sound stimuli, the Zilany et al (2010, 2014) auditory periphery model is used to generate auditory nerve spike trains (either via the "cochlea" Python package or by the original MATLAB model; see below). The overall goal is to provide a platform for modeling networks of cochlear nucleus neurons with different levels of biological realism in the context of an accurate simulation of auditory nerve input.
+
+At the lowest level are NEURON-based implementations of ion channels and synapses. Ion channel models for potassium channels are derived largely from the measurements and models of Rothman and Manis (2003abc), and Kanold and Manis (1999, 2001); other channels are derived or modified from the literature. Cell models are in turn based on the insertion of ion channels in densities based on measurements in guinea pig and mouse. The "point" somatic cell models, which form the base set of models in CNModel, replicate the data reported in the original papers. 
+
+The postsynaptic receptor/conductance models are based on kinetic models of glutamate (Raman and Trussell, 1992) and glycinergic receptors, as adjusted to match measurements of synaptic conductances from the mouse cochlear nucleus collected in Xie and Manis, 2013. The glutamate receptor models include desensitization and the effects of internal polyamine receptor block, based on the kinetic scheme of Woodhull (1982).
+
+The presynaptic release model includes a multisite, probabilistic synapse that includes time-dependent changes in release probability based on the Dittman, Kreitzer and Regehr (J Neurosci. 2000 Feb 15;20(4):1374-85) kinetic scheme. Although detailed, this model is computationally expensive and likely not suitable for large scale network simulations. Other simpler models of synapses are also included.
+
+Network connectivity may be defined programmatically, or based on a table of connectivity patterns. A table with estimates derived from the literature is included in the source. 
+
+Included in this package is a set of test suites for different components. An overriding set of unit tests is available to confirm performance of the base models against a set of reference runs, several of which are in turn directly traceable to the original manuscripts. The test suites are useful in verifying performance of the model after modifications of the code or changes in the installation (upgrades of Python or Matlab, for example). 
+
+A manuscript describing this package has been published:
+--------------------------------------------------------
+
+    Paul B. Manis, Luke Campagnola,
+    A biophysical modelling platform of the cochlear nucleus and other auditory circuits: 
+    From channels to networks,
+    Hearing Research,
+    Volume 360,
+    2018,
+    Pages 76-91,
+    ISSN 0378-5955,
+    https://doi.org/10.1016/j.heares.2017.12.017.
+    Open Access: http://www.sciencedirect.com/science/article/pii/S037859551730360X
+
+If you use this package, we would appreciate it if you cite our work in any publications or abstracts.
+
+
+Installation requirements
+-------------------------
+This package depends on the following:
+
+1. Python 3.6 with numpy (1.14.3), scipy (1.1.0), lmfit (0.9.11), matplotlib (3.0.0), faulthandler, and pyqtgraph (0.11.0). The cochlea module requires pandas as well. 
+   An Anaconda install with the appropriate scientific packages works well::
+       
+       conda install python=3.6 pyqt pyqtgraph matplotlib numpy scipy pandas pytest cython
+       pip install resampy
+       pip install lmfit
+       pip install cochlea
+       
+       or:
+       
+       conda create --name py3mpl3 python=3.6 pyqt pyqtgraph matplotlib=3 numpy scipy pandas pytest cython
+       pip install resampy
+       pip install lmfit
+       pip install cochlea
+       
+      
+       (Note that under MacOSX, python 3.7 is usable, but the Windows versio of Matlab R2018b is restricted
+           to python 3.6)
+
+2. A Python-linked version of NEURON (www.neuron.yale.edu). The code has been tested with NEURON 7.5 and 7.6.
+3. A C compiler (gcc). Needed for compilation of mechanisms for NEURON.
+4. The Zilany et al (JASA 2014) auditory periphery model. This can be provided one of two ways:
+    
+   * The Python-based cochlea model by Rudnicki and Hemmert at https://github.com/mrkrd/cochlea. 
+     This is probably best installed via pip per the instructions on the PyPi site: ``pip install cochlea``.
+   * The original MATLAB-based Zilany model; requires MATLAB 2011 or later. A C compiler will also
+     be needed to build this model. The model should be placed in the directory 
+     ``cnmodel/cnmodel/an_model/model``, with the following components: ANmodel.m, complex.c, complex.h, 
+     complex.hpp, ffGn.m, fituaudiogram2.m, mexANmodel.m, model_IHC.c, model_Synapse.c, 
+     and the THRESHOLD_ALL_* files. When cnmodel attempts to access this code the first time, 
+     it will perform the necessary compilation.
+   
+5. neuronvis (optional) available at https://github.com/campagnola/neuronvis or https://github.com/pbmanis/neuronvis).
+   This provides 3D visualization for morphology.
+
+After the code is installed, enter the cnmodel directory and compile the NEURON mod files::
+
+    $ nrnivmodl cnmodel/mechanisms
+
+This will create a directory ("x86_64" or "special") in the top cnmodel directory with the compiled mechanisms.
+
+Under Windows 10, use::
+
+     $ mknrndll cnmodel\mechanisms
+
+to do the same thing. 
+
+
+For more detailed information on setup in a Windows environment, see the file Windows_setup.md. Thanks to Laurel Carney for prompting the generation of this set of instructions, and for identifying issues on Windows.
+
+Windows caveat:
+--------------
+Manually compile the mex files (using Matlab, go to the an_model/models folder, and use mexANmodel.m to compile the files). Then, add the an_model/model folder to the Matlab path, so that it can find the files when needed.
+
+For more detailed information on setup in a Windows environment, see the file Windows_setup.md. Thanks to Laurel Carney for prompting the generation of this set of instructions, and for identifying issues on Windows.
+
+Note: *This package is not yet compatible with Python 3.x. Neuron is not yet compatible with Python 3.x*
+
+
+Testing
+-------
+
+
+Make sure you are in the cnmodel directory, and that you have selected the right environment in Anaconda (in 
+my case, this is usually py3mpl3).
+
+At this point::
+
+After the code is installed, enter the cnmodel directory and compile the NEURON mod files::
+
+    $ nrnivmodl cnmodel/mechanisms
+
+This will create a directory ("x86_64" or "special") in the top cnmodel directory with the compiled mechanisms.
+
+Then::
+
+    $ python examples/toy_model.py
+     
+should generate a plot with several sets of traces showing responses of individual neuron models to depolarizing and hyperpolarizing current steps.
+
+The test suite should be run as::
+
+    $ python test.py
+
+This will test each of the models against reference data, the synapse mechanisms, a number of internal routines, and the auditory nerve model. The tests should pass for each component. Failures may indicate incorrect installation or incorrect function within individual components. These should be corrected before proceeding with simulations.
+
+
+Note
+----
+Under Windows, it may be best to use the standard windows command terminal rather than the "bash" terminal provided by NEURON, at least to run the Python scripts.
+
+Matlab
+------
+This version has been tested with the Matlab AN model of Zilany et al., 2014. 
+Before using, you will need to compile the C code in an_model using Matlab's mex tool. First however, change the following code:
+In model_Synapse.c:
+
+Change (line 63 in the source)::
+
+	$ int    nrep, pxbins, lp,  outsize[2], totalstim;
+
+to::
+	$ int    nrep, pxbins, lp,  totalstim;
+    $ size_t outsize[2];
+    
+Likewise, in model_IHC.c, change::
+
+	$ int    nrep, pxbins, lp,  outsize[2], totalstim, species;
+
+to::
+
+	$ int    nrep, pxbins, lp,  totalstim, species;
+    $ size_t outsize[2];
+
+Then, in Matlab, go to the cnmodel/an_model/model directory, and run::
+
+    $ mexANmodel
+
+Then, cd to an_model and run::
+    
+    $ testANmodel    
+    
+to confirm that the model is installed and working.
+(You may need to add the model directory to the Matlab path.)
+
+
+Figures
+-------
+
+The data for the figures in the manuscript (Manis and Campagnola, Hearing Research 2018) can be generated using the bash script "figures.sh" in the examples subdirectory. 
+From the main cnmodel directory::
+
+    $ ./examples figures.sh fignum
+
+where fignum is one of 2a, 2b, 2c, 3, 4, 5, 6a, 6b, or 7.
+
+Note that Figure 7 may take several **hours** to generate.
+
+Example code and tests
+----------------------
+
+A number of additional tests are included in the examples directory.
+
+    
+- `test_an_model.py` verifies that the auditory nerve model can be run. If necessary, it will compile (using MEX) the mechanisms for matlab. 
+- `test_ccstim.py` tests the generation of different stimulus waveforms by the pulse generator module.
+- `test_cells.py` runs different cell models in current or voltage clamp. 
+  Usage:: 
+      
+      test_cells.py celltype species[-h] [--type TYPE] [--temp TEMP] [-m MORPHOLOGY]
+                    [--nav NAV] [--ttx] [-p PULSETYPE] [--vc | --cc | --rmp]
+                    
+  For example: ``python test_cells.py bushy mouse --cc --temp 34``
+
+                  
+- `test_cells.py` can run protocols on selected cell models.
+  Usage:: 
+    
+        test_cells.py [-h] [--type TYPE] [--temp TEMP] [-m MORPHOLOGY]
+                      [--nav NAV] [--ttx] [-p PULSETYPE] [--vc | --cc | --rmp]
+                      celltype species
+
+- `test_circuit.py` tests the generation of circuits with populations of cells. No simulations are run.
+- `test_decorator.py` generates an IV curve for the reconstructed cell LC_bushy.hoc (Figure 5B,C)
+- `test_mechanisms.py` runs a voltage clamp I/V protocol on a selected mechanism and displays the result.
+  Usage:: 
+       
+         python test_mechanisms.py <mechname>
+           
+  Available channel mechanisms:
+              
+   ========== ========= ========== ============= ==================
+    CaPCalyx   KIR       bkpkj      hcno          hcnobo           
+    hh         hpkj      ihpyr      ihsgcApical   ihsgcBasalMiddle 
+    ihvcn      jsrna     k_ion      ka            kcnq             
+    kdpyr      kht       kif        kis           klt              
+    kpkj       kpkj2     kpkjslow   kpksk         leak             
+    lkpkj      na        naRsg      na_ion        nacn             
+    nacncoop   nap       napyr      nav11                          
+   ========== ========= ========== ============= ==================
+
+- `test_mso_inputs.py` runs a circuit that creates a point MSO neuron, innervated by bushy cells from independent "ears". This demonstrates how to construct a binaural circuit using CNModel.
+- `test_physiology.py` runs a large VCN circuit that converges onto a single bushy cell. This run can take a long time. The output was used to create Figure 7 of the manuscript.
+- `test_populations.py` tests synaptic connections between two cell types. Usage::
+    
+      python test_populations.py <pre_celltype> <post_celltype>
+      
+- `test_sgc_input_phaselocking.py` tests phase locking with SGC inputs to a bushy cell.
+- `test_sgc_input_PSTH.py` shows SGC inputs and postsynaptic bushy cell PSTHs.
+- `test_sgc_input.py` demonstrates SGC input to a VCN bushy cell.
+- `test_simple_synapses.py` tests simple Exp2Syn inputs to different cell types. Usage::
+    
+      python test_synapses.py <pre_celltype> <post_celltype>
+      
+  Supported cell types: sgc, bushy, tstellate, dstellate, tuberculoventral, pyramidal
+- `test_sound_stim.py` generates spike trains from the selected model (cochlea, matlab) and plots rate-intensity functions for the 3 different SR groups.
+- `test_sounds.py` generates waveforms for different kinds of sounds included in the sounds class.
+- `test_synapses.py` evokes spikes in a presynaptic cell while recording the postsynaptic potential. Usage::
+    
+      python test_synapses.py <pre_celltype> <post_celltype>
+      
+  Supported cell types: sgc, bushy, tstellate, dstellate
+- `toy_model.py` generates IV plots for each of the principal point cell types included in CNModel. This is the code that generates Figure 3 of the manuscript.
+
+Potential Issues and Solutions
+------------------------------
+
+1.  Occasionally one of the AN spike train files, which are stored in the directory `cnmodel/an_model/cache`, become locked. This can occur if the calling routines are aborted (^C, ^Z) in the middle of a transaction accessing the cache file, or perhaps during when parallel processing is enabled and a routine fails or is aborted. In this case, a file with the extension ``".lock"`` exists, which prevents the an_model code from accessing the file. The ``".lock"`` file needs to be deleted from the cache directory.
+    
+  *  First, print a list of the locked files::
+  
+      $ find /path/to/cache -name '*.lock'
+    
+  * Where /path/to/cache may be something like `cnmodel/an_model/cache`. 
+    There is most likely only one such file in the diretory.
+
+  * Next, to delete the files::
+  
+      $ find /path/to/cache -name '*.lock' -delete
+       
+  * Under Windows (and other OS's), you should be able do accomplish the same thing
+    with the File Explorer/Finder, limiting the files by extension.
+    
+   
+References
+----------
+
+1.   Cao XJ, Oertel D. The magnitudes of hyperpolarization-activated and
+low-voltage-activated potassium currents co-vary in neurons of the ventral
+cochlear nucleus. J Neurophysiol. 2011 Aug;106(2):630-40. doi:
+10.1152/jn.00015.2010. Epub 2011 May 11. PubMed PMID: 21562186; PubMed Central
+PMCID: PMC3154804.
+
+2.   Cao XJ, Oertel D. Auditory nerve fibers excite targets through synapses that
+vary in convergence, strength, and short-term plasticity. J Neurophysiol. 2010
+Nov;104(5):2308-20. doi: 10.1152/jn.00451.2010. Epub 2010 Aug 25. PubMed PMID:
+20739600; PubMed Central PMCID: PMC3350034.
+
+3.   Dittman JS, Kreitzer AC, Regehr WG. Interplay between facilitation, depression,
+and residual calcium at three presynaptic terminals. J Neurosci. 2000 
+Feb 15;20(4):1374-85. PubMed PMID: 10662828.
+
+1. Isaacson JS, Walmsley B. Counting quanta: direct measurements of transmitter
+release at a central synapse. Neuron. 1995 Oct;15(4):875-84.
+
+4. Kanold PO, Manis PB. A physiologically based model of discharge pattern
+regulation by transient K+ currents in cochlear nucleus pyramidal cells. J
+Neurophysiol. 2001 Feb;85(2):523-38. PubMed PMID: 11160490.
+
+5.   Kanold PO, Manis PB. Transient potassium currents regulate the discharge
+patterns of dorsal cochlear nucleus pyramidal cells. J Neurosci. 1999 Mar
+15;19(6):2195-208. PubMed PMID: 10066273.
+
+6.   Liu Q, Manis PB, Davis RL. Ih and HCN channels in murine spiral ganglion
+neurons: tonotopic variation, local heterogeneity, and kinetic model. J Assoc Res
+Otolaryngol. 2014 Aug;15(4):585-99. doi: 10.1007/s10162-014-0446-z. Epub 2014 Feb
+21. Erratum in: J Assoc Res Otolaryngol. 2014 Aug;15(4):601. PubMed PMID:
+24558054; PubMed Central PMCID: PMC4141436.
+
+7.   Raman IM, Trussell LO. The kinetics of the response to glutamate and kainate
+in neurons of the avian cochlear nucleus. Neuron. 1992 Jul;9(1):173-86. PubMed
+PMID: 1352983.
+
+8.   Rothman JS, Manis PB. The roles potassium currents play in regulating the
+electrical activity of ventral cochlear nucleus neurons. J Neurophysiol. 2003
+Jun;89(6):3097-113. PubMed PMID: 12783953.
+
+9.  Rothman JS, Manis PB. Kinetic analyses of three distinct potassium
+conductances in ventral cochlear nucleus neurons. J Neurophysiol. 2003
+Jun;89(6):3083-96. PubMed PMID: 12783952.
+
+10.   Rothman JS, Manis PB. Differential expression of three distinct potassium
+currents in the ventral cochlear nucleus. J Neurophysiol. 2003 Jun;89(6):3070-82.
+PubMed PMID: 12783951.
+
+11.   Rothman JS, Young ED, Manis PB. Convergence of auditory nerve fibers onto
+bushy cells in the ventral cochlear nucleus: implications of a computational
+model. J Neurophysiol. 1993 Dec;70(6):2562-83. PubMed PMID: 8120599.
+
+12.   Woodhull AM. Ionic blockage of sodium channels in nerve. J Gen Physiol. 1973
+Jun;61(6):687-708. PubMed PMID: 4541078; PubMed Central PMCID: PMC2203489.
+
+13.   Xie R, Manis PB. Target-specific IPSC kinetics promote temporal processing in 
+auditory parallel pathways. J Neurosci. 2013 Jan 23;33(4):1598-614. doi:
+10.1523/JNEUROSCI.2541-12.2013. PubMed PMID: 23345233; PubMed Central PMCID:
+PMC3737999.
+
+14.   Zilany MS, Bruce IC, Carney LH. Updated parameters and expanded simulation
+options for a model of the auditory periphery. J Acoust Soc Am. 2014
+Jan;135(1):283-6. doi: 10.1121/1.4837815. PubMed PMID: 24437768; PubMed Central
+PMCID: PMC3985897.
+
+15.   Zilany MS, Carney LH. Power-law dynamics in an auditory-nerve model can
+account for neural adaptation to sound-level statistics. J Neurosci. 2010 Aug
+4;30(31):10380-90. doi: 10.1523/JNEUROSCI.0647-10.2010. PubMed PMID: 20685981;
+PubMed Central PMCID: PMC2935089.
+
+16.   Zilany MS, Bruce IC, Nelson PC, Carney LH. A phenomenological model of the
+synapse between the inner hair cell and auditory nerve: long-term adaptation with
+power-law dynamics. J Acoust Soc Am. 2009 Nov;126(5):2390-412. doi:
+10.1121/1.3238250. PubMed PMID: 19894822; PubMed Central PMCID: PMC2787068.
+

Windows_setup.md

@@ -0,0 +1,321 @@
+CNMODEL Setup for Windows Systems
+=================================
+
+(as of 2 March 2018, tested on Windows 10)
+On a bare system, you will need to install the following packages:
+
+    1. Anaconda Python 2.7 (this may also include the Microsoft Visual Studio for Python)
+    2. git
+    3. msvc2.7forpython (required for cochlea)
+    4. external python package from PyPi: cochlea
+    5. NEURON7.5 (this must be installed last)
+    6. cnmodel, cloned from the main repository with git
+    7. build the .dll file for the Neuron mechanisms used in cnmodel
+
+Some installs are accomplished via a graphical interface (anaconda, git, msvc, neuron). 
+Other parts of the install are done from the terminal (Windows "Command Prompt" - look in the Windows Accessories)
+
+Follow the instructions below to do this installation. The order only partially matters - msvcforpython must be installed before cochlea can be built; python should be installed before NEURON; and git is required get the cnmodel repository.
+
+Step 1: Install Anaconda python for Python 2.7 for your system. 
+Note that NEURON is not yet compatible with Python 3.x
+
+https://repo.continuum.io/archive/
+The most recent versions of the Anaconda installer (5.x) are OK to use.
+Check the box to set DOS path variables so can run from command prompt.
+Install only for yourself.
+
+If you want, create an environment for python 2.7, Using the standard windows terminal ("Command Prompt"), install the additional required packages::
+
+    conda create --name models python=2.7 pyqt pyqtgraph matplotlib numpy scipy pandas pytest faulthandler
+
+Note: this may take a while, and will install many other packages that are required dependencies.
+
+Now, activate the environment::
+
+    conda activate models
+
+Or else, just install everything to the root environment:
+conda install pyqt pyqtgraph matplotlib numpy scipy pytest faulthandler
+
+If you are working with a previous Anaconda installation, you might need to do these steps::
+
+    conda update --all
+    conda update qt
+
+The following must be retrieved from the PyPi repository (I had problems with the one in the anaconda repo)::
+
+    pip install lmfit
+
+Step 2: Install git (a widely used source control module)::
+
+    https://git-scm.com/downloads
+
+Step 3: Install MS visual studio for python::
+
+    https://www.visualstudio.com/vs/python/
+
+Step 4: Install cochlea (Rudnicki and Hemmert's implementation of several models under python)::
+
+    pip install cochlea
+
+This should build quickly and have no errors. 
+
+Step 5: Install NEURON7.5 for mswindows from::
+    
+    www.neuron.yale.edu. 
+
+Restart the command window so that the paths to the python installation are updated.
+Make sure that python and neuron are installed correctly together by testing the ability to import NEURON::
+
+    (base) pbmanis: Python $ python
+    Python 2.7.14 |Anaconda custom (64-bit)| (default, Dec  7 2017, 11:07:58)
+    [GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)] on darwin
+    Type "help", "copyright", "credits" or "license" for more information.
+    >>> from neuron import h
+    NEURON -- VERSION 7.5 master (6b4c19f) 2017-09-25
+    Duke, Yale, and the BlueBrain Project -- Copyright 1984-2016
+    See http://neuron.yale.edu/neuron/credits
+
+    >>>
+    
+(^Z to exit)
+
+Step 6: Go to where you want to put the model code repository, and clone the repo::
+
+    git clone https://github.com/cnmodel/cnmodel.git
+
+Jump into the cnmodel directory.
+
+Step 7: Find the mknrndll gui from where you installed neuron with the Explorer (the program in the nrn folder). Run the program, and select the directory cnmodel\cnmodel\mechanisms, then build.
+This will make nrnmech.dll in the mechanisms directory.
+Copy the nrnmech.dll into the main cnmodel directory.
+
+Step 8: Run::
+
+    python setup.py develop
+    
+in the main cnmodel directory to make a library version in the anaconda site-packages directory. This will make cnmodel accessible as an import into python from any location. However, you will (on Windows) need to copy the nrnmech.dll file to the directory that you are starting in. (If you import cnmodel and there is not a list of mechanisms after the Neuron banner, then nrnmech.dll was not found).
+
+Running
+=======
+
+The following should be all that is needed once everything is set up.
+
+Go the the cnmodel main directory.
+
+if you set up a Python environment, activate it to make sure you have the right dependencies available.
+
+Now, these scripts should run just fine::
+
+    python examples/test_mechanisms.py klt
+    python examples/toy_model.py 
+    python examples/test_synapses.py sgc bushy
+    python examples/test_phaselocking.py
+
+You should also be able to run the full battery of tests::
+
+    python test.py
+
+All the tests should pass (except perhaps with the exception dstellate->dstellate; this passes under MacOSX so we will need to investigate why it fails under Windows). MacOSX and Windows test results are listed below.
+
+Notes
+=====
+
+1. You cannot use the "bash" terminal window that comes with neuron - it doesn't set the paths correctly for access to the anaconda python.
+
+2. Some of the graphical displays are not correctly sized in Windows. You may need to expand the window to see all the plots (specifically, toy_model has this problem).
+
+3. Report occurrences of failures to "import PyQt4" to: https://github.com/cnmodel/cnmodel/issues.
+
+4. The test suite (python test.py) may fail on one test: dstellate -> dstellate synapses (as of 23Feb2018), and if you do not have Matlab, the Matlab test will be skipped.
+
+
+Things solved with Windows
+==========================
+
+1. The test suite works and most tests pass (python test.py).
+2. Qt5 and current anaconda install are acceptable (we are no longer trying to force Qt4).
+3. The prior dependency on pylibrary has been removed.
+
+
+Potential problems
+==================
+
+This software has been tested primarily on Mac OSX and Linux systems. Some tests have been performed on Windows. If issues are found, please report them via github as an issue (as noted above).
+
+Test results
+============
+
+Windows 10 (24 Feb 2018; Qt5 Branch)
+------------------------------------
+
+::
+    C:\Users\pbmanis\Desktop\Python\cnmodel>python test.py > testresults.txt
+    NEURON -- VERSION 7.5 master (6b4c19f) 2017-09-25
+    Duke, Yale, and the BlueBrain Project -- Copyright 1984-2016
+    See http://neuron.yale.edu/neuron/credits
+
+    loading membrane mechanisms from C:\Users\pbmanis\Desktop\Python\cnmodel\nrnmech.dll
+    Additional mechanisms from files
+     CaPCalyx.mod Gly5GC.mod Gly5PL.mod Gly5State.mod Gly6S.mod Iclamp2.mod NMDA.mod NMDA_Kampa.mod
+      ampa_trussell.mod bkpkj.mod cabpump.mod cadiff.mod cadyn.mod cap.mod capmp.mod capump.mod cleftXmtr.mod
+       gly.mod gly2.mod hcno.mod hcno_bo.mod iStim.mod ihpkj.mod ihpyr.mod ihsgc_apical.mod ihsgc_
+       basalmiddle.mod ihvcn.mod inav11.mod jsrnaf.mod ka.mod kcnq.mod kdpyr.mod kht.mod kif.mod 
+       kir.mod kis.mod klt.mod kpkj.mod kpkj2.mod kpkjslow.mod kpksk.mod leak.mod multisite.mod 
+       na.mod nacn.mod nacncoop.mod nap.mod napyr.mod pkjlk.mod rsg.mod vecevent.mod
+
+     Testing with flags: -v --tb=short cnmodel/
+    ============================= test session starts =============================
+    platform win32 -- Python 2.7.14, pytest-3.3.2, py-1.5.2, pluggy-0.6.0 -- C:\Users\pbmanis\Anaconda2\python.exe
+    cachedir: .cache
+    rootdir: C:\Users\pbmanis\Desktop\Python\cnmodel, inifile:
+    collecting ... collected 36 items
+
+    cnmodel/an_model/tests/test_cache.py::test_cache PASSED                  [  2%]
+    cnmodel/an_model/tests/test_cache.py::test_parallel PASSED               [  5%]
+    cnmodel/cells/tests/test_cells.py::test_bushy PASSED                     [  8%]
+    cnmodel/cells/tests/test_cells.py::test_bushy21 PASSED                   [ 11%]
+    cnmodel/cells/tests/test_cells.py::test_bushy_mouse PASSED               [ 13%]
+    cnmodel/cells/tests/test_cells.py::test_tstellate PASSED                 [ 16%]
+    cnmodel/cells/tests/test_cells.py::test_tstellate_mouse PASSED           [ 19%]
+    cnmodel/cells/tests/test_cells.py::test_tstellatet PASSED                [ 22%]
+    cnmodel/cells/tests/test_cells.py::test_dstellate PASSED                 [ 25%]
+    cnmodel/cells/tests/test_cells.py::test_dstellate_mouse PASSED           [ 27%]
+    cnmodel/cells/tests/test_cells.py::test_octopus PASSED                   [ 30%]
+    cnmodel/cells/tests/test_cells.py::test_pyramidal PASSED                 [ 33%]
+    cnmodel/cells/tests/test_cells.py::test_tuberculoventral PASSED          [ 36%]
+    cnmodel/cells/tests/test_cells.py::test_cartwheel PASSED                 [ 38%]
+    cnmodel/cells/tests/test_cells.py::test_sgc_basal_middle PASSED          [ 41%]
+    cnmodel/cells/tests/test_cells.py::test_sgc_apical PASSED                [ 44%]
+    cnmodel/data/tests/test_db.py::test_db PASSED                            [ 47%]
+    cnmodel/mechanisms/tests/test_mechanisms.py::test_max_open_probability PASSED [ 50%]
+    cnmodel/synapses/tests/test_psd.py::test_sgc_bushy_psd PASSED            [ 52%]
+    cnmodel/synapses/tests/test_psd.py::test_sgc_tstellate_psd PASSED        [ 55%]
+    cnmodel/synapses/tests/test_psd.py::test_sgc_dstellate_psd PASSED        [ 58%]
+    cnmodel/synapses/tests/test_psd.py::test_sgc_octopus_psd PASSED          [ 61%]
+    cnmodel/synapses/tests/test_synapses.py::test_sgc_bushy PASSED           [ 63%]
+    cnmodel/synapses/tests/test_synapses.py::test_sgc_tstellate PASSED       [ 66%]
+    cnmodel/synapses/tests/test_synapses.py::test_sgc_tstellate2 PASSED      [ 69%]
+    cnmodel/synapses/tests/test_synapses.py::test_sgc_dstellate PASSED       [ 72%]
+    cnmodel/synapses/tests/test_synapses.py::test_dstellate_bushy PASSED     [ 75%]
+    cnmodel/synapses/tests/test_synapses.py::test_dstellate_tstellate PASSED [ 77%]
+    cnmodel/synapses/tests/test_synapses.py::test_dstellate_dstellate FAILED [ 80%]
+    cnmodel/util/tests/test_expfitting.py::test_fit1 PASSED                  [ 83%]
+    cnmodel/util/tests/test_expfitting.py::test_fit2 PASSED                  [ 86%]
+    cnmodel/util/tests/test_matlab.py::test_matlab SKIPPED                   [ 88%]
+    cnmodel/util/tests/test_sound.py::test_conversions PASSED                [ 91%]
+    cnmodel/util/tests/test_sound.py::test_tonepip PASSED                    [ 94%]
+    cnmodel/util/tests/test_sound.py::test_noisepip PASSED                   [ 97%]
+    cnmodel/util/tests/test_stim.py::test_make_pulse PASSED                  [100%]
+
+    ================================== FAILURES ===================================
+    __________________________ test_dstellate_dstellate ___________________________
+    cnmodel\synapses\tests\test_synapses.py:40: in test_dstellate_dstellate
+        SynapseTester('dstellate', 'dstellate')
+    cnmodel\synapses\tests\test_synapses.py:72: in __init__
+        UserTester.__init__(self, "%s_%s" % (pre, post), pre, post)
+    cnmodel\util\user_tester.py:33: in __init__
+        self.assert_test_info(*args, **kwds)
+    cnmodel\synapses\tests\test_synapses.py:108: in assert_test_info
+        super(SynapseTester, self).assert_test_info(*args, **kwds)
+    cnmodel\util\user_tester.py:127: in assert_test_info
+        self.compare_results(result, expect)
+    cnmodel\util\user_tester.py:60: in compare_results
+        self.compare_results(info[k], expect[k])
+    cnmodel\util\user_tester.py:63: in compare_results
+        self.compare_results(info[i], expect[i])
+    cnmodel\util\user_tester.py:79: in compare_results
+        self.compare_results(info[k], expect[k])
+    cnmodel\util\user_tester.py:71: in compare_results
+        assert np.all(inans == enans)
+    E   AssertionError
+    ---------------------------- Captured stdout call -----------------------------
+    << D-stellate: JSR Stellate Type I-II cell model created >>
+    << D-stellate: JSR Stellate Type I-II cell model created >>
+    Elapsed time for 1 Repetions: 0.355309
+    =============== 1 failed, 34 passed, 1 skipped in 96.33 seconds ===============
+
+
+
+Mac OSX (24 Feb 2018; Qt5 Branch)
+---------------------
+
+::
+    (base) pbmanis: cnmodel [qt5+]$ python test.py
+    NEURON -- VERSION 7.5 master (6b4c19f) 2017-09-25
+    Duke, Yale, and the BlueBrain Project -- Copyright 1984-2016
+    See http://neuron.yale.edu/neuron/credits
+
+    loading membrane mechanisms from x86_64/.libs/libnrnmech.so
+    Additional mechanisms from files
+     cnmodel/mechanisms//CaPCalyx.mod cnmodel/mechanisms//Gly5GC.mod cnmodel/mechanisms//Gly5PL.mod 
+     cnmodel/mechanisms//Gly5State.mod cnmodel/mechanisms//Gly6S.mod cnmodel/mechanisms//Iclamp2.mod 
+     cnmodel/mechanisms//NMDA.mod cnmodel/mechanisms//NMDA_Kampa.mod 
+     cnmodel/mechanisms//ampa_trussell.mod cnmodel/mechanisms//bkpkj.mod 
+     cnmodel/mechanisms//cabpump.mod cnmodel/mechanisms//cadiff.mod cnmodel/mechanisms//cadyn.mod 
+     cnmodel/mechanisms//cap.mod cnmodel/mechanisms//capmp.mod 
+     cnmodel/mechanisms//capump.mod cnmodel/mechanisms//cleftXmtr.mod 
+     cnmodel/mechanisms//gly.mod cnmodel/mechanisms//gly2.mod cnmodel/mechanisms//hcno.mod 
+     cnmodel/mechanisms//hcno_bo.mod cnmodel/mechanisms//iStim.mod cnmodel/mechanisms//ihpkj.mod 
+     cnmodel/mechanisms//ihpyr.mod cnmodel/mechanisms//ihsgc_apical.mod 
+     cnmodel/mechanisms//ihsgc_basalmiddle.mod cnmodel/mechanisms//ihvcn.mod 
+     cnmodel/mechanisms//inav11.mod cnmodel/mechanisms//jsrnaf.mod 
+     cnmodel/mechanisms//ka.mod cnmodel/mechanisms//kcnq.mod cnmodel/mechanisms//kdpyr.mod 
+     cnmodel/mechanisms//kht.mod cnmodel/mechanisms//kif.mod cnmodel/mechanisms//kir.mod 
+     cnmodel/mechanisms//kis.mod cnmodel/mechanisms//klt.mod cnmodel/mechanisms//kpkj.mod 
+     cnmodel/mechanisms//kpkj2.mod cnmodel/mechanisms//kpkjslow.mod cnmodel/mechanisms//kpksk.mod 
+     cnmodel/mechanisms//leak.mod cnmodel/mechanisms//multisite.mod cnmodel/mechanisms//na.mod 
+     cnmodel/mechanisms//nacn.mod cnmodel/mechanisms//nacncoop.mod cnmodel/mechanisms//nap.mod c
+     nmodel/mechanisms//napyr.mod cnmodel/mechanisms//pkjlk.mod cnmodel/mechanisms//rsg.mod 
+     cnmodel/mechanisms//vecevent.mod
+    Testing with flags: -v --tb=short cnmodel/
+    =============================================== test session starts ===============================================
+    platform darwin -- Python 2.7.14, pytest-3.0.7, py-1.4.33, pluggy-0.4.0 -- /Users/pbmanis/anaconda/bin/python
+    cachedir: .cache
+    rootdir: /Users/pbmanis/Desktop/Python/cnmodel, inifile:
+    collected 36 items
+
+    cnmodel/an_model/tests/test_cache.py::test_cache PASSED
+    cnmodel/an_model/tests/test_cache.py::test_parallel PASSED
+    cnmodel/cells/tests/test_cells.py::test_bushy PASSED
+    cnmodel/cells/tests/test_cells.py::test_bushy21 PASSED
+    cnmodel/cells/tests/test_cells.py::test_bushy_mouse PASSED
+    cnmodel/cells/tests/test_cells.py::test_tstellate PASSED
+    cnmodel/cells/tests/test_cells.py::test_tstellate_mouse PASSED
+    cnmodel/cells/tests/test_cells.py::test_tstellatet PASSED
+    cnmodel/cells/tests/test_cells.py::test_dstellate PASSED
+    cnmodel/cells/tests/test_cells.py::test_dstellate_mouse PASSED
+    cnmodel/cells/tests/test_cells.py::test_octopus PASSED
+    cnmodel/cells/tests/test_cells.py::test_pyramidal PASSED
+    cnmodel/cells/tests/test_cells.py::test_tuberculoventral PASSED
+    cnmodel/cells/tests/test_cells.py::test_cartwheel PASSED
+    cnmodel/cells/tests/test_cells.py::test_sgc_basal_middle PASSED
+    cnmodel/cells/tests/test_cells.py::test_sgc_apical PASSED
+    cnmodel/data/tests/test_db.py::test_db PASSED
+    cnmodel/mechanisms/tests/test_mechanisms.py::test_max_open_probability PASSED
+    cnmodel/synapses/tests/test_psd.py::test_sgc_bushy_psd PASSED
+    cnmodel/synapses/tests/test_psd.py::test_sgc_tstellate_psd PASSED
+    cnmodel/synapses/tests/test_psd.py::test_sgc_dstellate_psd PASSED
+    cnmodel/synapses/tests/test_psd.py::test_sgc_octopus_psd PASSED
+    cnmodel/synapses/tests/test_synapses.py::test_sgc_bushy PASSED
+    cnmodel/synapses/tests/test_synapses.py::test_sgc_tstellate PASSED
+    cnmodel/synapses/tests/test_synapses.py::test_sgc_tstellate2 PASSED
+    cnmodel/synapses/tests/test_synapses.py::test_sgc_dstellate PASSED
+    cnmodel/synapses/tests/test_synapses.py::test_dstellate_bushy PASSED
+    cnmodel/synapses/tests/test_synapses.py::test_dstellate_tstellate PASSED
+    cnmodel/synapses/tests/test_synapses.py::test_dstellate_dstellate PASSED
+    cnmodel/util/tests/test_expfitting.py::test_fit1 PASSED
+    cnmodel/util/tests/test_expfitting.py::test_fit2 PASSED
+    cnmodel/util/tests/test_matlab.py::test_matlab PASSED
+    cnmodel/util/tests/test_sound.py::test_conversions PASSED
+    cnmodel/util/tests/test_sound.py::test_tonepip PASSED
+    cnmodel/util/tests/test_sound.py::test_noisepip PASSED
+    cnmodel/util/tests/test_stim.py::test_make_pulse PASSED
+
+    =========================================== 36 passed in 94.05 seconds ============================================
+    (base) pbmanis: cnmodel [qt5+]$
+
+
+
+

_config.yml

@@ -0,0 +1 @@
+theme: jekyll-theme-cayman

cnmodel/__init__.py

@@ -0,0 +1,25 @@
+__author__ = "Paul B. Manis and Luke Campagnola"
+__version__ = "0.32a"
+
+try:
+    import faulthandler
+
+    faulthandler.enable()
+except ImportError:
+    pass
+
+import logging
+
+logging.basicConfig(level=logging.INFO, format="[%(process)s] %(message)s")
+import os
+
+dirname = os.path.abspath(os.path.dirname(__file__))
+libpath = os.path.join(dirname, "..")
+import neuron
+
+try:
+    neuron.h.MultiSiteSynapse
+except AttributeError:
+    neuron.load_mechanisms(libpath)
+# flag to allow unit tests to store / overwrite test results
+AUDIT_TESTS = False

cnmodel/cells/__init__.py

@@ -0,0 +1,23 @@
+"""
+Cell definitions for models.
+
+This class includes a number of different cell definitions and default
+conductances for point models. 
+"""
+
+from .bushy import *
+from .tstellate import *
+from .dstellate import *
+from .cartwheel import *
+from .pyramidal import *
+from .sgc import *
+from .octopus import *
+from .tuberculoventral import *
+from .msoprincipal import *
+from .hh import *
+
+from .cell import Cell
+
+
+def cell_from_section(sec):
+    return Cell.from_section(sec)

cnmodel/cells/bushy.py

@@ -0,0 +1,967 @@
+from __future__ import print_function
+from neuron import h
+from collections import OrderedDict
+from .cell import Cell
+from .. import synapses
+from ..util import nstomho
+from ..util import Params
+import numpy as np
+from .. import data
+import pprint
+
+pp = pprint.PrettyPrinter(indent=4, width=60)
+
+__all__ = ["Bushy", "BushyRothman"]
+
+
+class Bushy(Cell):
+
+    type = "bushy"
+
+    @classmethod
+    def create(cls, model="RM03", **kwds):
+        if model == "RM03":
+            return BushyRothman(**kwds)
+        else:
+            raise ValueError("Bushy model %s is unknown", model)
+
+    def make_psd(self, terminal, psd_type, **kwds):
+        """
+        Connect a presynaptic terminal to one post section at the specified location, with the fraction
+        of the "standard" conductance determined by gbar.
+        The default condition is designed to pass the unit test (loc=0.5)
+        
+        Parameters
+        ----------
+        terminal : Presynaptic terminal (NEURON object)
+        
+        psd_type : either simple or multisite PSD for bushy cell
+        
+        kwds: dictionary of options. 
+            Two are currently handled:
+            postsite : expect a list consisting of [sectionno, location (float)]
+            AMPAScale : float to scale the ampa currents
+        
+        """
+        if (
+            "postsite" in kwds
+        ):  # use a defined location instead of the default (soma(0.5)
+            postsite = kwds["postsite"]
+            loc = postsite[1]  # where on the section?
+            uname = (
+                "sections[%d]" % postsite[0]
+            )  # make a name to look up the neuron section object
+            post_sec = self.hr.get_section(uname)  # Tell us where to put the synapse.
+        else:
+            loc = 0.5
+            post_sec = self.soma
+
+        if psd_type == "simple":
+            if terminal.cell.type in ["sgc", "dstellate", "tuberculoventral"]:
+                weight = data.get(
+                    "%s_synapse" % terminal.cell.type,
+                    species=self.species,
+                    post_type=self.type,
+                    field="weight",
+                )
+                tau1 = data.get(
+                    "%s_synapse" % terminal.cell.type,
+                    species=self.species,
+                    post_type=self.type,
+                    field="tau1",
+                )
+                tau2 = data.get(
+                    "%s_synapse" % terminal.cell.type,
+                    species=self.species,
+                    post_type=self.type,
+                    field="tau2",
+                )
+                erev = data.get(
+                    "%s_synapse" % terminal.cell.type,
+                    species=self.species,
+                    post_type=self.type,
+                    field="erev",
+                )
+                return self.make_exp2_psd(
+                    post_sec,
+                    terminal,
+                    weight=weight,
+                    loc=loc,
+                    tau1=tau1,
+                    tau2=tau2,
+                    erev=erev,
+                )
+            else:
+                raise TypeError(
+                    "Cannot make simple PSD for %s => %s"
+                    % (terminal.cell.type, self.type)
+                )
+
+        elif psd_type == "multisite":
+            if terminal.cell.type == "sgc":
+                # Max conductances for the glu mechanisms are calibrated by
+                # running `synapses/tests/test_psd.py`. The test should fail
+                # if these values are incorrect
+                self.AMPAR_gmax = (
+                    data.get(
+                        "sgc_synapse",
+                        species=self.species,
+                        post_type=self.type,
+                        field="AMPAR_gmax",
+                    )
+                    * 1e3
+                )
+                self.NMDAR_gmax = (
+                    data.get(
+                        "sgc_synapse",
+                        species=self.species,
+                        post_type=self.type,
+                        field="NMDAR_gmax",
+                    )
+                    * 1e3
+                )
+                self.Pr = data.get(
+                    "sgc_synapse", species=self.species, post_type=self.type, field="Pr"
+                )
+                self.NMDAR_vshift = data.get(
+                    "sgc_synapse",
+                    species=self.species,
+                    post_type=self.type,
+                    field="NMDAR_vshift",
+                )
+                # adjust gmax to correct for initial Pr
+                self.AMPAR_gmax = self.AMPAR_gmax / self.Pr
+                self.NMDAR_gmax = self.NMDAR_gmax / self.Pr
+
+                #               original values (now in synapses.py):
+                #                self.AMPA_gmax = 3.314707700918133*1e3  # factor of 1e3 scales to pS (.mod mechanisms) from nS.
+                #                self.NMDA_gmax = 0.4531929783503451*1e3
+                if "AMPAScale" in kwds:  # normally, this should not be done!
+                    self.AMPAR_gmax = (
+                        self.AMPAR_gmax * kwds["AMPAScale"]
+                    )  # allow scaling of AMPA conductances
+                if "NMDAScale" in kwds:
+                    self.NMDAR_gmax = self.NMDAR_gmax * kwds["NMDAScale"]  # and NMDA...
+                return self.make_glu_psd(
+                    post_sec,
+                    terminal,
+                    self.AMPAR_gmax,
+                    self.NMDAR_gmax,
+                    loc=loc,
+                    nmda_vshift=self.NMDAR_vshift,
+                )
+            elif terminal.cell.type == "dstellate":
+                return self.make_gly_psd(post_sec, terminal, psdtype="glyslow", loc=loc)
+            elif terminal.cell.type == "tuberculoventral":
+                return self.make_gly_psd(post_sec, terminal, psdtype="glyslow", loc=loc)
+            else:
+                raise TypeError(
+                    "Cannot make PSD for %s => %s" % (terminal.cell.type, self.type)
+                )
+        else:
+            raise ValueError("Unsupported psd type %s" % psd_type)
+
+    def make_terminal(self, post_cell, term_type, **kwds):
+        if term_type == "simple":
+            return synapses.SimpleTerminal(self.soma, post_cell, **kwds)
+
+        elif term_type == "multisite":
+            if post_cell.type in ["mso"]:
+                nzones = data.get(
+                    "bushy_synapse",
+                    species=self.species,
+                    post_type=post_cell.type,
+                    field="n_rsites",
+                )
+                delay = data.get(
+                    "bushy_synapse",
+                    species=self.species,
+                    post_type=post_cell.type,
+                    field="delay",
+                )
+            else:
+                raise NotImplementedError(
+                    "No knowledge as to how to connect Bushy cell to cell type %s"
+                    % type(post_cell)
+                )
+            pre_sec = self.soma
+            return synapses.StochasticTerminal(
+                pre_sec,
+                post_cell,
+                nzones=nzones,
+                spike_source=self.spike_source,
+                delay=delay,
+                **kwds,
+            )
+        else:
+            raise ValueError("Unsupported terminal type %s" % term_type)
+
+
+class BushyRothman(Bushy):
+    """
+    VCN bushy cell models.
+        Rothman and Manis, 2003abc (Type II, Type II-I)
+        Xie and Manis, 2013
+    """
+
+    def __init__(
+        self,
+        morphology=None,
+        decorator=None,
+        nach=None,
+        ttx=False,
+        species="guineapig",
+        modelType=None,
+        modelName=None,
+        debug=False,
+        temperature=None,
+    ):
+        """
+        Create a bushy cell, using the default parameters for guinea pig from
+        R&M2003, as a type II cell.
+        Additional modifications to the cell can be made by calling methods below.
+        
+        Parameters
+        ----------
+        morphology : string (default: None)
+            Name of a .hoc file representing the morphology. This file is used to constructe
+            an electrotonic (cable) model. 
+            If None (default), then a "point" (really, single cylinder) model is made, exactly according to RM03.
+            
+        decorator : Python function (default: None)
+            decorator is a function that "decorates" the morphology with ion channels according
+            to a set of rules.
+            If None, a default set of channels is inserted into the first soma section, and the
+            rest of the structure is "bare".
+        
+        nach : string (default: None)
+            nach selects the type of sodium channel that will be used in the model. A channel mechanism
+            by that name must exist. The default channel is set to 'nacn' (R&M03)
+        
+        temperature : float (default: 22)
+            temperature to run the cell at. 
+                 
+        ttx : Boolean (default: False)
+            If ttx is True, then the sodium channel conductance is set to 0 everywhere in the cell.
+            This flag duplicates the effects of tetrodotoxin in the model. Currently, the flag is not implemented.
+        
+        species: string (default 'guineapig')
+            species defines the pattern of ion channel densities that will be inserted, according to 
+            prior measurements in various species. Note that
+            if a decorator function is specified, this argument is ignored as the decorator will
+            specify the channel density.
+
+        modelName: string (default: None)
+            modelName specifies the source conductance pattern (RM03, XM13, etc).
+            modelName is passed to the decorator, or to species_scaling to adjust point (single cylinder) models.
+                             
+        modelType: string (default: None)
+            modelType specifies the subtype of the cell model that will be used (e.g., "II", "II-I", etc).
+            modelType is passed to the decorator, or to species_scaling to adjust point (single cylinder) models.
+            
+        debug: boolean (default: False)
+            When True, there will be multiple printouts of progress and parameters.
+            
+        Returns
+        -------
+            Nothing
+                 
+        """
+        super(BushyRothman, self).__init__()
+        self.i_test_range = {
+            "pulse": (-1, 1, 0.05)
+        }  # note that this might get reset with decorator according to channels
+        # Changing the default values will cause the unit tests to fail!
+        if modelType == None:
+            modelType = "II"
+        if species == "guineapig":
+            modelName = "RM03"
+            temp = 22.0
+            if nach == None:
+                nach = "na"
+        if species == "mouse":
+            temp = 34.0
+            if modelName is None:
+                modelName = "XM13"
+            if nach is None:
+                nach = "na"
+        self.debug = debug
+        self.status = {
+            "species": species,
+            "cellClass": self.type,
+            "modelType": modelType,
+            "modelName": modelName,
+            "soma": True,
+            "axon": False,
+            "dendrites": False,
+            "pumps": False,
+            "hillock": False,
+            "initialsegment": False,
+            "myelinatedaxon": False,
+            "unmyelinatedaxon": False,
+            "na": nach,
+            "ttx": ttx,
+            "name": self.type,
+            "morphology": morphology,
+            "decorator": decorator,
+            "temperature": temperature,
+        }
+
+        self.spike_threshold = -40
+        self.vrange = [-70.0, -55.0]  # set a default vrange for searching for rmp
+        if self.debug:
+            print(
+                "model type, model name, species: ", modelType, modelName, species, nach
+            )
+
+        self.c_m = 0.9e-6  # default in units of F/cm^2
+
+        self._valid_temperatures = (temp,)
+        if self.status["temperature"] == None:
+            self.status["temperature"] = temp
+
+        if morphology is None:
+            """
+            instantiate a basic soma-only ("point") model
+            """
+            if self.debug:
+                print("<< Bushy model: Creating point cell >>")
+            soma = h.Section(
+                name="Bushy_Soma_%x" % id(self)
+            )  # one compartment of about 29000 um2
+            soma.nseg = 1
+            self.add_section(soma, "soma")
+        else:
+            """
+            instantiate a structured model with the morphology as specified by 
+            the morphology file
+            """
+            if self.debug:
+                print(
+                    "<< Bushy model: Creating cell with morphology from %s >>"
+                    % morphology
+                )
+            self.set_morphology(morphology_file=morphology)
+
+        # decorate the morphology with ion channels
+        if decorator is None:  # basic model, only on the soma, does not use tables.
+            self.mechanisms = ["klt", "kht", "ihvcn", "leak", nach]
+            for mech in self.mechanisms:
+                self.soma.insert(mech)
+            self.soma.ena = self.e_na
+            self.soma.ek = self.e_k
+            self.soma().ihvcn.eh = self.e_h
+            self.soma().leak.erev = self.e_leak
+            self.c_m = 0.9
+            self.species_scaling(
+                silent=True, species=species, modelType=modelType
+            )  # set the default type II cell parameters
+        else:  # decorate according to a defined set of rules on all cell compartments, with tables.
+            self.decorate()
+        self.save_all_mechs()  # save all mechanisms inserted, location and gbar values...
+        self.get_mechs(self.soma)
+        if debug:
+            print("   << Created cell >>")
+
+    def get_cellpars(self, dataset, species="guineapig", modelType="II"):
+        """
+        Read data for ion channels and cell parameters from the tables
+        """
+        # cell_type = self.map_celltype(cell_type)
+        # print('getcellpars: dataset, species, mmodeltype: ', dataset, species, modelType)
+        # print('model name: ', self.status['modelName'])
+        cellcap = data.get(
+            dataset, species=species, model_type=modelType, field="soma_Cap"
+        )
+        chtype = data.get(
+            dataset, species=species, model_type=modelType, field="na_type"
+        )
+        pars = Params(cap=cellcap, natype=chtype)
+        # print('pars cell/chtype: ')
+        if self.debug:
+            pars.show()
+        if self.status["modelName"] == "RM03":
+            for g in [
+                "%s_gbar" % pars.natype,
+                "kht_gbar",
+                "klt_gbar",
+                "ih_gbar",
+                "leak_gbar",
+            ]:
+                pars.additem(
+                    g, data.get(dataset, species=species, model_type=modelType, field=g)
+                )
+        if self.status["modelName"] == "XM13":
+            for g in [
+                "%s_gbar" % pars.natype,
+                "kht_gbar",
+                "klt_gbar",
+                "ihvcn_gbar",
+                "leak_gbar",
+            ]:
+                pars.additem(
+                    g, data.get(dataset, species=species, model_type=modelType, field=g)
+                )
+        if self.status["modelName"] == "mGBC":
+            for g in [
+                "%s_gbar" % pars.natype,
+                "kht_gbar",
+                "klt_gbar",
+                "ihvcn_gbar",
+                "leak_gbar",
+            ]:
+                pars.additem(
+                    g, data.get(dataset, species=species, model_type=modelType, field=g)
+                )
+
+        return pars
+
+    def species_scaling(self, species="guineapig", modelType="II", silent=True):
+        """
+        This is called for POINT CELLS ONLY
+        Adjust all of the conductances and the cell size according to the species requested.
+        This scaling should be used ONLY for point models, as no other compartments
+        are scaled.
+        
+        This scaling routine also sets the temperature for the model to a default value. Some models
+        can be run at multiple temperatures, and so a default from one of the temperatures is used.
+        The calling cell.set_temperature(newtemp) will change the conductances and reinitialize
+        the cell to the new temperature settings.
+        
+        Parameters
+        ----------
+        species : string (default: 'guineapig')
+            name of the species to use for scaling the conductances in the base point model
+            Must be one of mouse, cat, guineapig
+        
+        modelType: string (default: 'II')
+            definition of model type from RM03 models, type II or type II-I
+        
+        silent : boolean (default: True)
+            run silently (True) or verbosely (False)
+        
+        """
+        # print '\nSpecies scaling: %s   %s' % (species, type)
+        knownspecies = ["mouse", "guineapig", "cat"]
+
+        soma = self.soma
+        #       cellType = self.map_celltype(modelType)
+
+        if species == "mouse":
+            # use conductance levels determined from Cao et al.,  J. Neurophys., 2007. as
+            # model description in Xie and Manis 2013. Note that
+            # conductances were not scaled for temperature (rates were)
+            # so here we reset the default Q10's for conductance (g) to 1.0
+            if modelType not in ["II", "II-I"]:
+                raise ValueError(
+                    "\nModel type %s is not implemented for mouse bushy cells"
+                    % modelType
+                )
+            if self.debug:
+                print(
+                    "  Setting conductances for mouse bushy cell (%s), Xie and Manis, 2013"
+                    % modelType
+                )
+            if modelname == "XM13":
+                dataset = "XM13_channels"
+            elif modelname == "XM13nacncoop":
+                dataset = "XM13_channels_nacncoop"
+            elif modelname.startswith("mGBC"):
+                dataset = "mGBC_channels"
+            else:
+                raise ValueError(
+                    f"ModelName {modelname:s} not recognized for mouse bushy cells"
+                )
+            self.vrange = [-68.0, -55.0]  # set a default vrange for searching for rmp
+            self.i_test_range = {"pulse": (-1.0, 1.0, 0.05)}
+            self._valid_temperatures = (34.0,)
+            if self.status["temperature"] is None:
+                self.status["temperature"] = 34.0
+
+            pars = self.get_cellpars(dataset, species=species, modelType=modelType)
+            self.set_soma_size_from_Cm(pars.cap)
+            self.status["na"] = pars.natype
+            self.adjust_na_chans(soma, sf=1.0)
+            soma().kht.gbar = nstomho(pars.kht_gbar, self.somaarea)
+            soma().klt.gbar = nstomho(pars.klt_gbar, self.somaarea)
+            soma().ihvcn.gbar = nstomho(pars.ihvcn_gbar, self.somaarea)
+            soma().leak.gbar = nstomho(pars.leak_gbar, self.somaarea)
+            self.axonsf = 0.57
+
+        elif species == "guineapig":
+            if self.debug:
+                print(
+                    "  Setting conductances for guinea pig %s bushy cell, Rothman and Manis, 2003"
+                    % modelType
+                )
+            self._valid_temperatures = (22.0, 38.0)
+            if self.status["temperature"] is None:
+                self.status["temperature"] = 22.0
+            self.i_test_range = {"pulse": (-0.4, 0.4, 0.02)}
+            sf = 1.0
+            if (
+                self.status["temperature"] == 38.0
+            ):  # adjust for 2003 model conductance levels at 38
+                sf = 2  # Q10 of 2, 22->38C. (p3106, R&M2003c)
+                # note that kinetics are scaled in the mod file.
+            dataset = "RM03_channels"
+            pars = self.get_cellpars(dataset, species=species, modelType=modelType)
+            self.set_soma_size_from_Cm(pars.cap)
+            self.status["na"] = pars.natype
+            self.adjust_na_chans(soma, sf=sf)
+            soma().kht.gbar = nstomho(pars.kht_gbar, self.somaarea)
+            soma().klt.gbar = nstomho(pars.klt_gbar, self.somaarea)
+            soma().ihvcn.gbar = nstomho(pars.ih_gbar, self.somaarea)
+            soma().leak.gbar = nstomho(pars.leak_gbar, self.somaarea)
+
+            self.axonsf = 0.57
+
+        else:
+            errmsg = (
+                'Species "%s" or model type "%s" is not recognized for Bushy cells.'
+                % (species, modelType)
+            )
+            errmsg += "\n  Valid species are: \n"
+            for s in knownspecies:
+                errmsg += "    %s\n" % s
+            errmsg += "-" * 40
+            raise ValueError(errmsg)
+
+        self.status["species"] = species
+        self.status["modelType"] = modelType
+        self.check_temperature()
+        #        self.cell_initialize(vrange=self.vrange)  # no need to do this just yet.
+        if not silent:
+            print(" set cell as: ", species)
+            print(" with Vm rest = %6.3f" % self.vm0)
+
+    #     def channel_manager(self, modelType='RM03', cell_type='bushy-II'):
+    #         """
+    #         This routine defines channel density maps and distance map patterns
+    #         for each type of compartment in the cell. The maps
+    #         are used by the ChannelDecorator class (specifically, its private
+    #         \_biophys function) to decorate the cell membrane.
+    #         These settings are only used if the decorator is called; otherwise
+    #         for point cells, the species_scaling routine defines the channel
+    #         densities.
+    #
+    #         Parameters
+    #         ----------
+    #         modelType : string (default: 'RM03')
+    #             A string that defines the type of the model. Currently, 3 types are implemented:
+    #             RM03: Rothman and Manis, 2003 somatic densities for guinea pig
+    #             XM13: Xie and Manis, 2013, somatic densities for mouse
+    #             mGBC: experimental mouse globular bushy cell with dendrites, axon, hillock and initial segment, for
+    #             use with fully reconstructed neurons.
+    #
+    #         Returns
+    #         -------
+    #         Nothing
+    #
+    #         Notes
+    #         -----
+    #         This routine defines the following variables for the class:
+    #
+    #             * conductances (gBar)
+    #             * a channelMap (dictonary of channel densities in defined anatomical compartments)
+    #             * a current injection range for IV's (used for testing)
+    #             * a distance map, which defines how each conductance in a selected compartment
+    #               changes with distance from the soma. The current implementation includes both
+    #               linear and exponential gradients,
+    #               the minimum conductance at the end of the gradient, and the space constant or
+    #               slope for the gradient.
+    #
+    #         """
+    #
+    #
+    #         dataset = '%s_channels' % modelType
+    #         decorationmap = dataset + '_compartments'
+    #         # print('dataset: {0:s}   decorationmap: {1:s}'.format(dataset, decorationmap))
+    #         cellpars = self.get_cellpars(dataset, species=self.status['species'], celltype=cell_type)
+    #         refarea = 1e-3*cellpars.cap / self.c_m
+    #
+    #         table = data.get_table_info(dataset)
+    #         chscale = data.get_table_info(decorationmap)
+    #         pars = {}
+    #         # retrive the conductances from the data set
+    #         for g in table['field']:
+    #             x = data.get(dataset, species=self.status['species'], cell_type=cell_type,
+    #                                 field=g)
+    #             if not isinstance(x, float):
+    #                 continue
+    #             if '_gbar' in g:
+    #                 pars[g] = x/refarea
+    #             else:
+    #                 pars[g] = x
+    #
+    #         self.channelMap = OrderedDict()
+    #         for c in chscale['compartment']:
+    #             self.channelMap[c] = {}
+    #             for g in pars.keys():
+    #                 if g not in chscale['parameter']:
+    # #                    print ('Parameter %s not found in chscale parameters!' % g)
+    #                     continue
+    #                 scale = data.get(decorationmap, species=self.status['species'], cell_type=cell_type,
+    #                         compartment=c, parameter=g)
+    #                 if '_gbar' in g:
+    #                     self.channelMap[c][g] = pars[g]*scale
+    #                 else:
+    #                     self.channelMap[c][g] = pars[g]
+    #
+    # self.irange = np.linspace(-0.6, 1, 9)
+
+    def get_distancemap(self):
+        return {
+            "dend": {
+                "klt": {"gradient": "exp", "gminf": 0.0, "lambda": 50.0},
+                "kht": {"gradient": "exp", "gminf": 0.0, "lambda": 50.0},
+                "nav11": {"gradient": "exp", "gminf": 0.0, "lambda": 50.0},
+            },  # linear with distance, gminf (factor) is multiplied by gbar
+            "dendrite": {
+                "klt": {"gradient": "linear", "gminf": 0.0, "lambda": 100.0},
+                "kht": {"gradient": "linear", "gminf": 0.0, "lambda": 100.0},
+                "nav11": {"gradient": "linear", "gminf": 0.0, "lambda": 100.0},
+            },  # linear with distance, gminf (factor) is multiplied by gbar
+            "apic": {
+                "klt": {"gradient": "linear", "gminf": 0.0, "lambda": 100.0},
+                "kht": {"gradient": "linear", "gminf": 0.0, "lambda": 100.0},
+                "nav11": {"gradient": "exp", "gminf": 0.0, "lambda": 200.0},
+            },  # gradients are: flat, linear, exponential
+        }
+        # self.check_temperature()
+        # return
+        #
+
+    #
+    #
+    #         if modelType == 'RM03':
+    #             #
+    #             # Create a model based on the Rothman and Manis 2003 conductance set from guinea pig
+    #             #
+    #             self.c_m = 0.9E-6  # default in units of F/cm^2
+    #             self._valid_temperatures = (22., 38.)
+    #             sf = 1.0
+    #             if self.status['temperature'] == None:
+    #                 self.status['temperature'] = 22.
+    #             if self.status['temperature'] == 38:
+    #                 sf = 3.03
+    #             dataset = 'RM03_channels'
+    #             pars = self.get_cellpars(dataset, species=self.status['species'], celltype='bushy-II')
+    #             refarea = 1e-3*pars.cap / self.c_m
+    #             self.gBar = Params(nabar=sf*pars.soma_na_gbar/refarea, # 1000.0E-9/refarea,
+    #                                khtbar=sf*pars.soma_kht_gbar/refarea,
+    #                                kltbar=sf*pars.soma_klt_gbar/refarea,
+    #                                ihbar=sf*pars.soma_ih_gbar/refarea,
+    #                                leakbar=sf*pars.soma_leak_gbar/refarea,
+    #                               )
+    #             print 'RM03 gbar:\n', self.gBar.show()
+    #
+    #             self.channelMap = {
+    #                 'axon': {'nacn': self.gBar.nabar, 'klt': self.gBar.kltbar, 'kht': self.gBar.khtbar, 'ihvcn': 0.,
+    #                          'leak': self.gBar.leakbar / 2.},
+    #                 'hillock': {'nacn': self.gBar.nabar, 'klt': self.gBar.kltbar, 'kht': self.gBar.khtbar, 'ihvcn': 0.,
+    #                             'leak': self.gBar.leakbar, },
+    #                 'initseg': {'nacn': self.gBar.nabar, 'klt': self.gBar.kltbar, 'kht': self.gBar.khtbar,
+    #                             'ihvcn': self.gBar.ihbar / 2., 'leak': self.gBar.leakbar, },
+    #                 'soma': {'nacn': self.gBar.nabar, 'klt': self.gBar.kltbar, 'kht': self.gBar.khtbar,
+    #                          'ihvcn': self.gBar.ihbar, 'leak': self.gBar.leakbar, },
+    #                 'dend': {'nacn': self.gBar.nabar, 'klt': self.gBar.kltbar * 0.5, 'kht': self.gBar.khtbar * 0.5,
+    #                          'ihvcn': self.gBar.ihbar / 3., 'leak': self.gBar.leakbar * 0.5, },
+    #                 'apic': {'nacn': self.gBar.nabar, 'klt': self.gBar.kltbar * 0.2, 'kht': self.gBar.khtbar * 0.2,
+    #                          'ihvcn': self.gBar.ihbar / 4., 'leak': self.gBar.leakbar * 0.2, },
+    #             }
+    # #            self.irange = np.linspace(-1., 1., 21)
+    #             self.distMap = {'dend': {'klt': {'gradient': 'linear', 'gminf': 0., 'lambda': 100.},
+    #                                      'kht': {'gradient': 'linear', 'gminf': 0., 'lambda': 100.},
+    #                                      'nacn': {'gradient': 'exp', 'gminf': 0., 'lambda': 100.}}, # linear with distance, gminf (factor) is multiplied by gbar
+    #                             'apic': {'klt': {'gradient': 'linear', 'gminf': 0., 'lambda': 100.},
+    #                                      'kht': {'gradient': 'linear', 'gminf': 0., 'lambda': 100.},
+    #                                      'nacn': {'gradient': 'exp', 'gminf': 0., 'lambda': 100.}}, # gradients are: flat, linear, exponential
+    #                             }
+    #
+    #         elif modelType == 'XM13':
+    #             #
+    #             # Create a model for a mouse bushy cell from Xie and Manis, 2013
+    #             # based on Cao and Oertel mouse conductance values
+    #             # and Rothman and Manis kinetics.
+    #             self.c_m = 0.9E-6  # default in units of F/cm^2
+    #             self._valid_temperatures = (34., )
+    #             if self.status['temperature'] == None:
+    #                 self.status['temperature'] = 34.
+    #             dataset = 'XM13_channels'
+    #             pars = self.get_cellpars(dataset, species=self.status['species'], celltype='bushy-II')
+    #             refarea = 1e-3*pars.cap / self.c_m
+    #             # self.gBar = Params(nabar=pars.soma_nav11_gbar/refarea, # 1000.0E-9/refarea,
+    #             #                    khtbar=pars.soma_kht_gbar/refarea,
+    #             #                    kltbar=pars.soma_klt_gbar/refarea,
+    #             #                    ihbar=pars.soma_ihvcn_gbar/refarea,
+    #             #                    leakbar=pars.soma_leak_gbar/refarea,
+    #             #                   )
+    #             # print 'XM13 gbar:\n', self.gBar.show()
+    #             # # create channel map:
+    #             decorationmap = 'XM13_channels_bycompartment'
+    #
+    #             table = data.get_table_info(dataset)
+    #             pars = {}
+    #             for g in table['field']:
+    #                 x = data.get(dataset, species=self.status['species'], cell_type='bushy-II',
+    #                                     field=g)
+    #                 if not isinstance(x, float):
+    #                     continue
+    #                 pars[g] = (1./refarea)*data.get(dataset, species=self.status['species'], cell_type='bushy-II',
+    #                     field=g)
+    #             chscale = data.get_table_info(decorationmap)
+    #             self.channelMap1 = OrderedDict()
+    # #            print chscale['parameter']
+    #             for c in chscale['compartment']:
+    #                 self.channelMap1[c] = {}
+    #                 for g in pars.keys():
+    # #                    print g
+    #                     if g[5:] not in chscale['parameter']:
+    #                         continue
+    #                     scale = data.get(decorationmap, species=self.status['species'], cell_type='bushy-II',
+    #                             compartment=c, parameter=g[5:])
+    #                     self.channelMap1[c][g] = pars[g]*scale
+    #
+    #             #
+    #             # self.channelMap = {
+    #             #     'unmyelinatedaxon': {'nav11': self.gBar.nabar*1, 'klt': self.gBar.kltbar * 1.0, 'kht': self.gBar.khtbar, 'ihvcn': 0.,
+    #             #              'leak': self.gBar.leakbar * 0.25},
+    #             #     'hillock': {'nav11': self.gBar.nabar*2, 'klt': self.gBar.kltbar, 'kht': self.gBar.khtbar*2.0, 'ihvcn': 0.,
+    #             #                 'leak': self.gBar.leakbar, },
+    #             #     'initialsegment': {'nav11': self.gBar.nabar*3.0, 'klt': self.gBar.kltbar*1, 'kht': self.gBar.khtbar*2,
+    #             #                 'ihvcn': self.gBar.ihbar * 0.5, 'leak': self.gBar.leakbar, },
+    #             #     'soma': {'nav11': self.gBar.nabar*1.0, 'klt': self.gBar.kltbar, 'kht': self.gBar.khtbar,
+    #             #              'ihvcn': self.gBar.ihbar, 'leak': self.gBar.leakbar, },
+    #             #     'dend': {'nav11': self.gBar.nabar * 0.25, 'klt': self.gBar.kltbar *0.5, 'kht': self.gBar.khtbar *0.5,
+    #             #              'ihvcn': self.gBar.ihbar *0.5, 'leak': self.gBar.leakbar * 0.5, },
+    #             #     'primarydendrite': {'nav11': self.gBar.nabar * 0.25, 'klt': self.gBar.kltbar *0.5, 'kht': self.gBar.khtbar *0.5,
+    #             #              'ihvcn': self.gBar.ihbar *0.5, 'leak': self.gBar.leakbar * 0.5, },
+    #             #     'apic': {'nav11': self.gBar.nabar * 0.25, 'klt': self.gBar.kltbar * 0.25, 'kht': self.gBar.khtbar * 0.25,
+    #             #              'ihvcn': self.gBar.ihbar *0.25, 'leak': self.gBar.leakbar * 0.25, },
+    #             # }
+    #             import pprint
+    # #            print 'original map:\n'
+    # #             for k in self.channelMap.keys():
+    # #                 print('Region: %s' % k)
+    # #                 if k in self.channelMap1.keys():
+    # #                     print 'overlapping Region: %s' % k
+    # #                     for ch in self.channelMap[k].keys():
+    # #                         # print ch
+    # #                        #  print self.channelMap1[k].keys()
+    # #                        #  print self.channelMap[k].keys()
+    # #                         if 'soma_' + ch + '_gbar' in self.channelMap1[k].keys():
+    # #                             cx = u'soma_' + ch + u'_gbar'
+    # # #                            print ch, cx
+    # #                             print( '    {0:>4s} = {1:e}   {2:e}   {3:<5s}'.format(ch, self.channelMap[k][ch], self.channelMap1[k][cx],
+    # #                                     str(np.isclose(self.channelMap[k][ch], self.channelMap1[k][cx]))))
+    #
+    #             # print 'original: ', self.channelMap['soma']
+    #             self.channelMap = self.channelMap1 # use the data table
+    #             # except need to remove soma_ from keys
+    #             for k in self.channelMap.keys():
+    #                 for n in self.channelMap[k].keys():
+    #                     new_key = n.replace('_gbar', '')
+    #                    # new_key = n
+    #                     new_key = new_key.replace('soma_', '')
+    #                   # strip 'soma_' from key
+    #                     #print 'newkey: ', new_key, n
+    #                     self.channelMap[k][new_key] = self.channelMap[k].pop(n)
+    #
+    #             print 'final map: ', self.channelMap['soma']
+    #
+    #             self.irange = np.linspace(-0.6, 1, 9)
+    #             self.distMap = {'dend': {'klt': {'gradient': 'exp', 'gminf': 0., 'lambda': 50.},
+    #                                      'kht': {'gradient': 'exp', 'gminf': 0., 'lambda': 50.},
+    #                                      'nav11': {'gradient': 'exp', 'gminf': 0., 'lambda': 50.}}, # linear with distance, gminf (factor) is multiplied by gbar
+    #                             'dendrite': {'klt': {'gradient': 'linear', 'gminf': 0., 'lambda': 100.},
+    #                                      'kht': {'gradient': 'linear', 'gminf': 0., 'lambda': 100.},
+    #                                      'nav11': {'gradient': 'linear', 'gminf': 0., 'lambda': 100.}}, # linear with distance, gminf (factor) is multiplied by gbar
+    #                             'apic': {'klt': {'gradient': 'linear', 'gminf': 0., 'lambda': 100.},
+    #                                      'kht': {'gradient': 'linear', 'gminf': 0., 'lambda': 100.},
+    #                                      'nav11': {'gradient': 'exp', 'gminf': 0., 'lambda': 200.}}, # gradients are: flat, linear, exponential
+    #                             }
+    #
+    #         elif modelType == 'mGBC':
+    #             # bushy from Xie and Manis, 2013, based on Cao and Oertel mouse conductances,
+    #             # BUT modified ad hoc for SBEM reconstructions.
+    #             dataset = 'mGBC_channels'
+    #
+    #             self._valid_temperatures = (34.,)
+    #             if self.status['temperature'] == None:
+    #                 self.status['temperature'] = 34.
+    #             pars = self.get_cellpars(dataset, species=self.status['species'], celltype='bushy-II')
+    #             refarea = 1e-3*pars.cap / self.c_m
+    #             print (pars.cap, pars.soma_kht_gbar, refarea)  # refarea should be about 30e-6
+    #
+    #             self.gBar = Params(nabar=pars.soma_na_gbar/refarea, # 1000.0E-9/refarea,
+    #                                khtbar=pars.soma_kht_gbar/refarea,
+    #                                kltbar=pars.soma_klt_gbar/refarea,
+    #                                ihbar=pars.soma_ih_gbar/refarea,
+    #                                leakbar=pars.soma_leak_gbar/refarea,
+    #                               )
+    #             print 'mGBC gbar:\n', self.gBar.show()
+    #             sodiumch = 'jsrna'
+    #             self.channelMap = {
+    #                 'axon': {sodiumch: self.gBar.nabar*1., 'klt': self.gBar.kltbar * 1.0, 'kht': self.gBar.khtbar, 'ihvcn': 0.,
+    #                          'leak': self.gBar.leakbar * 0.25},
+    #                 'unmyelinatedaxon': {sodiumch: self.gBar.nabar*3.0, 'klt': self.gBar.kltbar * 2.0,
+    #                          'kht': self.gBar.khtbar*3.0, 'ihvcn': 0.,
+    #                          'leak': self.gBar.leakbar * 0.25},
+    #                 'myelinatedaxon': {sodiumch: self.gBar.nabar*0, 'klt': self.gBar.kltbar * 1e-2,
+    #                          'kht': self.gBar.khtbar*1e-2, 'ihvcn': 0.,
+    #                          'leak': self.gBar.leakbar * 0.25*1e-3},
+    #                 'hillock': {sodiumch: self.gBar.nabar*4.0, 'klt': self.gBar.kltbar*1.0, 'kht': self.gBar.khtbar*3.0,
+    #                              'ihvcn': 0., 'leak': self.gBar.leakbar, },
+    #                 'initseg': {sodiumch: self.gBar.nabar*3.0, 'klt': self.gBar.kltbar*2, 'kht': self.gBar.khtbar*2,
+    #                             'ihvcn': self.gBar.ihbar * 0.5, 'leak': self.gBar.leakbar, },
+    #                 'soma': {sodiumch: self.gBar.nabar*0.65, 'klt': self.gBar.kltbar, 'kht': self.gBar.khtbar*1.5,
+    #                          'ihvcn': self.gBar.ihbar, 'leak': self.gBar.leakbar, },
+    #                 'dend': {sodiumch: self.gBar.nabar * 0.2, 'klt': self.gBar.kltbar *1, 'kht': self.gBar.khtbar *1,
+    #                          'ihvcn': self.gBar.ihbar *0.5, 'leak': self.gBar.leakbar * 0.5, },
+    #                 'dendrite': {sodiumch: self.gBar.nabar * 0.2, 'klt': self.gBar.kltbar *1, 'kht': self.gBar.khtbar *1,
+    #                          'ihvcn': self.gBar.ihbar *0.5, 'leak': self.gBar.leakbar * 0.5, },
+    #                 'apic': {sodiumch: self.gBar.nabar * 0.25, 'klt': self.gBar.kltbar * 0.25, 'kht': self.gBar.khtbar * 0.25,
+    #                          'ihvcn': self.gBar.ihbar *0.25, 'leak': self.gBar.leakbar * 0.25, },
+    #             }
+    #             self.irange = np.arange(-1.5, 2.1, 0.25 )
+    #             self.distMap = {'dend': {'klt': {'gradient': 'linear', 'gminf': 0., 'lambda': 100.},
+    #                                      'kht': {'gradient': 'linear', 'gminf': 0., 'lambda': 100.},
+    #                                      sodiumch: {'gradient': 'linear', 'gminf': 0., 'lambda': 100.}}, # linear with distance, gminf (factor) is multiplied by gbar
+    #                             'dendrite': {'klt': {'gradient': 'linear', 'gminf': 0., 'lambda': 20.},
+    #                                       'kht': {'gradient': 'linear', 'gminf': 0., 'lambda': 20.},
+    #                                       sodiumch: {'gradient': 'linear', 'gminf': 0., 'lambda': 20.}}, # linear with distance, gminf (factor) is multiplied by gbar
+    #                             'apic': {'klt': {'gradient': 'linear', 'gminf': 0., 'lambda': 100.},
+    #                                      'kht': {'gradient': 'linear', 'gminf': 0., 'lambda': 100.},
+    #                                      sodiumch: {'gradient': 'exp', 'gminf': 0., 'lambda': 200.}}, # gradients are: flat, linear, exponential
+    #                             }
+    #         else:
+    #             raise ValueError('model type %s is not implemented' % modelType)
+    #         self.check_temperature()
+
+    def adjust_na_chans(self, soma, sf=1.0, gbar=1000.0):
+        """
+        adjust the sodium channel conductance
+        
+        Parameters
+        ----------
+        soma : neuron section object
+            A soma object whose sodium channel complement will have its 
+            conductances adjusted depending on the channel type
+        
+        gbar : float (default: 1000.)
+            The maximal conductance for the sodium channel
+        
+        Returns
+        -------
+            Nothing :
+        
+        """
+
+        if self.status["ttx"]:
+            gnabar = 0.0
+        else:
+            gnabar = nstomho(gbar, self.somaarea) * sf
+        nach = self.status["na"]
+        if nach == "jsrna":
+            soma().jsrna.gbar = gnabar
+            soma.ena = self.e_na
+            if self.debug:
+                print("jsrna gbar: ", soma().jsrna.gbar)
+        elif nach == "nav11":
+            soma().nav11.gbar = gnabar
+            soma.ena = 50  # self.e_na
+            #            print('gnabar: ', soma().nav11.gbar, ' vs: 0.0192307692308')
+            soma().nav11.vsna = 4.3
+            if self.debug:
+                print("bushy using inva11")
+        if nach == "nacncoop":
+            soma().nacncoop.gbar = gnabar
+            soma().nacncoop.KJ = 2000.0
+            soma().nacncoop.p = 0.25
+            somae().nacncoop.vsna = 0.0
+            soma.ena = self.e_na
+            if debug:
+                print("nacncoop gbar: ", soma().nacncoop.gbar)
+        elif nach in ["na", "nacn"]:
+            soma().na.gbar = gnabar
+            soma.ena = self.e_na
+            # soma().na.vsna = 0.
+            if self.debug:
+                print("na gbar: ", soma().na.gbar)
+        else:
+            raise ValueError(
+                "Sodium channel %s is not recognized for Bushy cells", nach
+            )
+
+    def add_axon(self):
+        """
+        Add a default axon from the generic cell class to the bushy cell (see cell class).
+        """
+        Cell.add_axon(self, self.c_m, self.R_a, self.axonsf)
+
+    def add_pumps(self):
+        """
+        Insert mechanisms for potassium ion management, sodium ion management, and a
+        sodium-potassium pump at the soma.
+        """
+        soma = self.soma
+        soma.insert("k_conc")
+
+        ki0_k_ion = 140
+        soma().ki = ki0_k_ion
+        soma().ki0_k_conc = ki0_k_ion
+        soma().beta_k_conc = 0.075
+
+        soma.insert("na_conc")
+        nai0_na_ion = 5
+        soma().nai = nai0_na_ion
+        soma().nai0_na_conc = nai0_na_ion
+        soma().beta_na_conc = 0.075
+
+        soma.insert("nakpump")
+        soma().nakpump.inakmax = 8
+        soma().nao = 145
+        soma().ko = 5
+        soma().nakpump.Nai_inf = 5
+        soma().nakpump.Ki_inf = 140
+        soma().nakpump.ATPi = 5
+        self.status["pumps"] = True
+
+    def add_dendrites(self):
+        """
+        Add a simple dendrite to the bushy cell.
+        """
+        if self.debug:
+            print("Adding dendrite to Bushy model")
+        section = h.Section
+        primarydendrite = section(cell=self.soma)
+        primarydendrite.connect(self.soma)
+        primarydendrite.nseg = 10
+        primarydendrite.L = 100.0
+        primarydendrite.diam = 2.5
+        primarydendrite.insert("klt")
+        primarydendrite.insert("ihvcn")
+        primarydendrite().klt.gbar = self.soma().klt.gbar / 2.0
+        primarydendrite().ihvcn.gbar = self.soma().ihvcn.gbar / 2.0
+
+        primarydendrite.cm = self.c_m
+        primarydendrite.Ra = self.R_a
+        nsecd = range(0, 5)
+        secondarydendrite = []
+        for ibd in nsecd:
+            secondarydendrite.append(section(cell=self.soma))
+        for ibd in nsecd:
+            secondarydendrite[ibd].connect(primarydendrite)
+            secondarydendrite[ibd].diam = 1.0
+            secondarydendrite[ibd].L = 15.0
+            secondarydendrite[ibd].cm = self.c_m
+            secondarydendrite[ibd].Ra = self.R_a
+        self.primarydendrite = primarydendrite
+        self.secondarydendrite = secondarydendrite
+        self.status["dendrite"] = True
+        if self.debug:
+            print("Bushy: added dendrites")
+            h.topology()
+        self.add_section(maindend, "primarydendrite")
+        self.add_section(secdend, "secondarydendrite")

cnmodel/cells/cartwheel.py

@@ -0,0 +1,447 @@
+from __future__ import print_function
+
+import numpy as np
+from neuron import h
+
+from .cell import Cell
+from .. import data
+from .. import synapses
+from ..util import Params
+from ..util import nstomho
+
+__all__ = ["Cartwheel", "CartwheelDefault"]
+
+
+class Cartwheel(Cell):
+
+    type = "cartwheel"
+
+    @classmethod
+    def create(cls, model="CW", **kwds):
+        if model == "CW":
+            return CartwheelDefault(**kwds)
+        else:
+            raise ValueError("Carthweel model is unknown", model)
+
+    def make_psd(self, terminal, psd_type, **kwds):
+        """
+        Connect a presynaptic terminal to one post section at the specified location, with the fraction
+        of the "standard" conductance determined by gbar.
+        The default condition is to try to pass the default unit test (loc=0.5)
+        
+        Parameters
+        ----------
+        terminal : Presynaptic terminal (NEURON object)
+        
+        psd_type : either simple or multisite PSD for bushy cell
+        
+        kwds: dict of options. Two are currently handled:
+        postsize : expect a list consisting of [sectionno, location (float)]
+        AMPAScale : float to scale the ampa currents
+        
+        """
+        self.pre_sec = terminal.section
+        pre_cell = terminal.cell
+        post_sec = self.soma
+
+        if psd_type == "simple":
+            if terminal.cell.type in [
+                "sgc",
+                "dstellate",
+                "tuberculoventral",
+                "cartwheel",
+            ]:
+                weight = data.get(
+                    "%s_synapse" % terminal.cell.type,
+                    species=self.species,
+                    post_type=self.type,
+                    field="weight",
+                )
+                tau1 = data.get(
+                    "%s_synapse" % terminal.cell.type,
+                    species=self.species,
+                    post_type=self.type,
+                    field="tau1",
+                )
+                tau2 = data.get(
+                    "%s_synapse" % terminal.cell.type,
+                    species=self.species,
+                    post_type=self.type,
+                    field="tau2",
+                )
+                erev = data.get(
+                    "%s_synapse" % terminal.cell.type,
+                    species=self.species,
+                    post_type=self.type,
+                    field="erev",
+                )
+                return self.make_exp2_psd(
+                    post_sec,
+                    terminal,
+                    weight=weight,
+                    loc=loc,
+                    tau1=tau1,
+                    tau2=tau2,
+                    erev=erev,
+                )
+            else:
+                raise TypeError(
+                    "Cannot make simple PSD for %s => %s"
+                    % (terminal.cell.type, self.type)
+                )
+
+        else:
+            raise ValueError(
+                "Unsupported psd type %s for cartwheel cell (inputs not implemented yet)"
+                % psd_type
+            )
+
+    def make_terminal(self, post_cell, term_type, **kwds):
+        if term_type == "simple":
+            return synapses.SimpleTerminal(self.soma, post_cell, **kwds)
+        elif term_type == "multisite":
+            if post_cell.type in ["tuberculoventral", "pyramidal"]:
+                nzones = data.get(
+                    "cartwheel_synapse",
+                    species=self.species,
+                    post_type=post_cell.type,
+                    field="n_rsites",
+                )
+                delay = data.get(
+                    "cartwheel_synapse",
+                    species=self.species,
+                    post_type=post_cell.type,
+                    field="delay",
+                )
+            else:
+                raise NotImplementedError(
+                    "No knowledge as to how to connect cartwheel cell to cell type %s"
+                    % type(post_cell)
+                )
+            pre_sec = self.soma
+            return synapses.StochasticTerminal(
+                pre_sec,
+                post_cell,
+                nzones=nzones,
+                spike_source=self.spike_source,
+                delay=delay,
+                **kwds
+            )
+        else:
+            raise ValueError("Unsupported terminal type %s" % term_type)
+
+
+class CartwheelDefault(Cartwheel, Cell):
+    """
+    DCN cartwheel cell model.
+    
+    """
+
+    def __init__(
+        self,
+        morphology=None,
+        decorator=None,
+        ttx=False,
+        nach=None,
+        species="mouse",
+        modelType=None,
+        debug=False,
+    ):
+        """        
+        Create cartwheel cell model, based on a Purkinje cell model from Raman.
+        There are no variations available for this model.
+        
+        Parameters
+        ----------
+        morphology : string (default: None)
+            Name of a .hoc file representing the morphology. This file is used to constructe
+            an electrotonic (cable) model. 
+            If None (default), then a "point" (really, single cylinder) model is made, exactly according to RM03.
+            
+        decorator : Python function (default: None)
+            decorator is a function that "decorates" the morphology with ion channels according
+            to a set of rules.
+            If None, a default set of channels is inserted into the first soma section, and the
+            rest of the structure is "bare".
+        
+        nach : string (default: None)
+            nach selects the type of sodium channel that will be used in the model. A channel mechanism
+            by that name must exist. The default is naRsg, a resurgent sodium channel model.
+        
+        ttx : Boolean (default: False)
+            If ttx is True, then the sodium channel conductance is set to 0 everywhere in the cell.
+            This flag duplicates the effects of tetrodotoxin in the model. Currently, the flag is not implemented.
+        
+        species: string (default 'rat')
+            species defines the pattern of ion channel densities that will be inserted, according to 
+            prior measurements in various species. Note that
+            if a decorator function is specified, this argument is ignored as the decorator will
+            specify the channel density.
+            
+        modelType: string (default: None)
+            modelType specifies the subtype of the cell model that will be used.
+            modelType is passed to the decorator, or to species_scaling to adjust point (single cylinder) models.
+            Only type "I" is recognized for the cartwheel cell model.
+            
+        debug: boolean (default: False)
+            When True, there will be multiple printouts of progress and parameters.
+            
+        Returns
+        -------
+            Nothing
+        """
+        super(CartwheelDefault, self).__init__()
+        if modelType == None:
+            modelType = "I"
+        if nach == None:
+            nach = "naRsg"
+        self.status = {
+            "soma": True,
+            "axon": False,
+            "dendrites": False,
+            "pumps": False,
+            "na": nach,
+            "species": species,
+            "modelType": modelType,
+            "ttx": ttx,
+            "name": "Cartwheel",
+            "morphology": morphology,
+            "decorator": decorator,
+            "temperature": None,
+        }
+
+        self.i_test_range = {"pulse": (-0.2, 0.2, 0.02)}
+        # self.spike_threshold = 0
+        self.vrange = [-75.0, -52.0]  # set a default vrange for searching for rmp
+
+        if morphology is None:
+            """
+            instantiate a basic soma-only ("point") model
+            """
+            soma = h.Section(
+                name="Cartwheel_Soma_%x" % id(self)
+            )  # one compartment of about 29000 um2
+            #            cm = 1
+            soma.nseg = 1
+            self.add_section(soma, "soma")
+        else:
+            """
+            instantiate a structured model with the morphology as specified by 
+            the morphology file
+            """
+            self.set_morphology(morphology_file=morphology)
+
+        # decorate the morphology with ion channels
+        if decorator is None:  # basic model, only on the soma
+            # v_potassium = -80       # potassium reversal potential
+            # v_sodium = 50           # sodium reversal potential
+
+            self.mechanisms = [
+                "naRsg",
+                "bkpkj",
+                "hpkj",
+                "kpkj",
+                "kpkj2",
+                "kpkjslow",
+                "kpksk",
+                "lkpkj",
+                "cap",
+            ]
+            for mech in self.mechanisms:
+                self.soma.insert(mech)
+            self.soma.insert("cadiff")
+            self.species_scaling(
+                silent=True, species=species, modelType=modelType
+            )  # set the default type II cell parameters
+        else:  # decorate according to a defined set of rules on all cell compartments
+            self.decorate()
+        self.save_all_mechs()  # save all mechanisms inserted, location and gbar values...
+        self.get_mechs(self.soma)
+
+        if debug:
+            print(
+                "<< Cartwheel: Modified version of Raman Purkinje cell model created >>"
+            )
+
+    def get_cellpars(self, dataset, species="guineapig", celltype="II"):
+        somaDia = data.get(
+            dataset, species=species, cell_type=celltype, field="soma_Dia"
+        )
+        chtype = data.get(
+            dataset, species=species, cell_type=celltype, field="soma_na_type"
+        )
+        pcabar = data.get(
+            dataset, species=species, cell_type=celltype, field="soma_pcabar"
+        )
+        pars = Params(soma_Dia=somaDia, soma_natype=chtype, soma_pcabar=pcabar)
+        for g in [
+            "soma_narsg_gbar",
+            "soma_kpkj_gbar",
+            "soma_kpkj2_gbar",
+            "soma_kpkjslow_gbar",
+            "soma_kpksk_gbar",
+            "soma_lkpkj_gbar",
+            "soma_bkpkj_gbar",
+            "soma_hpkj_gbar",
+            "soma_hpkj_eh",
+            "soma_lkpkj_e",
+            "soma_e_k",
+            "soma_e_na",
+            "soma_e_ca",
+        ]:
+            pars.additem(
+                g, data.get(dataset, species=species, cell_type=celltype, field=g)
+            )
+        return pars
+
+    def species_scaling(self, silent=True, species="mouse", modelType="I"):
+        """
+        Adjust all of the conductances and the cell size according to the species requested.
+        This scaling should be used ONLY for point models, as no other compartments
+        are scaled.
+        
+        Parameters
+        ----------
+        species : string (default: 'rat')
+            name of the species to use for scaling the conductances in the base point model
+            Must be one of mouse, cat, guineapig
+        
+        modelType: string (default: 'I')
+            definition of model type from RM03 models, type II or type II-I
+        
+        silent : boolean (default: True)
+            run silently (True) or verbosely (False)
+        
+        Note
+        ----
+            For the cartwheel cell model, there is only a single scaling recognized. 
+        """
+        if species is not "mouse":
+            raise ValueError('Cartwheel species: only "mouse" is recognized')
+        if modelType is not "I":
+            raise ValueError('Cartwheel modelType: only "I" is recognized')
+        self._valid_temperatures = (34.0,)
+        if self.status["temperature"] is None:
+            self.set_temperature(34.0)
+
+        pars = self.get_cellpars("CW_channels", species=species, celltype="cartwheel")
+        self.set_soma_size_from_Diam(pars.soma_Dia)
+        self.soma().bkpkj.gbar = nstomho(pars.soma_bkpkj_gbar, self.somaarea)
+        self.soma().hpkj.gbar = nstomho(pars.soma_hpkj_gbar, self.somaarea)
+        self.soma().kpkj.gbar = nstomho(pars.soma_kpkj_gbar, self.somaarea)
+        self.soma().kpkj2.gbar = nstomho(pars.soma_kpkj2_gbar, self.somaarea)
+        self.soma().kpkjslow.gbar = nstomho(pars.soma_kpkjslow_gbar, self.somaarea)
+        self.soma().kpksk.gbar = nstomho(pars.soma_kpksk_gbar, self.somaarea)
+        self.soma().lkpkj.gbar = nstomho(pars.soma_lkpkj_gbar, self.somaarea)
+        self.soma().naRsg.gbar = nstomho(pars.soma_narsg_gbar, self.somaarea)
+        self.soma().cap.pcabar = pars.soma_pcabar
+        self.soma().ena = pars.soma_e_na  # 50
+        self.soma().ek = pars.soma_e_k  # -80
+        self.soma().lkpkj.e = pars.soma_lkpkj_e  # -65
+        self.soma().hpkj.eh = pars.soma_hpkj_eh  # -43
+        self.soma().eca = pars.soma_e_ca  # 50
+
+        self.status["na"] = pars.soma_natype
+        self.status["species"] = species
+        self.status["modelType"] = modelType
+        self.check_temperature()
+        if not silent:
+            print("set cell as: ", species)
+            print(" with Vm rest = %f" % self.vm0)
+
+    # print 'set up'
+
+    def i_currents(self, V):
+        """
+        For the steady-state case, return the total current at voltage V
+        Used to find the zero current point.
+        Overrides i_currents in cells.py, because this model uses conductances
+        that are not specified in the default cell mode.
+        
+        Parameters
+        ----------
+        V : float, mV (no default)
+            Voltage at which the current for each conductance is computed.
+        
+        Returns
+        -------
+        I : float, nA
+             The sum of the currents at steady-state for all of the conductances.
+        """
+        for part in self.all_sections.keys():
+            for sec in self.all_sections[part]:
+                sec.v = V
+        h.celsius = self.status["temperature"]
+        h.finitialize()
+        self.ix = {}
+
+        if "naRsg" in self.mechanisms:
+            self.ix["naRsg"] = self.soma().naRsg.gna * (V - self.soma().ena)
+        if "cap" in self.mechanisms:
+            a = self.soma().cap.pcabar * self.soma().cap.minf
+            self.ix["cap"] = a * self.ghk(V, self.soma().cao, self.soma().cai, 2)
+        if "kpkj" in self.mechanisms:
+            self.ix["kpkj"] = self.soma().kpkj.gk * (V - self.soma().ek)
+        if "kpkj2" in self.mechanisms:
+            self.ix["kpkj2"] = self.soma().kpkj2.gk * (V - self.soma().ek)
+        if "kpkjslow" in self.mechanisms:
+            self.ix["kpkjslow"] = self.soma().kpkjslow.gk * (V - self.soma().ek)
+        if "kpksk" in self.mechanisms:
+            self.ix["kpksk"] = self.soma().kpksk.gk * (V - self.soma().ek)
+        if "bkpkj" in self.mechanisms:
+            self.ix["bkpkj"] = self.soma().bkpkj.gbkpkj * (V - self.soma().ek)
+        if "hpkj" in self.mechanisms:
+            self.ix["hpkj"] = self.soma().hpkj.gh * (V - self.soma().hpkj.eh)
+        # leak
+        if "lkpkj" in self.mechanisms:
+            self.ix["lkpkj"] = self.soma().lkpkj.gbar * (V - self.soma().lkpkj.e)
+        return np.sum([self.ix[i] for i in self.ix])
+
+    def ghk(self, v, ci, co, z):
+        """
+        GHK flux equation, used to calculate current density through calcium channels
+        rather than standard Nernst equation.
+        
+        Parameters
+        ----------
+        v : float, mV
+            voltage for GHK calculation
+        ci : float, mM
+            internal ion concentration
+        co : float, mM
+            external ion concentraion
+        z : float, no units
+            valence
+        
+        Returns
+        -------
+        flux : A/m^2
+
+        """
+        F = 9.6485e4  # (coul)
+        R = 8.3145  # (joule/degC)
+        T = h.celsius + 273.19  # Kelvin
+        E = (1e-3) * v  # convert mV to V
+        Ci = ci + (self.soma().cap.monovalPerm) * (
+            self.soma().cap.monovalConc
+        )  #       : Monovalent permeability
+        if (
+            np.fabs(1 - np.exp(-z * (F * E) / (R * T))) < 1e-6
+        ):  # denominator is small -> Taylor series
+            ghk = (
+                (1e-6)
+                * z
+                * F
+                * (Ci - co * np.exp(-z * (F * E) / (R * T)))
+                * (1 - (z * (F * E) / (R * T)))
+            )
+        else:
+            ghk = (
+                (1e-6)
+                * z ** 2.0
+                * (E * F ** 2.0)
+                / (R * T)
+                * (Ci - co * np.exp(-z * (F * E) / (R * T)))
+                / (1 - np.exp(-z * (F * E) / (R * T)))
+            )
+        return ghk

cnmodel/cells/dstellate.py

@@ -0,0 +1,897 @@
+from __future__ import print_function
+from neuron import h
+from ..util import nstomho
+from .cell import Cell
+from ..util import Params
+from .. import synapses
+from .. import data
+
+__all__ = ["DStellate", "DStellateRothman", "DStellateEager"]
+
+
+class DStellate(Cell):
+
+    type = "dstellate"
+
+    @classmethod
+    def create(cls, model="RM03", **kwds):
+        if model == "RM03":
+            return DStellateRothman(**kwds)
+        elif model == "Eager":
+            return DStellateEager(**kwds)
+        elif model == "dummy":
+            return DummyDStellate(**kwds)
+        else:
+            raise ValueError("DStellate type %s is unknown", type)
+
+    def __init__(self):
+        Cell.__init__(self)
+        self.spike_source = (
+            None
+        )  # used by DummyDStellate to connect VecStim to terminal
+
+    def make_psd(self, terminal, psd_type, **kwds):
+        """
+        Connect a presynaptic terminal to one post section at the specified location, with the fraction
+        of the "standard" conductance determined by gbar.
+        The default condition is designed to pass the unit test (loc=0.5)
+        
+        Parameters
+        ----------
+        terminal : Presynaptic terminal (NEURON object)
+        
+        psd_type : either simple or multisite PSD for bushy cell
+        
+        kwds: dictionary of options. 
+            Two are currently handled:
+            postsize : expect a list consisting of [sectionno, location (float)]
+            AMPAScale : float to scale the ampa currents
+        
+        """
+        if (
+            "postsite" in kwds
+        ):  # use a defined location instead of the default (soma(0.5)
+            postsite = kwds["postsite"]
+            loc = postsite[1]  # where on the section?
+            uname = (
+                "sections[%d]" % postsite[0]
+            )  # make a name to look up the neuron section object
+            post_sec = self.hr.get_section(uname)  # Tell us where to put the synapse.
+        else:
+            loc = 0.5
+            post_sec = self.soma
+
+        if psd_type == "simple":
+            if terminal.cell.type in ["sgc", "dstellate", "tuberculoventral"]:
+                weight = data.get(
+                    "%s_synapse" % terminal.cell.type,
+                    species=self.species,
+                    post_type=self.type,
+                    field="weight",
+                )
+                tau1 = data.get(
+                    "%s_synapse" % terminal.cell.type,
+                    species=self.species,
+                    post_type=self.type,
+                    field="tau1",
+                )
+                tau2 = data.get(
+                    "%s_synapse" % terminal.cell.type,
+                    species=self.species,
+                    post_type=self.type,
+                    field="tau2",
+                )
+                erev = data.get(
+                    "%s_synapse" % terminal.cell.type,
+                    species=self.species,
+                    post_type=self.type,
+                    field="erev",
+                )
+                return self.make_exp2_psd(
+                    post_sec,
+                    terminal,
+                    weight=weight,
+                    loc=loc,
+                    tau1=tau1,
+                    tau2=tau2,
+                    erev=erev,
+                )
+            else:
+                raise TypeError(
+                    "Cannot make simple PSD for %s => %s"
+                    % (terminal.cell.type, self.type)
+                )
+
+        elif psd_type == "multisite":
+            if terminal.cell.type == "sgc":
+                # Max conductances for the glu mechanisms are calibrated by
+                # running `synapses/tests/test_psd.py`. The test should fail
+                # if these values are incorrect
+                self.AMPAR_gmax = (
+                    data.get(
+                        "sgc_synapse",
+                        species=self.species,
+                        post_type=self.type,
+                        field="AMPAR_gmax",
+                    )
+                    * 1e3
+                )
+                self.NMDAR_gmax = (
+                    data.get(
+                        "sgc_synapse",
+                        species=self.species,
+                        post_type=self.type,
+                        field="NMDAR_gmax",
+                    )
+                    * 1e3
+                )
+                self.Pr = data.get(
+                    "sgc_synapse", species=self.species, post_type=self.type, field="Pr"
+                )
+                # adjust gmax to correct for initial Pr
+                self.AMPAR_gmax = self.AMPAR_gmax / self.Pr
+                self.NMDAR_gmax = self.NMDAR_gmax / self.Pr
+                # old values:
+                # AMPA_gmax = 0.22479596944138733*1e3  # factor of 1e3 scales to pS (.mod mechanisms) from nS.
+                # NMDA_gmax = 0.12281291946623739*1e3
+                if "AMPAScale" in kwds:
+                    self.AMPAR_gmax = (
+                        self.AMPAR_gmax * kwds["AMPAScale"]
+                    )  # allow scaling of AMPA conductances
+                if "NMDAScale" in kwds:
+                    self.NMDAR_gmax = self.NMDAR_gmax * kwds["NMDAScale"]
+                return self.make_glu_psd(
+                    post_sec, terminal, self.AMPAR_gmax, self.NMDAR_gmax, loc=loc
+                )
+
+            elif terminal.cell.type == "dstellate":
+                # Get GLY kinetic constants from database
+                return self.make_gly_psd(post_sec, terminal, psdtype="glyfast", loc=loc)
+            elif terminal.cell.type == "tuberculoventral":
+                # Get GLY kinetic constants from database
+                return self.make_gly_psd(post_sec, terminal, psdtype="glyfast", loc=loc)
+            else:
+                raise TypeError(
+                    "Cannot make PSD for %s => %s" % (terminal.cell.type, self.type)
+                )
+        else:
+            raise ValueError("Unsupported psd type %s" % psd_type)
+
+    def make_terminal(self, post_cell, term_type, **kwds):
+        if term_type == "simple":
+            return synapses.SimpleTerminal(
+                self.soma, post_cell, spike_source=self.spike_source, **kwds
+            )
+        elif term_type == "multisite":
+            if post_cell.type in [
+                "dstellate",
+                "tuberculoventral",
+                "pyramidal",
+                "bushy",
+                "tstellate",
+            ]:
+                nzones = data.get(
+                    "dstellate_synapse",
+                    species=self.species,
+                    post_type=post_cell.type,
+                    field="n_rsites",
+                )
+                delay = data.get(
+                    "dstellate_synapse",
+                    species=self.species,
+                    post_type=post_cell.type,
+                    field="delay",
+                )
+            else:
+                raise NotImplementedError(
+                    "No knowledge as to how to connect D stellate cell to cell type %s"
+                    % type(post_cell)
+                )
+            pre_sec = self.soma
+            return synapses.StochasticTerminal(
+                pre_sec,
+                post_cell,
+                nzones=nzones,
+                spike_source=self.spike_source,
+                delay=delay,
+                **kwds
+            )
+        else:
+            raise ValueError("Unsupported terminal type %s" % term_type)
+
+
+class DStellateRothman(DStellate):
+    """
+    VCN D-stellate model:
+    as a type I-II from Rothman and Manis, 2003
+    """
+
+    def __init__(
+        self,
+        morphology=None,
+        decorator=None,
+        nach=None,
+        ttx=False,
+        species="guineapig",
+        modelType=None,
+        modelName=None,
+        debug=False,
+    ):
+        """
+        initialize a radial stellate (D-stellate) cell, using the default parameters for guinea pig from
+        R&M2003, as a type I-II cell.
+        Modifications to the cell can be made by calling methods below. These include:
+                
+            * changing the sodium channel
+            * Changing "species" to mouse or cat (scales conductances)
+            * Shifting model type
+                
+        Parameters
+        ----------
+        morphology : string (default: None)
+            Name of a .hoc file representing the morphology. This file is used to constructe
+            an electrotonic (cable) model. 
+            If None (default), then a "point" (really, single cylinder) model is made, exactly according to RM03.
+            
+        decorator : Python function (default: None)
+            decorator is a function that "decorates" the morphology with ion channels according
+            to a set of rules.
+            If None, a default set of channels is inserted into the first soma section, and the
+            rest of the structure is "bare".
+        
+        nach : string (default: None)
+            nach selects the type of sodium channel that will be used in the model. A channel mechanism
+            by that name must exist. A value of None will set the channel to a default for the model (nacn).
+        
+        ttx : Boolean (default: False)
+            If ttx is True, then the sodium channel conductance is set to 0 everywhere in the cell.
+            This flag duplicates the effects of tetrodotoxin in the model. Currently, the flag is not implemented.
+        
+        species: string (default 'guineapig')
+            species defines the pattern of ion channel densities that will be inserted, according to 
+            prior measurements in various species. Note that
+            if a decorator function is specified, this argument is ignored as the decorator will
+            specify the channel density.
+            
+        modelType: string (default: None)
+            modelType specifies the subtype of the cell model that will be used (e.g., "II", "II-I", etc).
+            modelType is passed to the decorator, or to species_scaling to adjust point (single cylinder) models.
+            
+        debug: boolean (default: False)
+            When True, there will be multiple printouts of progress and parameters.
+            
+            
+        Returns
+        -------
+            Nothing
+        """
+
+        super(DStellateRothman, self).__init__()
+        if modelType == None:
+            modelType = "I-II"
+        if species == "guineapig":
+            modelName = "RM03"
+            temp = 22.0
+            if nach == None:
+                nach = "nacn"
+        if species == "mouse":
+            temp = 34.0
+            if modelName is None:
+                modelName = "XM13"
+            if nach is None:
+                nach = "na"
+        self.debug = debug
+        # if modelType == None:  # allow us to pass None to get the default
+        #     modelType = 'I-II'
+        # if nach == None:
+        #     nach = 'na'
+        self.status = {
+            "soma": True,
+            "axon": False,
+            "dendrites": False,
+            "pumps": False,
+            "na": nach,
+            "species": species,
+            "modelType": modelType,
+            "modelName": modelName,
+            "ttx": ttx,
+            "name": "DStellate",
+            "morphology": morphology,
+            "decorator": decorator,
+            "temperature": None,
+        }
+        self.i_test_range = {
+            "pulse": [(-0.3, 0.3, 0.03), (-0.05, 0.0, 0.005)]
+        }  # set range for ic command test
+        self.vrange = [-75.0, -55.0]
+        self.spike_threshold = (
+            -40.0
+        )  # matches threshold in released CNModel (set in base cell class)
+
+        if morphology is None:
+            """
+            instantiate a basic soma-only ("point") model
+            """
+            soma = h.Section(
+                name="DStellate_Soma_%x" % id(self)
+            )  # one compartment of about 29000 um2
+            soma.nseg = 1
+            self.add_section(soma, "soma")
+        else:
+            """
+            instantiate a structured model with the morphology as specified by 
+            the morphology file
+            """
+            self.set_morphology(morphology_file=morphology)
+
+        # decorate the morphology with ion channels
+        if decorator is None:  # basic model, only on the soma
+            self.mechanisms = ["klt", "kht", "ihvcn", "leak", nach]
+            for mech in self.mechanisms:
+                self.soma.insert(mech)
+            self.soma.ena = self.e_na
+            self.soma.ek = self.e_k
+            self.soma().leak.erev = self.e_leak
+            self.c_m = 0.9
+            self.set_soma_size_from_Cm(12.0)
+            self.species_scaling(
+                silent=True, species=species, modelType=modelType
+            )  # set the default type II cell parameters
+        else:  # decorate according to a defined set of rules on all cell compartments
+            self.decorate()
+        self.save_all_mechs()  # save all mechanisms inserted, location and gbar values...
+        self.get_mechs(self.soma)
+
+        if self.debug:
+            print("<< D-stellate: JSR Stellate Type I-II cell model created >>")
+
+    def get_cellpars(self, dataset, species="guineapig", modelType="I-II"):
+        cellcap = data.get(
+            dataset, species=species, model_type=modelType, field="soma_Cap"
+        )
+        chtype = data.get(
+            dataset, species=species, model_type=modelType, field="na_type"
+        )
+        pars = Params(cap=cellcap, natype=chtype)
+        # pars.show()
+
+        if self.status["modelName"] == "RM03":
+            for g in [
+                "%s_gbar" % pars.natype,
+                "kht_gbar",
+                "klt_gbar",
+                "ka_gbar",
+                "ih_gbar",
+                "leak_gbar",
+                "leak_erev",
+                "ih_eh",
+                "e_k",
+                "e_na",
+            ]:
+                pars.additem(
+                    g, data.get(dataset, species=species, model_type=modelType, field=g)
+                )
+        if self.status["modelName"] == "XM13":
+            for g in [
+                "%s_gbar" % pars.natype,
+                "kht_gbar",
+                "klt_gbar",
+                "ka_gbar",
+                "ihvcn_gbar",
+                "leak_gbar",
+                "leak_erev",
+                "ih_eh",
+                "e_k",
+                "e_na",
+            ]:
+                pars.additem(
+                    g, data.get(dataset, species=species, model_type=modelType, field=g)
+                )
+        if self.status["modelName"] == "mGBC":
+            for g in [
+                "%s_gbar" % pars.natype,
+                "kht_gbar",
+                "klt_gbar",
+                "ka_gbar",
+                "ihvcn_gbar",
+                "leak_gbar",
+                "leak_erev",
+            ]:
+                pars.additem(
+                    g, data.get(dataset, species=species, model_type=modelType, field=g)
+                )
+        return pars
+
+    def species_scaling(self, species="guineapig", modelType="I-II", silent=True):
+        """
+        Adjust all of the conductances and the cell size according to the species requested.
+        
+        Parameters
+        ----------
+        species : string (default: 'guineapig')
+            A string specifying the species used for scaling. Recognized values are
+            'mouse', 'guineapig', and 'cat' (cat is just a larger version of the guineapig)
+        
+        modelType : string (default: 'I-II')
+            A string specifying the version of the model to use. 
+            Current choices are 'I-II' (others need to be implemented)
+        
+        silent : boolean (default: True)
+            Flag for printing debugging information.
+            
+        """
+        soma = self.soma
+        celltype = modelType
+        if species == "mouse":
+            # use conductance levels from Cao et al.,  J. Neurophys., 2007.
+            dataset = "XM13_channels"
+            self._valid_temperatures = (34.0,)
+            if self.status["temperature"] is None:
+                self.set_temperature(34.0)
+            self.c_m = 0.9
+            pars = self.get_cellpars(dataset, species=species, modelType=modelType)
+            self.set_soma_size_from_Cm(pars.cap)
+            self.status["na"] = pars.natype
+            # pars.show()
+            self.adjust_na_chans(soma, gbar=pars.na_gbar, sf=1.0)
+            soma().kht.gbar = nstomho(pars.kht_gbar, self.somaarea)
+            soma().klt.gbar = nstomho(pars.klt_gbar, self.somaarea)
+            # soma().ka.gbar = nstomho(pars.ka_gbar, self.somaarea)
+            soma().ihvcn.gbar = nstomho(pars.ihvcn_gbar, self.somaarea)
+            soma().ihvcn.eh = pars.ih_eh  # Rodrigues and Oertel, 2006
+            soma().leak.gbar = nstomho(pars.leak_gbar, self.somaarea)
+            soma().leak.erev = pars.leak_erev
+            self.e_k = pars.e_k
+            self.e_na = pars.e_na
+            soma.ena = self.e_na
+            soma.ek = self.e_k
+            # soma().leak.erev = pars.leak_erev
+            self.axonsf = 0.5
+
+        elif species == "guineapig":  # values from R&M 2003, Type II-I
+            dataset = "RM03_channels"
+            self.c_m = 0.9
+            self._valid_temperatures = (22.0, 38.0)
+            if self.status["temperature"] is None:
+                self.set_temperature(22.0)
+            sf = 1.0
+            if (
+                self.status["temperature"] == 38.0
+            ):  # adjust for 2003 model conductance levels at 38
+                sf = 3.03  # Q10 of 2, 22->38C. (p3106, R&M2003c)
+                self.i_test_range = {"pulse": (-0.3, 0.3, 0.03)}
+            self.vrange = [-75.0, -55.0]
+            pars = self.get_cellpars(dataset, species=species, modelType=modelType)
+            self.set_soma_size_from_Cm(pars.cap)
+            self.status["na"] = pars.natype
+            #            pars.show()
+            self.adjust_na_chans(soma, gbar=pars.nacn_gbar, sf=sf)
+            soma().kht.gbar = nstomho(pars.kht_gbar, self.somaarea)
+            soma().klt.gbar = nstomho(pars.klt_gbar, self.somaarea)
+            # soma().ka.gbar = nstomho(pars.ka_gbar, self.somaarea)
+            soma().ihvcn.gbar = nstomho(pars.ih_gbar, self.somaarea)
+            soma().leak.gbar = nstomho(pars.leak_gbar, self.somaarea)
+            soma().leak.erev = pars.leak_erev
+            self.axonsf = 0.5
+
+        else:
+            raise ValueError(
+                "Species %s or species-modelType %s is not recognized for D-Stellate cells"
+                % (species, modelType)
+            )
+        self.status["species"] = species
+        self.status["modelType"] = modelType
+        self.check_temperature()
+        #        self.cell_initialize(showinfo=False)
+        if not silent:
+            print("set cell as: ", species)
+            print(" with Vm rest = %6.3f" % self.vm0)
+
+    def adjust_na_chans(self, soma, sf=1.0, gbar=1000.0):
+        """
+        adjust the sodium channel conductance
+        
+        Parameters
+        ----------
+        soma : soma object (no default)
+            soma object whose sodium channel complement will have it's
+            conductances adjusted depending on the channel type
+        
+        gbar : float (default: 1000.)
+            The conductance to be set for the sodium channel
+        
+        debug : boolean (default: False)
+            Flag for printing the conductance value and Na channel model
+            
+        Returns
+        -------
+            Nothing
+        """
+        if self.status["ttx"]:
+            gnabar = 0.0
+        else:
+            gnabar = nstomho(gbar, self.somaarea) * sf
+        nach = self.status["na"]
+        if nach == "jsrna":
+            soma().jsrna.gbar = gnabar
+            soma.ena = self.e_na
+            if self.debug:
+                print("jsrna gbar: ", soma().jsrna.gbar)
+        elif nach == "nav11":
+            soma().nav11.gbar = gnabar * 0.5
+            soma.ena = self.e_na
+            soma().nav11.vsna = 4.3
+            if self.debug:
+                print("bushy using inva11")
+            print("nav11 gbar: ", soma().nav11.gbar)
+        elif nach == "na":
+            soma().na.gbar = gnabar
+            soma.ena = self.e_na
+            if self.debug:
+                print("na gbar: ", soma().na.gbar)
+        elif nach == "nacn":
+            soma().nacn.gbar = gnabar
+            soma.ena = self.e_na
+            if self.debug:
+                print("nacn gbar: ", soma().nacn.gbar)
+        else:
+            raise ValueError(
+                "Dstellate setting Na channels: channel %s not known" % nach
+            )
+
+    def add_axon(self):
+        """
+        Add a default axon from the generic cell class to the bushy cell (see cell class).
+        """
+        Cell.add_axon(self, self.soma, self.somaarea, self.c_m, self.R_a, self.axonsf)
+
+    def add_dendrites(self):
+        """
+        Add simple unbranched dendrites to basic Rothman Type I-II model.
+        The dendrites have some kht and ih current
+        """
+        cs = False  # not implemented outside here - internal Cesium.
+        nDend = range(4)  # these will be simple, unbranced, N=4 dendrites
+        dendrites = []
+        for i in nDend:
+            dendrites.append(h.Section(cell=self.soma))
+        for i in nDend:
+            dendrites[i].connect(self.soma)
+            dendrites[i].L = 300  # length of the dendrite (not tapered)
+            dendrites[i].diam = 1.25  # dendrite diameter
+            dendrites[i].nseg = 21  # # segments in dendrites
+            dendrites[i].Ra = 150  # ohm.cm
+            dendrites[i].insert("kht")
+            if cs is False:
+                dendrites[i]().kht.gbar = 0.005  # a little Ht
+            else:
+                dendrites[i]().kht.gbar = 0.0
+            dendrites[i].insert("leak")  # leak
+            dendrites[i]().leak.gbar = 0.0001
+            dendrites[i].insert("ihvcn")  # some H current
+            dendrites[i]().ihvcn.gbar = 0.0  # 0.001
+            dendrites[i]().ihvcn.eh = -43.0
+        self.maindend = dendrites
+        self.status["dendrites"] = True
+        self.add_section(self.maindend, "maindend")
+
+
+class DummyDStellate(DStellate):
+    """ DStellate class with no cell body; this cell only replays a predetermined
+    spike train. Useful for testing, or replacing spike trains to determine
+    the importance of spike structures within a network.
+    """
+
+    def __init__(self, cf=None, species="mouse"):
+        """
+        Parameters
+        ----------
+        cf : float (default: None)
+            Required: the characteristic frequency for the DStellate
+            Really just for reference.
+
+        """
+
+        DStellate.__init__(self)
+        self.vecstim = h.VecStim()
+
+        # this causes the terminal to receive events from the VecStim:
+        self.spike_source = self.vecstim
+
+        # just an empty section for holding the terminal
+        self.add_section(h.Section(), "soma")
+        self.status = {
+            "soma": True,
+            "axon": False,
+            "dendrites": False,
+            "pumps": False,
+            "na": None,
+            "species": species,
+            "modelType": "Dummy",
+            "modelName": "DummyDStellate",
+            "ttx": None,
+            "name": "DummyDStellate",
+            "morphology": None,
+            "decorator": None,
+            "temperature": None,
+        }
+        print("<< DStellate: Dummy DStellate Cell created >>")
+
+    def set_spiketrain(self, times):
+        """ Set the times of spikes (in seconds) to be replayed by the cell.
+        """
+        self._spiketrain = times
+        self._stvec = h.Vector(times)
+        self.vecstim.play(self._stvec)
+
+
+class DStellateEager(DStellate):
+    """
+    This is a model of the VCN D-Stellate cells as proposed by
+    Eager, M.A., Grayden, D.B., Burkitt, A.N., and Meffin, H.,
+    "A neural circuit model of the ventral cochlear nucleus",
+    Internet:
+    http://citeseerx.ist.pus.edu/viewdoc/download?doi=10.1.79.9620.pdf&rep
+    =rep&type=pdf
+    also cited as:
+    Proceedings of the 10th Australian International Conference on
+    Speech Science and Technology, pp. 539-544, 2004.
+    It is based on the Rothman and Manis (2003c) model,
+    with small modifications.
+    Their model includes dendrites and an axon, which are added in this version
+    """
+
+    def __init__(
+        self, nach="na", ttx=False, species="guineapig", modelType="I-II", debug=False
+    ):
+        """
+        Initialize the VCN D-stellate model of Eager et al. Some model parameters may be modified.
+        
+        Parameters
+        ----------
+        nach : string (default: 'na')
+            Set the sodium channel model. Choices are 'na', 'nav11', 'jsrna'
+        
+        ttx : boolean (default: False)
+            ttx sets the sodium channel conductance to 0
+        
+        species : string (default: 'guineapig')
+            species to use for conductance scaling
+        
+        modelType : string (default: 'I-II')
+            RM03 model type to use for conductances. 
+        
+        debug : boolean (default: False)
+            Flag to use to enable print statements for debugging purposes.
+        
+        """
+        super(DStellateEager, self).__init__()
+
+        self.status = {
+            "soma": True,
+            "axon": False,
+            "dendrites": False,
+            "pumps": False,
+            "na": nach,
+            "species": species,
+            "modelType": modelType,
+            "ttx": ttx,
+            "name": "DStellateEager",
+        }
+        self.i_test_range = (-0.25, 0.25, 0.025)  # set range for ic command test
+
+        soma = h.Section(name="DStellateEager_Soma_%x" % id(self))  # one compartment
+
+        soma.nseg = 1
+
+        if nach in ["nacn", "na"]:
+            soma.insert("na")
+        elif nach == "nav11":
+            soma.insert("nav11")
+        elif nach == "jsrna":
+            soma.insert("jsrna")
+        else:
+            raise ValueError("Sodium channel %s in type 1 cell not known" % nach)
+        self.debug = debug
+        soma.insert("kht")
+        soma.insert("klt")
+        soma.insert("ihvcn")
+        soma.insert("leak")
+        soma.ek = self.e_k
+        soma().leak.erev = self.e_leak
+        self.mechanisms = ["kht", "klt", "ihvcn", "leak", nach]
+        self.add_section(soma, "soma")
+        self.species_scaling(
+            silent=False, species=species, modelType=modelType
+        )  # set the default type II cell parameters
+        self.add_axon()  # must follow species scaling so that area parameters are available
+        self.add_dendrites()  # similar for dendrites
+        self.save_all_mechs()  # save all mechanisms inserted, location and gbar values...
+        self.get_mechs(soma)
+
+        if self.debug:
+            print("<< D-stellateEager: Eager DStellate Type I-II cell model created >>")
+
+    def species_scaling(self, species="guineapig", modelType="I-II", silent=True):
+        """
+        Adjust all of the conductances and the cell size according to the species requested.
+        
+        Parameters
+        ----------
+        species : string (default: 'guineapig')
+            A string specifying the species used for scaling. Recognized values are
+            'mouse', 'guineapig', and 'cat' (cat is just a larger version of the guineapig)
+        
+        modelType : string (default: 'I-II')
+            A string specifying the version of the model to use. 
+            Current choices are 'I-II' (others need to be implemented)
+        
+        silent : boolean (default: True)
+            Flag for printing debugging information.
+            
+        """
+        soma = self.soma
+        if species == "mouse" and modelType == "I-II":
+            # use conductance levels from Cao et al.,  J. Neurophys., 2007.
+            self.set_soma_size_from_Cm(25.0)
+            self.adjust_na_chans(soma, gbar=800.0)
+            soma().kht.gbar = nstomho(150.0, self.somaarea)
+            soma().klt.gbar = nstomho(20.0, self.somaarea)
+            soma().ihvcn.gbar = nstomho(2.0, self.somaarea)
+            soma().ihvcn.eh = -43  # Rodrigues and Oertel, 2006
+            soma().leak.gbar = nstomho(2.0, self.somaarea)
+            self.axonsf = 0.5
+        elif (
+            species == "guineapig" and modelType == "I-II"
+        ):  # values from R&M 2003, Type II-I
+            self.set_soma_size_from_Diam(25.0)
+            self.adjust_na_chans(soma, gbar=1000.0 * 0.75)
+            soma().kht.gbar = 0.02  # nstomho(150.0, self.somaarea)
+            soma().klt.gbar = 0.005  # nstomho(20.0, self.somaarea)
+            soma().ihvcn.gbar = 0.0002  # nstomho(2.0, self.somaarea)
+            soma().leak.gbar = 0.0005  # nstomho(2.0, self.somaarea)
+            self.axonsf = 1.0
+        elif (
+            species == "cat" and modelType == "I=II"
+        ):  # a cat is a big guinea pig Type I
+            self.set_soma_size_from_Cm(35.0)
+            self.adjust_na_chans(soma)
+            soma().kht.gbar = nstomho(150.0, self.somaarea)
+            soma().klt.gbar = nstomho(20.0, self.somaarea)
+            soma().ihvcn.gbar = nstomho(2.0, self.somaarea)
+            soma().leak.gbar = nstomho(2.0, self.somaarea)
+            self.axonsf = 1.0
+        else:
+            raise ValueError(
+                "Species %s or species-type %s is not recognized for D-StellateEager cells"
+                % (species, type)
+            )
+        self.status["species"] = species
+        self.status["type"] = modelType
+        self.cell_initialize(showinfo=True)
+        if not silent:
+            print(" set cell as: ", species)
+            print(" with Vm rest = %6.3f" % self.vm0)
+
+    def adjust_na_chans(self, soma, gbar=1000.0):
+        """
+        adjust the sodium channel conductance
+        
+        Parameters
+        ----------
+        soma : soma object (no default)
+            soma object whose sodium channel complement will have it's
+            conductances adjusted depending on the channel type
+        
+        gbar : float (default: 1000.)
+            The conductance to be set for the sodium channel
+        
+        Returns
+        -------
+            Nothing
+        """
+
+        if self.status["ttx"]:
+            gnabar = 0.0
+        else:
+            gnabar = nstomho(gbar, self.somaarea)
+        nach = self.status["na"]
+        if nach == "jsrna":
+            soma().jsrna.gbar = gnabar
+            soma.ena = self.e_na
+            if self.debug:
+                print("using jsrna with gbar: ", soma().jsrna.gbar)
+        elif nach == "nav11":
+            soma().nav11.gbar = gnabar * 0.5
+            soma.ena = self.e_na
+            soma().nav11.vsna = 4.3
+            if self.debug:
+                print("using inva11 with gbar:", soma().na.gbar)
+            print("nav11 gbar: ", soma().nav11.gbar)
+        elif nach == "na":
+            soma().na.gbar = gnabar
+            soma.ena = self.e_na
+            if self.debug:
+                print("using na with gbar: ", soma().na.gbar)
+        elif nach == "nach":
+            soma().nach.gbar = gnabar
+            soma.ena = self.e_na
+            if self.debug:
+                print(("uwing nacn with gbar: ", soma().nacn.gbar))
+        else:
+            raise ValueError(
+                "DstellateEager setting Na channels: channel %s not known" % nach
+            )
+        # print soma().na.gbar
+
+    def add_axon(self):
+        """
+        Adds an axon to the Eager. et al model
+        Cell.add_axon(self, nodes=1, c_m=self.c_m, R_a=self.R_a, axonsf=self.axonsf, dia=3.0, len=70, seg=2)
+        The Eager et al model just uses one cable, 70 microns long and 3 microns in dameter.
+        
+        Parameters
+        ----------
+            None
+        
+        Returns
+        -------
+            Nothing
+        """
+
+        naxons = 1
+        axon = []
+        for i in range(naxons):
+            axon.append(h.Section(cell=self.soma))
+        for i in range(naxons):
+            axon[i].connect(self.soma)
+            axon[i].L = 70
+            axon[i].diam = 3.0
+            axon[i].Ra = 500
+            axon[i].cm = 0.9
+            axon[i].nseg = 2
+            axon[i].insert("kht")
+            axon[i].insert("klt")
+            axon[i].insert("ihvcn")
+            axon[i].insert("leak")
+            axon[i].insert("na")
+            axon[i].ek = self.e_k
+            axon[i].ena = self.e_na
+            axon[i]().leak.erev = self.e_leak
+            axon[i]().na.gbar = 0.5
+            axon[i]().klt.gbar = 0.005
+            axon[i]().kht.gbar = 0.02
+            axon[i]().ihvcn.gbar = 0.0002
+            axon[i]().leak.gbar = 0.0005
+        self.status["axon"] = True
+        self.add_section(axon, "axon")
+
+    def add_dendrites(self):
+        """
+        Adds dendrites to the Eager model. The Eager model uses simple passive dendrites.
+        
+        Parameters
+        ----------
+            None
+        
+        Returns
+        -------
+            Nothing
+        """
+
+        nDend = range(2)  # these will be simple, unbranced, N=4 dendrites
+        dendrites = []
+        for i in nDend:
+            dendrites.append(h.Section(cell=self.soma))
+        for i in nDend:
+            dendrites[i].connect(self.soma)
+            dendrites[i].L = 1100  # length of the dendrite (not tapered)
+            dendrites[i].diam = 3.5  # dendrite diameter
+            dendrites[i].nseg = 5  # # segments in dendrites
+            dendrites[i].Ra = 1500  # ohm.cm
+            dendrites[i].insert("leak")  # leak
+            dendrites[i]().leak.gbar = 0.00025
+            dendrites[i]().leak.erev = self.e_leak
+        self.maindend = dendrites
+        self.status["dendrites"] = True
+        self.add_section(self.maindend, "maindend")

cnmodel/cells/hh.py

@@ -0,0 +1,47 @@
+from __future__ import print_function
+from neuron import h
+import neuron as nrn
+from ..util import nstomho
+
+from .cell import Cell
+
+__all__ = ["HH"]
+
+
+class HH(Cell):
+    """
+    Standard Hodgkin-Huxley mechanisms from NEURON
+    """
+
+    def __init__(self, debug=False, message=None):
+        super(HH, self).__init__()
+
+        soma = h.Section(
+            name="HH_Soma_%x" % id(self)
+        )  # one compartment of about 29000 um2
+        v_potassium = -80  # potassium reversal potential
+        v_sodium = 50  # sodium reversal potential
+        c_m = 1.0
+        scalefactor = 1.0  # This determines the relative size of the cell
+        rinsf = 1.0  # input resistance adjustment (also current...)
+        totcap = 20.0  # scalefactor * 1.0 # cap in pF for cell
+        effcap = totcap  # sometimes we change capacitance - that's effcap
+        somaarea = totcap * 1e-6 / c_m  # pf -> uF, cm = 1uf/cm^2 nominal
+        lstd = 1e4 * ((somaarea / 3.14159) ** 0.5)  # convert from cm to um
+
+        soma.nseg = 1
+        soma.diam = lstd
+        soma.L = lstd
+
+        seg = soma
+        seg.insert("hh")
+        seg.insert("pas")
+        if debug:
+            if message is None:
+                print("<< Standard HH model created >>")
+            else:
+                print(message)
+
+        self.add_section(soma, "soma")
+
+        self.vm0 = -67.536

cnmodel/cells/msoprincipal.py

@@ -0,0 +1,516 @@
+from __future__ import print_function
+from neuron import h
+
+from .cell import Cell
+
+# from .. import synapses
+from ..util import nstomho
+from ..util import Params
+import numpy as np
+from .. import data
+
+__all__ = ["MSO"]
+
+
+class MSO(Cell):
+
+    type = "mso"
+
+    @classmethod
+    def create(cls, model="MSO-principal", **kwds):
+        if model == "MSO-principal":
+            return MSOPrincipal(**kwds)
+        else:
+            raise ValueError("MSO cell model %s is unknown", model)
+
+    def make_psd(self, terminal, psd_type, **kwds):
+        """
+        Connect a presynaptic terminal to one post section at the specified location, with the fraction
+        of the "standard" conductance determined by gbar.
+        The default condition is designed to pass the unit test (loc=0.5)
+        
+        Parameters
+        ----------
+        terminal : Presynaptic terminal (NEURON object)
+        
+        psd_type : either simple or multisite PSD for MSO cell
+        
+        kwds: dictionary of options. 
+            Two are currently handled:
+            postsite : expect a list consisting of [sectionno, location (float)]
+            AMPAScale : float to scale the ampa currents
+        
+        """
+        if (
+            "postsite" in kwds
+        ):  # use a defined location instead of the default (soma(0.5)
+            postsite = kwds["postsite"]
+            loc = postsite[1]  # where on the section?
+            uname = (
+                "sections[%d]" % postsite[0]
+            )  # make a name to look up the neuron section object
+            post_sec = self.hr.get_section(uname)  # Tell us where to put the synapse.
+        else:
+            loc = 0.5
+            post_sec = self.soma
+
+        if psd_type == "simple":
+            return self.make_exp2_psd(post_sec, terminal, loc=loc)
+        elif psd_type == "multisite":
+            if terminal.cell.type == "bushy":
+                # Max conductances for the glu mechanisms are calibrated by
+                # running `synapses/tests/test_psd.py`. The test should fail
+                # if these values are incorrect
+                self.AMPAR_gmax = (
+                    data.get(
+                        "bushy_synapse",
+                        species=self.species,
+                        post_type=self.type,
+                        field="AMPAR_gmax",
+                    )
+                    * 1e3
+                )
+                self.NMDAR_gmax = (
+                    data.get(
+                        "bushy_synapse",
+                        species=self.species,
+                        post_type=self.type,
+                        field="NMDAR_gmax",
+                    )
+                    * 1e3
+                )
+                self.Pr = data.get(
+                    "bushy_synapse",
+                    species=self.species,
+                    post_type=self.type,
+                    field="Pr",
+                )
+                # adjust gmax to correct for initial Pr
+                self.AMPAR_gmax = self.AMPAR_gmax / self.Pr
+                self.NMDAR_gmax = self.NMDAR_gmax / self.Pr
+                if "AMPAScale" in kwds:  # normally, this should not be done!
+                    self.AMPAR_gmax = (
+                        self.AMPAR_gmax * kwds["AMPAScale"]
+                    )  # allow scaling of AMPA conductances
+                if "NMDAScale" in kwds:
+                    self.NMDAR_gmax = self.NMDAR_gmax * kwds["NMDAScale"]  # and NMDA...
+                return self.make_glu_psd(
+                    post_sec, terminal, self.AMPAR_gmax, self.NMDAR_gmax, loc=loc
+                )
+            else:
+                raise TypeError(
+                    "Cannot make PSD for %s => %s" % (terminal.cell.type, self.type)
+                )
+        else:
+            raise ValueError("Unsupported psd type %s" % psd_type)
+
+
+class MSOPrincipal(MSO):
+    """
+    VCN MSO cell models.
+        Using Rothman and Manis, 2003abc (Type II)
+        MSO principal cell type
+    """
+
+    def __init__(
+        self,
+        morphology=None,
+        decorator=None,
+        nach=None,
+        ttx=False,
+        species="guineapig",
+        modelType=None,
+        debug=False,
+        temperature=None,
+    ):
+        """
+        Create a MSO principal cell, using the default parameters for guinea pig from
+        R&M2003, as a type II cell.
+        Additional modifications to the cell can be made by calling methods below.
+        
+        Parameters
+        ----------
+        morphology : string (default: None)
+            Name of a .hoc file representing the morphology. This file is used to constructe
+            an electrotonic (cable) model. 
+            If None (default), then a "point" (really, single cylinder) model is made, exactly according to RM03.
+            
+        decorator : Python function (default: None)
+            decorator is a function that "decorates" the morphology with ion channels according
+            to a set of rules.
+            If None, a default set of channels is inserted into the first soma section, and the
+            rest of the structure is "bare".
+        
+        nach : string (default: None)
+            nach selects the type of sodium channel that will be used in the model. A channel mechanism
+            by that name must exist. The default channel is set to 'nacn' (R&M03)
+        
+        temperature : float (default: 22)
+            temperature to run the cell at. 
+                 
+        ttx : Boolean (default: False)
+            If ttx is True, then the sodium channel conductance is set to 0 everywhere in the cell.
+            This flag duplicates the effects of tetrodotoxin in the model. Currently, the flag is not implemented.
+        
+        temperature : float (default: None, sets to model default of 22)
+            temperature (deg C) to run the cell at. Must be a valid temperature for the model.
+        
+        species: string (default 'guineapig')
+            species defines the pattern of ion channel densities that will be inserted, according to 
+            prior measurements in various species. Note that
+            if a decorator function is specified, this argument is ignored as the decorator will
+            specify the channel density.
+            
+        modelType: string (default: None)
+            modelType specifies the subtype of the cell model that will be used (e.g., "II", "II-I", etc).
+            modelType is passed to the decorator, or to species_scaling to adjust point (single cylinder) models.
+            
+        debug: boolean (default: False)
+            When True, there will be multiple printouts of progress and parameters.
+            
+        Returns
+        -------
+            Nothing
+                 
+        """
+        super(MSO, self).__init__()
+        self.i_test_range = {
+            "pulse": (-1, 1, 0.05)
+        }  # note that this gets reset with decorator according to channels
+        # Changing the default values will cause the unit tests to fail!
+        if modelType == None:
+            modelType = "principal"
+        if nach == None and species == "guineapig":
+            nach = "na"
+        if nach == None and species == "mouse":
+            nach = "na"
+            self.i_test_range = {"pulse": (-1, 1.2, 0.05)}
+
+        self.status = {
+            "soma": True,
+            "axon": False,
+            "dendrites": False,
+            "pumps": False,
+            "hillock": False,
+            "initialsegment": False,
+            "myelinatedaxon": False,
+            "unmyelinatedaxon": False,
+            "na": nach,
+            "species": species,
+            "modelType": modelType,
+            "ttx": ttx,
+            "name": "MSO",
+            "morphology": morphology,
+            "decorator": decorator,
+            "temperature": temperature,
+        }
+
+        self.spike_threshold = -40
+        self.vrange = [-70.0, -55.0]  # set a default vrange for searching for rmp
+        print("model type, species: ", modelType, species, nach)
+        if morphology is None:
+            """
+            instantiate a basic soma-only ("point") model
+            """
+            print("<< MSO model: Creating point principal cell >>")
+            soma = h.Section(
+                name="MSO_Soma_%x" % id(self)
+            )  # one compartment of about 29000 um2
+            soma.nseg = 1
+            self.add_section(soma, "soma")
+        else:
+            """
+            instantiate a structured model with the morphology as specified by 
+            the morphology file
+            """
+            print(
+                "<< MSO principal cell model: Creating cell with morphology from %s >>"
+                % morphology
+            )
+            self.set_morphology(morphology_file=morphology)
+
+        # decorate the morphology with ion channels
+        if decorator is None:  # basic model, only on the soma
+            self.mechanisms = ["klt", "kht", "ihvcn", "leak", nach]
+            for mech in self.mechanisms:
+                self.soma.insert(mech)
+            self.soma.ena = self.e_na
+            self.soma.ek = self.e_k
+            self.soma().ihvcn.eh = self.e_h
+            self.soma().leak.erev = self.e_leak
+            self.c_m = 0.9
+            self.species_scaling(
+                silent=True, species=species, modelType=modelType
+            )  # set the default type II cell parameters
+        else:  # decorate according to a defined set of rules on all cell compartments
+            self.decorate()
+        self.save_all_mechs()  # save all mechanisms inserted, location and gbar values...
+        self.get_mechs(self.soma)
+
+        if debug:
+            print("   << Created cell >>")
+
+    def get_cellpars(self, dataset, species="guineapig", celltype="principal"):
+        cellcap = data.get(
+            dataset, species=species, cell_type=celltype, field="soma_Cap"
+        )
+        chtype = data.get(
+            dataset, species=species, cell_type=celltype, field="soma_na_type"
+        )
+        pars = Params(cap=cellcap, natype=chtype)
+        for g in ["soma_kht_gbar", "soma_klt_gbar", "soma_ih_gbar", "soma_leak_gbar"]:
+            pars.additem(
+                g, data.get(dataset, species=species, cell_type=celltype, field=g)
+            )
+        return pars
+
+    def species_scaling(self, species="guineapig", modelType="principal", silent=True):
+        """
+        Adjust all of the conductances and the cell size according to the species requested.
+        This scaling should be used ONLY for point models, as no other compartments
+        are scaled.
+        
+        This scaling routine also sets the temperature for the model to a default value. Some models
+        can be run at multiple temperatures, and so a default from one of the temperatures is used.
+        The calling cell.set_temperature(newtemp) will change the conductances and reinitialize
+        the cell to the new temperature settings.
+        
+        Parameters
+        ----------
+        species : string (default: 'guineapig')
+            name of the species to use for scaling the conductances in the base point model
+            Must be one of mouse, cat, guineapig
+        
+        modelType: string (default: 'principal')
+            definition of model type from RM03 models, principal cell for mso 
+        
+        silent : boolean (default: True)
+            run silently (True) or verbosely (False)
+        
+        """
+        # print '\nSpecies scaling: %s   %s' % (species, type)
+        knownspecies = ["guineapig"]
+
+        soma = self.soma
+        if modelType == "principal":
+            celltype = (
+                "MSO-principal"
+            )  # There are other possiblities in the literature - this is just the main one
+        else:
+            raise ValueError("model type not recognized")
+
+        if species == "guineapig":
+            print(
+                "  Setting conductances for guinea pig %s MSO cell, based on Rothman and Manis, 2003 bushy cell"
+                % modelType
+            )
+            self._valid_temperatures = (22.0, 38.0)
+            if self.status["temperature"] is None:
+                self.status["temperature"] = 22.0
+            self.i_test_range = {"pulse": (-0.4, 0.4, 0.02)}
+            sf = 1.0
+            if (
+                self.status["temperature"] == 38.0
+            ):  # adjust for 2003 model conductance levels at 38
+                sf = 2  # Q10 of 2, 22->38C. (p3106, R&M2003c)
+                # note that kinetics are scaled in the mod file.
+            dataset = "MSO_principal_channels"
+            pars = self.get_cellpars(dataset, species=species, celltype=celltype)
+            self.set_soma_size_from_Cm(pars.cap)
+            self.status["na"] = pars.natype
+            self.adjust_na_chans(soma, sf=sf)
+            soma().kht.gbar = nstomho(pars.soma_kht_gbar, self.somaarea)
+            soma().klt.gbar = nstomho(pars.soma_klt_gbar, self.somaarea)
+            soma().ihvcn.gbar = nstomho(pars.soma_ih_gbar, self.somaarea)
+            soma().leak.gbar = nstomho(pars.soma_leak_gbar, self.somaarea)
+
+            self.axonsf = 0.57
+
+        else:
+            errmsg = (
+                'Species "%s" or model type "%s" is not recognized for MSO cells.'
+                % (species, modelType)
+            )
+            errmsg += "\n  Valid species are: \n"
+            for s in knownspecies:
+                errmsg += "    %s\n" % s
+            errmsg += "-" * 40
+            raise ValueError(errmsg)
+
+        self.status["species"] = species
+        self.status["modelType"] = modelType
+        self.check_temperature()
+        #        self.cell_initialize(vrange=self.vrange)  # no need to do this just yet.
+        if not silent:
+            print(" set cell as: ", species)
+            print(" with Vm rest = %6.3f" % self.vm0)
+
+    def channel_manager(self, modelType="MSO-principal"):
+        """
+        This routine defines channel density maps and distance map patterns
+        for each type of compartment in the cell. The maps
+        are used by the ChannelDecorator class (specifically, its private
+        \_biophys function) to decorate the cell membrane.
+        These settings are only used if the decorator is called; otherwise
+        for point cells, the species_scaling routine defines the channel
+        densities.
+        
+        Parameters
+        ----------
+        modelType : string (default: 'RM03')
+            A string that defines the type of the model. Currently, only 1 type is implemented:
+            RM03: Rothman and Manis, 2003 somatic densities based on guinea pig bushy cell
+        
+        Returns
+        -------
+        Nothing
+        
+        Notes
+        -----
+        This routine defines the following variables for the class:
+        
+            * conductances (gBar)
+            * a channelMap (dictonary of channel densities in defined anatomical compartments)
+            * a current injection range for IV's (used for testing)
+            * a distance map, which defines how each conductance in a selected compartment
+              changes with distance from the soma. The current implementation includes both
+              linear and exponential gradients,
+              the minimum conductance at the end of the gradient, and the space constant or
+              slope for the gradient.
+        
+        """
+
+        self.c_m = 1e-6  # default in units of F/cm^2
+        if modelType == "MSO-principal":
+            #
+            # Create a model based on the Rothman and Manis 2003 conductance set from guinea pig
+            #
+            self.c_m = 0.9e-6  # default in units of F/cm^2
+            totcap = 12.0e-12  # in units of F, from Rothman and Manis, 2003.
+            refarea = totcap / self.c_m  # area is in cm^2
+            # MSO Rothman-Manis, guinea pig type II
+            # model gave cell conductance in nS, but we want S/cm^2 for NEURON
+            # so conversion is 1e-9*nS = uS, and refarea is already in cm2
+            self._valid_temperatures = (22.0, 38.0)
+            sf = 1.0
+            if self.status["temperature"] == None:
+                self.status["temperature"] = 22.0
+            if self.status["temperature"] == 38:
+                sf = 3.03
+            self.gBar = Params(
+                nabar=sf * 1000.0e-9 / refarea,
+                khtbar=sf * 150.0e-9 / refarea,
+                kltbar=sf * 200.0e-9 / refarea,
+                ihbar=sf * 20.0e-9 / refarea,
+                leakbar=sf * 2.0e-9 / refarea,
+            )
+            print("MSO principal channels gbar:\n", self.gBar.show())
+
+            self.channelMap = {
+                "axon": {
+                    "nacn": self.gBar.nabar,
+                    "klt": self.gBar.kltbar,
+                    "kht": self.gBar.khtbar,
+                    "ihvcn": 0.0,
+                    "leak": self.gBar.leakbar / 2.0,
+                },
+                "hillock": {
+                    "nacn": self.gBar.nabar,
+                    "klt": self.gBar.kltbar,
+                    "kht": self.gBar.khtbar,
+                    "ihvcn": 0.0,
+                    "leak": self.gBar.leakbar,
+                },
+                "initseg": {
+                    "nacn": self.gBar.nabar,
+                    "klt": self.gBar.kltbar,
+                    "kht": self.gBar.khtbar,
+                    "ihvcn": self.gBar.ihbar / 2.0,
+                    "leak": self.gBar.leakbar,
+                },
+                "soma": {
+                    "nacn": self.gBar.nabar,
+                    "klt": self.gBar.kltbar,
+                    "kht": self.gBar.khtbar,
+                    "ihvcn": self.gBar.ihbar,
+                    "leak": self.gBar.leakbar,
+                },
+                "dend": {
+                    "nacn": self.gBar.nabar,
+                    "klt": self.gBar.kltbar * 0.5,
+                    "kht": self.gBar.khtbar * 0.5,
+                    "ihvcn": self.gBar.ihbar / 3.0,
+                    "leak": self.gBar.leakbar * 0.5,
+                },
+                "apic": {
+                    "nacn": self.gBar.nabar,
+                    "klt": self.gBar.kltbar * 0.2,
+                    "kht": self.gBar.khtbar * 0.2,
+                    "ihvcn": self.gBar.ihbar / 4.0,
+                    "leak": self.gBar.leakbar * 0.2,
+                },
+            }
+            #            self.irange = np.linspace(-1., 1., 21)
+            self.distMap = {
+                "dend": {
+                    "klt": {"gradient": "linear", "gminf": 0.0, "lambda": 100.0},
+                    "kht": {"gradient": "linear", "gminf": 0.0, "lambda": 100.0},
+                    "nacn": {"gradient": "exp", "gminf": 0.0, "lambda": 100.0},
+                },  # linear with distance, gminf (factor) is multiplied by gbar
+                "apic": {
+                    "klt": {"gradient": "linear", "gminf": 0.0, "lambda": 100.0},
+                    "kht": {"gradient": "linear", "gminf": 0.0, "lambda": 100.0},
+                    "nacn": {"gradient": "exp", "gminf": 0.0, "lambda": 100.0},
+                },  # gradients are: flat, linear, exponential
+            }
+
+        else:
+            raise ValueError("model type %s is not implemented" % modelType)
+        self.check_temperature()
+
+    def adjust_na_chans(self, soma, sf=1.0, gbar=1000.0, debug=False):
+        """
+        adjust the sodium channel conductance
+        
+        Parameters
+        ----------
+        soma : neuron section object
+            A soma object whose sodium channel complement will have its 
+            conductances adjusted depending on the channel type
+        
+        gbar : float (default: 1000.)
+            The maximal conductance for the sodium channel
+        
+        debug : boolean (false):
+            Verbose printing
+            
+        Returns
+        -------
+            Nothing :
+        
+        """
+
+        if self.status["ttx"]:
+            gnabar = 0.0
+        else:
+            gnabar = nstomho(gbar, self.somaarea) * sf
+        nach = self.status["na"]
+        if nach == "jsrna":
+            soma().jsrna.gbar = gnabar
+            soma.ena = self.e_na
+            if debug:
+                print("jsrna gbar: ", soma().jsrna.gbar)
+        elif nach == "nav11":
+            soma().nav11.gbar = gnabar
+            soma.ena = 50  # self.e_na
+            #            print('gnabar: ', soma().nav11.gbar, ' vs: 0.0192307692308')
+            soma().nav11.vsna = 4.3
+            if debug:
+                print("MSO using inva11")
+        elif nach in ["na", "nacn"]:
+            soma().na.gbar = gnabar
+            soma.ena = self.e_na
+            if debug:
+                print("na gbar: ", soma().na.gbar)
+        else:
+            raise ValueError("Sodium channel %s is not recognized for MSO cells", nach)

cnmodel/cells/octopus.py

@@ -0,0 +1,718 @@
+from __future__ import print_function
+from neuron import h
+from ..util import nstomho
+from ..util import Params
+import numpy as np
+from .cell import Cell
+from .. import data
+
+"""
+Original hoc code from RMmodel.hoc
+// including the "Octopus" cell:
+proc set_Type2o() {
+    gbar_na = nstomho(1000)
+    gbar_kht = nstomho(150)
+    gbar_klt = nstomho(600)
+    gbar_ka = nstomho(0)
+    gbar_ih = nstomho(0)
+    gbar_hcno = nstomho(40)
+    gbar_leak = nstomho(2)
+    model = 6
+    modelname = "Type IIo (Octopus)"
+    vm0 = -66.67
+}
+
+"""
+
+__all__ = ["Octopus", "OctopusRothman", "OctopusSpencer"]
+
+
+class Octopus(Cell):
+
+    type = "octopus"
+
+    @classmethod
+    def create(cls, modelType="RM03", **kwds):
+        if modelType in ["RM03", "II-o"]:
+            return OctopusRothman(**kwds)
+        elif modelType == "Spencer":
+            return OctopusSpencer(**kwds)
+        else:
+            raise ValueError("Octopus cell type %s is unknown" % modelType)
+
+    def make_psd(self, terminal, psd_type, **kwds):
+        """
+        Connect a presynaptic terminal to one post section at the specified location, with the fraction
+        of the "standard" conductance determined by gbar.
+        The default condition is to try to pass the default unit test (loc=0.5)
+        
+        Parameters
+        ----------
+        terminal : Presynaptic terminal (NEURON object)
+        
+        psd_type : either simple or multisite PSD for bushy cell
+        
+        kwds: dict of options. Two are currently handled:
+        postsize : expect a list consisting of [sectionno, location (float)]
+        AMPAScale : float to scale the ampa currents
+        
+        """
+        if (
+            "postsite" in kwds
+        ):  # use a defined location instead of the default (soma(0.5)
+            postsite = kwds["postsite"]
+            loc = postsite[1]  # where on the section?
+            uname = (
+                "sections[%d]" % postsite[0]
+            )  # make a name to look up the neuron section object
+            post_sec = self.hr.get_section(uname)  # Tell us where to put the synapse.
+        else:
+            loc = 0.5
+            post_sec = self.soma
+
+        if psd_type == "simple":
+            if terminal.cell.type in ["sgc"]:
+                weight = data.get(
+                    "%s_synapse" % terminal.cell.type,
+                    species=self.species,
+                    post_type=self.type,
+                    field="weight",
+                )
+                tau1 = data.get(
+                    "%s_synapse" % terminal.cell.type,
+                    species=self.species,
+                    post_type=self.type,
+                    field="tau1",
+                )
+                tau2 = data.get(
+                    "%s_synapse" % terminal.cell.type,
+                    species=self.species,
+                    post_type=self.type,
+                    field="tau2",
+                )
+                erev = data.get(
+                    "%s_synapse" % terminal.cell.type,
+                    species=self.species,
+                    post_type=self.type,
+                    field="erev",
+                )
+                return self.make_exp2_psd(
+                    post_sec,
+                    terminal,
+                    weight=weight,
+                    loc=loc,
+                    tau1=tau1,
+                    tau2=tau2,
+                    erev=erev,
+                )
+            else:
+                raise TypeError(
+                    "Cannot make simple PSD for %s => %s"
+                    % (terminal.cell.type, self.type)
+                )
+
+        elif psd_type == "multisite":
+            if terminal.cell.type == "sgc":
+                # Max conductances for the glu mechanisms are calibrated by
+                # running `synapses/tests/test_psd.py`. The test should fail
+                # if these values are incorrect
+                self.AMPAR_gmax = (
+                    data.get(
+                        "sgc_synapse",
+                        species=self.species,
+                        post_type=self.type,
+                        field="AMPAR_gmax",
+                    )
+                    * 1e3
+                )
+                self.NMDAR_gmax = (
+                    data.get(
+                        "sgc_synapse",
+                        species=self.species,
+                        post_type=self.type,
+                        field="NMDAR_gmax",
+                    )
+                    * 1e3
+                )
+                self.Pr = data.get(
+                    "sgc_synapse", species=self.species, post_type=self.type, field="Pr"
+                )
+                # adjust gmax to correct for initial Pr
+                self.AMPAR_gmax = self.AMPAR_gmax / self.Pr
+                self.NMDAR_gmax = self.NMDAR_gmax / self.Pr
+                # AMPA_gmax = 3.314707700918133*1e3  # factor of 1e3 scales to pS (.mod mechanisms) from nS.
+                # NMDA_gmax = 0.4531929783503451*1e3
+                if "AMPAScale" in kwds:
+                    self.AMPAR_gmax = (
+                        self.AMPAR_gmax * kwds["AMPAScale"]
+                    )  # allow scaling of AMPA conductances
+                if "NMDAScale" in kwds:
+                    self.NMDAR_gmax = self.NMDAR_gmax * kwds["NMDAScale"]
+                return self.make_glu_psd(
+                    post_sec, terminal, self.AMPAR_gmax, self.NMDAR_gmax, loc=loc
+                )
+            elif terminal.cell.type == "dstellate":
+                return self.make_gly_psd(post_sec, terminal, psdtype="glyslow", loc=loc)
+            else:
+                raise TypeError(
+                    "Cannot make PSD for %s => %s" % (terminal.cell.type, self.type)
+                )
+        else:
+            raise ValueError("Unsupported psd type %s" % psd_type)
+
+
+class OctopusRothman(Octopus, Cell):
+    """
+    VCN octopus cell model (point cell).
+    Rothman and Manis, 2003abc (Type II, with high gklt and hcno - octopus cell h current).
+    """
+
+    def __init__(
+        self,
+        morphology=None,
+        decorator=None,
+        nach=None,
+        ttx=False,
+        species="guineapig",
+        modelType=None,
+        debug=False,
+    ):
+        """
+        initialize the octopus cell, using the default parameters for guinea pig from
+        R&M2003, as a type II cell with modified conductances.
+        Modifications to the cell can be made by calling methods below.
+        
+        Parameters
+        ----------
+        morphology : string (default: None)
+            a file name to read the cell morphology from. If a valid file is found, a cell is constructed
+            as a cable model from the hoc file.
+            If None (default), the only a point model is made, exactly according to RM03.
+            
+        decorator : Python function (default: None)
+            decorator is a function that "decorates" the morphology with ion channels according
+            to a set of rules.
+            If None, a default set of channels aer inserted into the first soma section, and the
+            rest of the structure is "bare".
+        
+        nach : string (default: None)
+            nach selects the type of sodium channel that will be used in the model. A channel mechanism
+            by that name must exist. None implies the default channel (jsrna for this model).
+        
+        ttx : Boolean (default: False)
+            If ttx is True, then the sodium channel conductance is set to 0 everywhere in the cell.
+            Currently, this is not implemented.
+        
+        species: string (default 'guineapig')
+            species defines the channel density that will be inserted for different models. Note that
+            if a decorator function is specified, this argument is ignored.
+            
+        modelType: string (default: None)
+            modelType specifies the type of the model that will be used (e.g., "II", "II-I", etc).
+            modelType is passed to the decorator, or to species_scaling to adjust point models.
+            
+        debug: boolean (default: False)
+            debug is a boolean flag. When set, there will be multiple printouts of progress and parameters.
+            
+        Returns
+        -------
+            Nothing
+        """
+
+        super(OctopusRothman, self).__init__()
+        if modelType == None:
+            modelType = "II-o"
+        if nach == None and species == "guineapig":
+            nach = "jsrna"
+        if nach == None and species == "mouse":
+            nach = "nacn"
+        self.status = {
+            "soma": True,
+            "axon": False,
+            "dendrites": False,
+            "pumps": False,
+            "na": nach,
+            "species": species,
+            "modelType": modelType,
+            "ttx": ttx,
+            "name": "Octopus",
+            "morphology": morphology,
+            "decorator": decorator,
+            "temperature": None,
+        }
+        self.i_test_range = {"pulse": (-4.0, 4.0, 0.2)}
+        self.spike_threshold = -50
+        self.vrange = [-70.0, -57.0]  # set a default vrange for searching for rmp
+
+        if morphology is None:
+            """
+            instantiate a basic soma-only ("point") model
+            """
+            soma = h.Section(
+                name="Octopus_Soma_%x" % id(self)
+            )  # one compartment of about 29000 um2
+            soma.nseg = 1
+            self.add_section(soma, "soma")
+        else:
+            """
+            instantiate a structured model with the morphology as specified by 
+            the morphology file
+            """
+            self.set_morphology(morphology_file=morphology)
+
+        # decorate the morphology with ion channels
+        if decorator is None:  # basic model, only on the soma
+            self.e_leak = -73.0  # from McGinley et al., 2016
+            self.e_h = -38.0  # from McGinley et al.
+            self.R_a = 195  # McGinley et al.
+            if self.status["species"] == "mouse":
+                self.mechanisms = ["klt", "kht", "hcnobo", "leak", nach]
+            else:
+                self.mechanisms = ["klt", "kht", "ihvcn", "leak", nach]
+            for mech in self.mechanisms:
+                self.soma.insert(mech)
+            self.soma.ek = self.e_k
+            self.soma.ena = self.e_na
+            if self.status["species"] == "mouse":
+                self.soma().hcnobo.eh = self.e_h
+            else:
+                self.soma().ihvcn.eh = self.e_h
+            self.soma().leak.erev = self.e_leak
+            self.soma.Ra = self.R_a
+            self.species_scaling(
+                silent=True, species=species, modelType=modelType
+            )  # set the default type II cell parameters
+        else:  # decorate according to a defined set of rules on all cell compartments
+            self.decorate()
+        self.save_all_mechs()  # save all mechanisms inserted, location and gbar values...
+        self.get_mechs(self.soma)
+
+        if debug:
+            print("<< octopus: octopus cell model created >>")
+        # print 'Cell created: ', self.status
+
+    def species_scaling(self, species="guineapig", modelType="II-o", silent=True):
+        """
+        Adjust all of the conductances and the cell size according to the species requested.
+        Used ONLY for point models.
+        
+        Parameters
+        ----------
+        species : string (default: 'guineapig')
+            name of the species to use for scaling the conductances in the base point model
+            Must be guineapig
+        
+        modelType: string (default: 'II-o')
+            definition of model type from RM03 models, currently limited to type II-o
+        
+        silent : boolean (default: True)
+            run silently (True) or verbosely (False)
+        """
+        soma = self.soma
+
+        if species == "guineapig" and modelType == "II-o":
+            self.c_m = 0.9
+            self.set_soma_size_from_Cm(25.0)
+            self._valid_temperatures = (22.0, 38.0)
+            if self.status["temperature"] is None:
+                self.set_temperature(22.0)
+            sf = 1.0
+            if (
+                self.status["temperature"] == 38.0
+            ):  # adjust for 2003 model conductance levels at 38
+                sf = 3.03  # Q10 of 2, 22->38C. (p3106, R&M2003c)
+                # note that kinetics are scaled in the mod file.
+            #            self.print_soma_info()
+            self.adjust_na_chans(soma, sf=sf)
+            soma().kht.gbar = sf * nstomho(150.0, self.somaarea)  # 6.1 mmho/cm2
+            soma().klt.gbar = sf * nstomho(
+                1000.0, self.somaarea
+            )  #  40.7 mmho/cm2  3195?
+            soma().ihvcn.gbar = sf * nstomho(
+                30.0, self.somaarea
+            )  # 7.6 mmho/cm2, cf. Bal and Oertel, Spencer et al. 25 u dia cell 40ns?
+            soma().leak.gbar = sf * nstomho(2.0, self.somaarea)
+            self.axonsf = 1.0
+        elif species == "mouse" and modelType == "II-o":
+            self.set_soma_size_from_Cm(25.0)
+            self._valid_temperatures = (34.0,)
+            if self.status["temperature"] is None:
+                self.set_temperature(34.0)
+            #            self.print_soma_info()
+            self.adjust_na_chans(soma, gbar=3000.0)
+            soma().kht.gbar = nstomho(150.0, self.somaarea)  # 6.1 mmho/cm2
+            soma().klt.gbar = nstomho(3196.0, self.somaarea)  #  40.7 mmho/cm2
+            soma().hcnobo.gbar = nstomho(
+                40.0, self.somaarea
+            )  # 7.6 mmho/cm2, cf. Bal and Oertel, Spencer et al. 25 u dia cell
+            soma().leak.gbar = nstomho(2.0, self.somaarea)
+            self.axonsf = 1.0
+        else:
+            raise ValueError(
+                'Species "%s" or species-type "%s" is not recognized for octopus cells'
+                % (species, type)
+            )
+        self.status["species"] = species
+        self.status["modelType"] = modelType
+        #        self.cell_initialize(showinfo=True)
+        self.check_temperature()
+        if not silent:
+            print("set cell as: ", species)
+            print(" with Vm rest = %6.3f" % self.vm0)
+
+    def adjust_na_chans(self, soma, sf=1.0, gbar=1000.0, debug=False):
+        """
+        adjust the sodium channel conductance
+        
+        Parameters
+        ----------
+        soma : neuron section object
+            a soma object whose sodium channel complement will have it's 
+            conductances adjusted depending on the channel type
+        
+        gbar : float (default: 1000.)
+            the maximal conductance for the sodium channel
+        
+        debug : boolean (false):
+            verbose printing
+            
+        Returns
+        -------
+        Nothing
+        """
+
+        if self.status["ttx"]:
+            gnabar = 0.0
+        else:
+            gnabar = sf * nstomho(gbar, self.somaarea)  # mmho/cm2 - 4244.1 moh - 4.2441
+        nach = self.status["na"]
+        if nach == "jsrna":
+            soma().jsrna.gbar = gnabar
+            soma.ena = self.e_na
+            if debug:
+                print("octopus using jsrna, gbar: ", soma().jsrna.gbar)
+        elif nach == "nav11":
+            soma().nav11.gbar = gnabar
+            soma.ena = self.e_na
+            soma().nav11.vsna = 4.3
+            if debug:
+                print("octopus using inva11, gbar:", soma().nav11.gbar)
+        elif nach in ["na", "nacn"]:
+            soma().nacn.gbar = gnabar
+            soma.ena = self.e_na
+            if debug:
+                print("octopus cell using na/nacn, gbar: ", soma().na.gbar)
+        else:
+            raise ValueError(
+                "Sodium channel %s is not recognized for octopus cells", nach
+            )
+
+
+class OctopusSpencer(Octopus, Cell):
+    """
+    VCN octopus cell model (with dendrites).
+    Based on Spencer et al Front. Comput. Neurosci., 22 October 2012
+    https://doi.org/10.3389/fncom.2012.00083
+    """
+
+    def __init__(
+        self,
+        morphology=None,
+        decorator=None,
+        nach="jsrna",
+        ttx=False,
+        species="guineapig",
+        modelType=None,
+        debug=False,
+    ):
+        """
+        initialize the octopus cell, using the parameters Spencer et al. 2012
+        Modifications to the cell can be made by calling methods below.
+        
+        Parameters
+        ----------
+        morphology : string (default: None)
+            a file name to read the cell morphology from. If a valid file is found, a cell is constructed
+            as a cable model from the hoc file.
+            If None (default), the only a point model is made, exactly according to RM03.
+            
+        decorator : Python function (default: None)
+            decorator is a function that "decorates" the morphology with ion channels according
+            to a set of rules.
+            If None, a default set of channels aer inserted into the first soma section, and the
+            rest of the structure is "bare".
+        
+        nach : string (default: 'na')
+            nach selects the type of sodium channel that will be used in the model. A channel mechanism
+            by that name must exist. 
+        
+        ttx : Boolean (default: False)
+            If ttx is True, then the sodium channel conductance is set to 0 everywhere in the cell.
+            Currently, this is not implemented.
+        
+        species: string (default 'guineapig')
+            species defines the channel density that will be inserted for different models. Note that
+            if a decorator function is specified, this argument is ignored.
+            
+        modelType: string (default: None)
+            modelType specifies the type of the model that will be used (e.g., "II", "II-I", etc).
+            modelType is passed to the decorator, or to species_scaling to adjust point models.
+            
+        debug: boolean (default: False)
+            debug is a boolean flag. When set, there will be multiple printouts of progress and parameters.
+            
+        Returns
+        -------
+            Nothing
+        """
+
+        super(OctopusSpencer, self).__init__()
+        if modelType == None:
+            modelType = "Spencer"
+        self.status = {
+            "soma": True,
+            "axon": False,
+            "dendrites": False,
+            "pumps": False,
+            "na": nach,
+            "species": species,
+            "modelType": modelType,
+            "ttx": ttx,
+            "name": "Octopus",
+            "morphology": morphology,
+            "decorator": decorator,
+            "temperature": None,
+        }
+        self.i_test_range = (-4.0, 6.0, 0.25)
+        self.spike_threshold = -50
+        self.vrange = [-75.0, -63.0]  # set a default vrange for searching for rmp
+
+        if morphology is None:
+            """
+            instantiate a basic soma-only ("point") model
+            """
+            soma = h.Section(
+                name="Octopus_Soma_%x" % id(self)
+            )  # one compartment of about 29000 um2
+            soma.nseg = 1
+            self.add_section(soma, "soma")
+            self.set_soma_size_from_Section(self.soma)
+
+        else:
+            """
+            instantiate a structured model with the morphology as specified by 
+            the morphology file
+            """
+            self.set_morphology(morphology_file=morphology)
+
+        # decorate the morphology with ion channels
+        if decorator is None:  # basic model, only on the soma
+            self.e_leak = -62.0  # from Spencer et al., 2012
+            self.e_h = -38.0  ## from Spencer et al., 2012
+            self.R_a = 100.0  # from Spencer et al., 2012
+            self.mechanisms = ["klt", "kht", "hcnobo", "leak", nach]
+            for mech in self.mechanisms:
+                self.soma.insert(mech)
+            self.soma.ek = -70.0  # self.e_k
+            self.soma.ena = 55.0  # self.e_na
+            self.soma().hcnobo.eh = self.e_h
+            self.soma().leak.erev = self.e_leak
+            self.soma.Ra = self.R_a
+            self.species_scaling(
+                silent=True, species=species, modelType=modelType
+            )  # set the default type II cell parameters
+        else:  # decorate according to a defined set of rules on all cell compartments
+            self.decorate()
+            self.decorated.channelValidate(self, verify=True)
+        #        print 'Mechanisms inserted: ', self.mechanisms
+        self.get_mechs(self.soma)
+        #        self.cell_initialize(vrange=self.vrange)
+
+        if debug:
+            print("<< octopus: octopus cell model created >>")
+        # print 'Cell created: ', self.status
+
+    def channel_manager(self, modelType="Spencer"):
+        """
+        This routine defines channel density maps and distance map patterns
+        for each type of compartment in the cell. The maps
+        are used by the ChannelDecorator class (specifically, it's private
+        \_biophys function) to decorate the cell membrane.
+        
+        Parameters
+        ----------
+        modelType : string (default: 'Spencer')
+            A string that defines the type of the model. Currently, 1 type is implemented:
+            Spencer : Spencer et al Front. Comput. Neurosci. 2012
+        
+        Returns
+        -------
+        Nothing
+        
+        Notes
+        -----
+        This routine defines the following variables for the class:
+        # conductances (gBar)
+        # a channelMap (dictonary of channel densities in defined anatomical compartments)
+        # a current injection range for IV's (when testing)
+        # a distance map, which defines how selected conductances in selected compartments
+        will change with distance. This includes both linear and exponential gradients,
+        the minimum conductance at the end of the gradient, and the space constant or
+        slope for the gradient.
+        
+        """
+
+        #
+        # Create a model based on the Spencer model
+        # Channel decoration and stick model from Figure 2
+        # densities from Tables 2 and 3
+        if modelType == "Spencer":
+            #            print self.c_m
+            self.c_m = 0.9
+            #            self.set_soma_size_from_Section(self.soma)
+            totcap = self.totcap
+            refarea = self.somaarea  # totcap / self.c_m  # see above for units
+            #            self.print_soma_info()
+            self._valid_temperatures = (
+                34.0,
+            )  # 34 for consistency with other mouse models, but
+            # Spencer data used "33". This affects very slightly
+            # the HCN channel conductance.
+            if self.status["temperature"] is None:
+                self.set_temperature(34.0)
+            self.gBar = Params(
+                nabar=0.0,  # 0.0407,  # S/cm2
+                nabar_ais=0.42441,
+                kltbar_ais=0.0,
+                khtbar_ais=0.0,
+                ihbar_ais=0.0,
+                kltbar_soma=0.0407,
+                khtbar_soma=0.0061,
+                ihbar_soma=0.0076,
+                kltbar_dend=0.0027,
+                khtbar_dend=0.0,
+                ihbar_dend=0.0006,
+                khtbar_hillock=0.0,
+                kltbar_hillock=0.0,
+                ihbar_hillock=0.0,
+                leakbar=0.0020,
+            )
+
+            self.channelMap = {
+                "soma": {
+                    "jsrna": self.gBar.nabar,
+                    "klt": self.gBar.kltbar_soma,
+                    "kht": self.gBar.khtbar_soma,
+                    "hcnobo": self.gBar.ihbar_soma,
+                    "leak": self.gBar.leakbar,
+                },
+                "hillock": {
+                    "jsrna": 0.0,
+                    "klt": self.gBar.kltbar_hillock,
+                    "kht": self.gBar.khtbar_hillock,
+                    "hcnobo": self.gBar.ihbar_hillock,
+                    "leak": self.gBar.leakbar,
+                },
+                # axon initial segment:
+                "unmyelinatedaxon": {
+                    "jsrna": self.gBar.nabar_ais,
+                    "klt": self.gBar.kltbar_ais,
+                    "kht": self.gBar.khtbar_ais,
+                    "hcnobo": self.gBar.ihbar_ais,
+                    "leak": self.gBar.leakbar,
+                },
+                "primarydendrite": {
+                    "jsrna": 0.0,
+                    "klt": self.gBar.kltbar_dend,
+                    "kht": self.gBar.khtbar_dend,
+                    "hcnobo": self.gBar.ihbar_dend,
+                    "leak": self.gBar.leakbar,
+                },
+            }
+
+            self.distMap = {
+                "primarydendrite": {
+                    "klt": {"gradient": "flat", "gminf": 0.0, "lambda": 100.0},
+                    "kht": {"gradient": "flat", "gminf": 0.0, "lambda": 100.0},
+                    "hcnobo": {"gradient": "flat", "gminf": 0.0, "lambda": 100.0},
+                }  # all flat with distance
+            }
+            # reversal potential map
+            self.channelErevMap = {
+                "soma": {
+                    "jsrna": 55.0,
+                    "klt": -70,
+                    "kht": -70,
+                    "hcnobo": -38,
+                    "leak": -62.0,
+                },
+                "hillock": {
+                    "jsrna": 55.0,
+                    "klt": -70,
+                    "kht": -70,
+                    "hcnobo": -38,
+                    "leak": -62.0,
+                },
+                "unmyelinatedaxon": {
+                    "jsrna": 55.0,
+                    "klt": -70,
+                    "kht": -70,
+                    "hcnobo": -38,
+                    "leak": -62.0,
+                },
+                "primarydendrite": {
+                    "jsrna": 55.0,
+                    "klt": -70,
+                    "kht": -70,
+                    "hcnobo": -38,
+                    "leak": -62.0,
+                },
+            }
+
+        else:
+            raise ValueError("model type %s is not implemented" % modelType)
+        self.check_temperature()
+
+    def species_scaling(self, species="mouse", modelType="Spencer", silent=True):
+        """
+        Adjust all of the conductances and the cell size according to the species requested.
+        Used ONLY for point models.
+        
+        Parameters
+        ----------
+        species : string (default: 'guineapig')
+            name of the species to use for scaling the conductances in the base point model
+            Must be guineapig
+        
+        modelType: string (default: 'II-o')
+            definition of model type from RM03 models, currently limited to type II-o
+        
+        silent : boolean (default: True)
+            run silently (True) or verbosely (False)
+        """
+        soma = self.soma
+
+        if species == "mouse" and modelType == "Spencer":
+            print("Octopus: Mouse, Spencer point model - not a valid model")
+            self.set_soma_size_from_Cm(25.0)
+            self._valid_temperatures = (34.0,)
+            if self.status["temperature"] is None:
+                self.set_temperature(34.0)
+            self.print_soma_info()
+            #            self.adjust_na_chans(soma)
+            # soma().kht.gbar = 0.0061  # nstomho(150.0, self.somaarea)  # 6.1 mmho/cm2
+            # soma().klt.gbar = 0.0407  # nstomho(3196.0, self.somaarea)  #  40.7 mmho/cm2
+            # soma().hcnobo.gbar = 0.0076  #nstomho(40.0, self.somaarea)  # 7.6 mmho/cm2, cf. Bal and Oertel, Spencer et al. 25 u dia cell
+            # soma().leak.gbar = 0.0005  # nstomho(2.0, self.somaarea)
+            self.axonsf = 1.0
+        else:
+            raise ValueError(
+                'Species "%s" or species-type "%s" is not recognized for octopus cells'
+                % (species, type)
+            )
+        self.status["species"] = species
+        self.status["modelType"] = modelType
+        self.cell_initialize(showinfo=True)
+        if not silent:
+            print("set cell as: ", species)
+            print(" with Vm rest = %6.3f" % self.vm0)

cnmodel/cells/pyramidal.py

@@ -0,0 +1,482 @@
+from __future__ import print_function
+
+import numpy as np
+from neuron import h
+
+from .cell import Cell
+from .. import data
+from ..util import Params
+from ..util import nstomho
+
+__all__ = ["Pyramidal", "PyramidalKanold"]
+
+
+class Pyramidal(Cell):
+
+    type = "pyramidal"
+
+    @classmethod
+    def create(cls, model="POK", **kwds):
+        if model == "POK":
+            return PyramidalKanold(**kwds)
+        else:
+            raise ValueError("Pyramidal model %s is unknown", model)
+
+    def make_psd(self, terminal, psd_type, **kwds):
+        """
+        Connect a presynaptic terminal to one post section at the specified location, with the fraction
+        of the "standard" conductance determined by gbar.
+        The default condition is to try to pass the default unit test (loc=0.5)
+        
+        Parameters
+        ----------
+        terminal : Presynaptic terminal (NEURON object)
+        
+        psd_type : either simple or multisite PSD for bushy cell
+        
+        kwds: dict of options. Two are currently handled:
+        postsize : expect a list consisting of [sectionno, location (float)]
+        AMPAScale : float to scale the ampa currents
+        
+        """
+        if (
+            "postsite" in kwds
+        ):  # use a defined location instead of the default (soma(0.5)
+            postsite = kwds["postsite"]
+            loc = postsite[1]  # where on the section?
+            uname = (
+                "sections[%d]" % postsite[0]
+            )  # make a name to look up the neuron section object
+            post_sec = self.hr.get_section(uname)  # Tell us where to put the synapse.
+        else:
+            loc = 0.5
+            post_sec = self.soma
+
+        if psd_type == "simple":
+            if terminal.cell.type in [
+                "sgc",
+                "dstellate",
+                "tuberculoventral",
+                "cartwheel",
+            ]:
+                weight = data.get(
+                    "%s_synapse" % terminal.cell.type,
+                    species=self.species,
+                    post_type=self.type,
+                    field="weight",
+                )
+                tau1 = data.get(
+                    "%s_synapse" % terminal.cell.type,
+                    species=self.species,
+                    post_type=self.type,
+                    field="tau1",
+                )
+                tau2 = data.get(
+                    "%s_synapse" % terminal.cell.type,
+                    species=self.species,
+                    post_type=self.type,
+                    field="tau2",
+                )
+                erev = data.get(
+                    "%s_synapse" % terminal.cell.type,
+                    species=self.species,
+                    post_type=self.type,
+                    field="erev",
+                )
+                return self.make_exp2_psd(
+                    post_sec,
+                    terminal,
+                    weight=weight,
+                    loc=loc,
+                    tau1=tau1,
+                    tau2=tau2,
+                    erev=erev,
+                )
+            else:
+                raise TypeError(
+                    "Cannot make simple PSD for %s => %s"
+                    % (terminal.cell.type, self.type)
+                )
+
+        elif psd_type == "multisite":
+            if terminal.cell.type == "sgc":
+                # Max conductances for the glu mechanisms are calibrated by
+                # running `synapses/tests/test_psd.py`. The test should fail
+                # if these values are incorrect
+                self.AMPAR_gmax = (
+                    data.get(
+                        "sgc_synapse",
+                        species=self.species,
+                        post_type=self.type,
+                        field="AMPAR_gmax",
+                    )
+                    * 1e3
+                )
+                self.NMDAR_gmax = (
+                    data.get(
+                        "sgc_synapse",
+                        species=self.species,
+                        post_type=self.type,
+                        field="NMDAR_gmax",
+                    )
+                    * 1e3
+                )
+                self.Pr = data.get(
+                    "sgc_synapse", species=self.species, post_type=self.type, field="Pr"
+                )
+                # adjust gmax to correct for initial Pr
+                self.AMPAR_gmax = self.AMPAR_gmax / self.Pr
+                self.NMDAR_gmax = self.NMDAR_gmax / self.Pr
+                if "AMPAScale" in kwds:
+                    self.AMPA_gmax = (
+                        self.AMPA_gmax * kwds["AMPAScale"]
+                    )  # allow scaling of AMPA conductances
+                if "NMDAScale" in kwds:
+                    self.NMDA_gmax = self.NMDA_gmax * kwds["NMDAScale"]
+                return self.make_glu_psd(
+                    post_sec, terminal, self.AMPAR_gmax, self.NMDAR_gmax, loc=loc
+                )
+            elif terminal.cell.type == "dstellate":  # WBI input -Voigt, Nelken, Young
+                return self.make_gly_psd(post_sec, terminal, psdtype="glyfast", loc=loc)
+            elif (
+                terminal.cell.type == "tuberculoventral"
+            ):  # TV cells talk to each other-Kuo et al.
+                return self.make_gly_psd(post_sec, terminal, psdtype="glyfast", loc=loc)
+            else:
+                raise TypeError(
+                    "Cannot make PSD for %s => %s" % (terminal.cell.type, self.type)
+                )
+        else:
+            raise ValueError("Unsupported psd type %s" % psd_type)
+
+
+class PyramidalKanold(Pyramidal, Cell):
+    """
+    DCN pyramidal cell
+    Kanold and Manis, 1999, 2001, 2005
+    """
+
+    def __init__(
+        self,
+        morphology=None,
+        decorator=None,
+        nach=None,
+        ttx=False,
+        species="rat",
+        modelType=None,
+        debug=False,
+    ):
+        """
+        initialize a pyramidal cell, based on the Kanold-Manis (2001) pyramidal cell model.
+        Modifications to the cell can be made by calling methods below. These include
+        converting to a model with modified size and conductances (experimental).
+        
+        Parameters
+        ----------
+        morphology : string (default: None)
+            a file name to read the cell morphology from. If a valid file is found, a cell is constructed
+            as a cable model from the hoc file.
+            If None (default), the only a point model is made, exactly according to RM03.
+            
+        decorator : Python function (default: None)
+            decorator is a function that "decorates" the morphology with ion channels according
+            to a set of rules.
+            If None, a default set of channels is inserted into the first soma section, and the
+            rest of the structure is "bare".
+        
+        nach : string (default: None)
+            nach selects the type of sodium channel that will be used in the model. A channel mechanim
+            by that name must exist. None implies the default channel, 'napyr'.
+        
+        ttx : Boolean (default: False)
+            If ttx is True, then the sodium channel conductance is set to 0 everywhere in the cell.
+            Currently, this is not implemented.
+        
+        species: string (default 'guineapig')
+            species defines the channel density that will be inserted for different models. Note that
+            if a decorator function is specified, this argument is ignored (overridden by decorator).
+            
+        modelType: string (default: None)
+            modelType specifies the type of the model that will be used (e.g., "II", "II-I", etc).
+            modelType is passed to the decorator, or to species_scaling to adjust point models.
+            
+        debug: boolean (default: False)
+            debug is a boolean flag. When set, there will be multiple printouts of progress and parameters.
+            
+        Returns
+        -------
+            Nothing
+        
+        """
+        super(PyramidalKanold, self).__init__()
+        if modelType == None:
+            modelType = "POK"
+        if nach == None:
+            nach = "napyr"
+        self.status = {
+            "soma": True,
+            "axon": False,
+            "dendrites": False,
+            "pumps": False,
+            "na": nach,
+            "species": species,
+            "modelType": modelType,
+            "ttx": ttx,
+            "name": "Pyramidal",
+            "morphology": morphology,
+            "decorator": decorator,
+            "temperature": None,
+        }
+
+        self.i_test_range = {"pulse": (-0.3, 0.401, 0.02)}
+        self.vrange = [-75.0, -60.0]
+        if morphology is None:
+            """
+            instantiate a basic soma-only ("point") model
+            """
+            soma = h.Section(
+                name="Pyramidal_Soma_%x" % id(self)
+            )  # one compartment of about 29000 um2
+            soma.nseg = 1
+            self.add_section(soma, "soma")
+        else:
+            """
+            instantiate a structured model with the morphology as specified by 
+            the morphology file
+            """
+            self.set_morphology(morphology_file=morphology)
+
+        # decorate the morphology with ion channels
+        if decorator is None:  # basic model, only on the soma
+            self.mechanisms = [
+                "napyr",
+                "kdpyr",
+                "kif",
+                "kis",
+                "ihpyr",
+                "leak",
+                "kcnq",
+                "nap",
+            ]
+            for mech in self.mechanisms:
+                try:
+                    self.soma.insert(mech)
+                except ValueError:
+                    print("WARNING: Mechanism %s not found" % mech)
+            self.soma().kif.kif_ivh = -89.6
+            self.species_scaling(
+                silent=True, species=species, modelType=modelType
+            )  # set the default type I-c  cell parameters
+        else:  # decorate according to a defined set of rules on all cell compartments
+            self.decorate()
+        self.save_all_mechs()  # save all mechanisms inserted, location and gbar values...
+        self.get_mechs(self.soma)
+        if debug:
+            print("<< PYR: POK Pyramidal Cell created >>")
+
+    def get_cellpars(self, dataset, species="guineapig", celltype="II"):
+        cellcap = data.get(
+            dataset, species=species, cell_type=celltype, field="soma_Cap"
+        )
+        chtype = data.get(
+            dataset, species=species, cell_type=celltype, field="soma_natype"
+        )
+        pars = Params(cap=cellcap, natype=chtype)
+        for g in [
+            "soma_napyr_gbar",
+            "soma_kdpyr_gbar",
+            "soma_kif_gbar",
+            "soma_kis_gbar",
+            "soma_kcnq_gbar",
+            "soma_nap_gbar",
+            "soma_ihpyr_gbar",
+            "soma_leak_gbar",
+            "soma_e_h",
+            "soma_leak_erev",
+            "soma_e_k",
+            "soma_e_na",
+        ]:
+            pars.additem(
+                g, data.get(dataset, species=species, cell_type=celltype, field=g)
+            )
+        return pars
+
+    def species_scaling(self, species="rat", modelType="I", silent=True):
+        """
+        Adjust all of the conductances and the cell size according to the species requested.
+        Used ONLY for point models.
+        
+        Parameters
+        ----------
+        species : string (default: 'rat')
+            name of the species to use for scaling the conductances in the base point model
+            Must be 'rat'
+        
+        modelType: string (default: 'I')
+            definition of model type from Kanold and Manis, 2001
+            choices are 'I' or 'POK' (canonical model) or
+            'II', a modified model with more physiological surface area and KCNQ channels
+        
+        silent : boolean (default: True)
+            run silently (True) or verbosely (False)
+        """
+        if modelType in ["I", "POK"]:
+            celltype = "pyramidal"
+        elif modelType in ["II"]:
+            celltype = "pyramidal-II"
+        else:
+            celltype = modelType
+
+        dataset = "POK_channels"
+
+        soma = self.soma
+        if species in ["rat", "mouse"] and modelType in [
+            "I",
+            "POK",
+            "II",
+        ]:  # canonical K&M2001 model cell
+            self._valid_temperatures = (34.0,)
+            if self.status["temperature"] is None:
+                self.set_temperature(34.0)
+            pars = self.get_cellpars(dataset, species=species, celltype=celltype)
+            self.set_soma_size_from_Cm(pars.cap)
+            self.status["na"] = pars.natype
+            soma().napyr.gbar = nstomho(pars.soma_napyr_gbar, self.somaarea)
+            soma().nap.gbar = nstomho(
+                pars.soma_nap_gbar, self.somaarea
+            )  # does not exist in canonical model
+            soma().kdpyr.gbar = nstomho(pars.soma_kdpyr_gbar, self.somaarea)
+            soma().kcnq.gbar = nstomho(
+                pars.soma_kcnq_gbar, self.somaarea
+            )  # does not exist in canonical model.
+            soma().kif.gbar = nstomho(pars.soma_kif_gbar, self.somaarea)
+            soma().kis.gbar = nstomho(pars.soma_kis_gbar, self.somaarea)
+            soma().ihpyr.gbar = nstomho(pars.soma_ihpyr_gbar, self.somaarea)
+            #            soma().ihpyr_adj.q10 = 3.0  # no temp scaling to sta
+            soma().leak.gbar = nstomho(pars.soma_leak_gbar, self.somaarea)
+            soma().leak.erev = pars.soma_leak_erev
+            soma().ena = pars.soma_e_na
+            soma().ek = pars.soma_e_k
+            soma().ihpyr.eh = pars.soma_e_h
+
+        # elif species in 'rat' and modelType == 'II':
+        #     """
+        #     Modified canonical K&M2001 model cell
+        #     In this model version, the specific membrane capacitance is modified
+        #     so that the overall membrane time constant is consistent with experimental
+        #     measures in slices. However, this is not a physiological value. Attempts
+        #     to use the normal 1 uF/cm2 value were unsuccessful in establishing the expected
+        #     ~12 msec time constant.
+        #     This model also adds a KCNQ channel, as described by Li et al., 2012.
+        #     """
+        #     self.c_m = 6.0
+        #     self.set_soma_size_from_Diam(30.0)
+        #     # self.set_soma_size_from_Cm(80.0)
+        #     # print 'diameter: %7.1f' % self.soma.diam
+        #     self._valid_temperatures = (34.,)
+        #     if self.status['temperature'] is None:
+        #         self.set_temperature(34.)
+        #     self.refarea = self.somaarea
+        #     soma().napyr.gbar = nstomho(550, self.refarea)
+        #     soma().nap.gbar = nstomho(60.0, self.refarea)
+        #     soma().kcnq.gbar = nstomho(2, self.refarea)  # pyramidal cells have kcnq: Li et al, 2011 (Thanos)
+        #     soma().kdpyr.gbar = nstomho(180, self.refarea) # Normally 80.
+        #     soma().kif.gbar = nstomho(150, self.refarea) # normally 150
+        #     soma().kis.gbar = nstomho(40, self.refarea) # 40
+        #     soma().ihpyr.gbar = nstomho(2.8, self.refarea)
+        #     soma().leak.gbar = nstomho(0.5, self.refarea)
+        #     soma().leak.erev = -62.  # override default values in cell.py
+        #     soma().ena = 50.0
+        #     soma().ek = -81.5
+        #     soma().ihpyr.eh = -43
+        #     if not self.status['dendrites']:
+        #         self.add_dendrites()
+
+        else:
+            raise ValueError(
+                "Species %s or species-modelType %s is not implemented for Pyramidal cells"
+                % (species, modelType)
+            )
+
+        self.status["species"] = species
+        self.status["modelType"] = modelType
+        #        self.cell_initialize(showinfo=True)
+        self.check_temperature()
+        if not silent:
+            print("set cell as: ", species, modelType)
+            print(" with Vm rest = %f" % self.vm0)
+            print(self.status)
+            for m in self.mechanisms:
+                print("%s.gbar = %f" % (m, eval("soma().%s.gbar" % m)))
+
+    def i_currents(self, V):
+        """
+        For the steady-state case, return the total current at voltage V
+        Used to find the zero current point
+        vrange brackets the interval
+        Overrides i_currents in cells.py because we have a different set of currents
+        to compute.
+        """
+        for part in self.all_sections.keys():
+            for sec in self.all_sections[part]:
+                sec.v = V
+        h.celsius = self.status["temperature"]
+        h.finitialize()
+        self.ix = {}
+
+        if "napyr" in self.mechanisms:
+            self.ix["napyr"] = self.soma().napyr.gna * (V - self.soma().ena)
+        if "nap" in self.mechanisms:
+            self.ix["nap"] = self.soma().nap.gnap * (V - self.soma().ena)
+        if "kdpyr" in self.mechanisms:
+            self.ix["kdpyr"] = self.soma().kdpyr.gk * (V - self.soma().ek)
+        if "kif" in self.mechanisms:
+            self.ix["kif"] = self.soma().kif.gkif * (V - self.soma().ek)
+        if "kis" in self.mechanisms:
+            self.ix["kis"] = self.soma().kis.gkis * (V - self.soma().ek)
+        if "kcnq" in self.mechanisms:
+            self.ix["kcnq"] = self.soma().kcnq.gk * (V - self.soma().ek)
+        if "ihpyr" in self.mechanisms:
+            self.ix["ihpyr"] = self.soma().ihpyr.gh * (V - self.soma().ihpyr.eh)
+        if "ihpyr_adj" in self.mechanisms:
+            self.ix["ihpyr_adj"] = self.soma().ihpyr_adj.gh * (
+                V - self.soma().ihpyr_adj.eh
+            )
+        # leak
+        if "leak" in self.mechanisms:
+            self.ix["leak"] = self.soma().leak.gbar * (V - self.soma().leak.erev)
+        return np.sum([self.ix[i] for i in self.ix])
+
+    def add_dendrites(self):
+        """
+        Add simple unbranched dendrite.
+        The dendrites have some kd, kif and ih current
+        """
+        nDend = range(2)  # these will be simple, unbranced, N=4 dendrites
+        dendrites = []
+        for i in nDend:
+            dendrites.append(h.Section(cell=self.soma))
+        for i in nDend:
+            dendrites[i].connect(self.soma)
+            dendrites[i].L = 250  # length of the dendrite (not tapered)
+            dendrites[i].diam = 1
+            dendrites[i].cm = self.c_m
+            # h('dendrites[i].diam(0:1) = 2:1') # dendrite diameter, with tapering
+            dendrites[i].nseg = 21  # # segments in dendrites
+            dendrites[i].Ra = 150  # ohm.cm
+            dendrites[i].insert("napyr")
+            dendrites[i]().napyr.gbar = 0.00
+            dendrites[i].insert("kdpyr")
+            dendrites[i]().kdpyr.gbar = 0.002  # a little Ht
+            dendrites[i].insert("kif")
+            dendrites[i]().kif.gbar = 0.0001  # a little Ht
+            dendrites[i].insert("leak")  # leak
+            dendrites[i]().leak.gbar = 0.00001
+            dendrites[i].insert("ihpyr_adj")  # some H current
+            # mechanism missing so the ihvcn mechanism need to be inserted
+            dendrites[i].insert('ihvcn')
+            dendrites[i]().ihvcn.gbar = 0.0  # 0.00002
+            dendrites[i]().ihvcn.eh = -43.0
+        self.maindend = dendrites
+        self.status["dendrites"] = True
+        self.add_section(self.maindend, "maindend")

cnmodel/cells/sgc.py

@@ -0,0 +1,467 @@
+from __future__ import print_function
+from neuron import h
+from ..util import nstomho
+from ..util import Params
+import numpy as np
+from .cell import Cell
+from .. import synapses
+from .. import an_model
+from .. import data
+
+__all__ = ["SGC", "SGC_TypeI", "DummySGC"]
+
+
+class SGC(Cell):
+    type = "sgc"
+
+    @classmethod
+    def create(cls, model="I", species="mouse", **kwds):
+        if model == "dummy":
+            return DummySGC(**kwds)
+        elif model == "I":
+            return SGC_TypeI(species=species, **kwds)
+        else:
+            raise ValueError("SGC model %s is unknown", model)
+
+    def __init__(self, cf=None, sr=None):
+        Cell.__init__(self)
+        self._cf = cf
+        self._sr = sr
+        self.spike_source = None  # used by DummySGC to connect VecStim to terminal
+
+    @property
+    def cf(self):
+        """ Center frequency
+        """
+        return self._cf
+
+    @property
+    def sr(self):
+        """ Spontaneous rate group. 1=low, 2=mid, 3=high
+        """
+        return self._sr
+
+    def make_terminal(self, post_cell, term_type, **kwds):
+        """Create a StochasticTerminal and configure it according to the 
+        postsynaptic cell type.
+        """
+        pre_sec = self.soma
+
+        # Return a simple terminal unless a stochastic terminal was requested.
+        if term_type == "simple":
+            return synapses.SimpleTerminal(
+                pre_sec, post_cell, spike_source=self.spike_source, **kwds
+            )
+        elif term_type == "multisite":
+            n_rsites = data.get(
+                "sgc_synapse",
+                species="mouse",
+                post_type=post_cell.type,
+                field="n_rsites",
+            )
+            opts = {"nzones": n_rsites, "delay": 0, "dep_flag": 1}
+            opts.update(kwds)
+            # when created, depflag is set True (1) so that we compute the DKR D*F to get release
+            # this can be modified prior to the run by setting the terminal(s) so that dep_flag is 0
+            # (no DKR: constant release probability)
+            term = synapses.StochasticTerminal(
+                pre_sec, post_cell, spike_source=self.spike_source, **opts
+            )
+
+            kinetics = data.get(
+                "sgc_ampa_kinetics",
+                species="mouse",
+                post_type=post_cell.type,
+                field=["tau_g", "amp_g"],
+            )
+            term.set_params(**kinetics)
+            dynamics = data.get(
+                "sgc_release_dynamics",
+                species="mouse",
+                post_type=post_cell.type,
+                field=["F", "k0", "kmax", "kd", "kf", "taud", "tauf", "dD", "dF"],
+            )
+            term.set_params(**dynamics)
+            return term
+        else:
+            raise ValueError("Unsupported terminal type %s" % term_type)
+
+
+class DummySGC(SGC):
+    """ SGC class with no cell body; this cell only replays a predetermined
+    spike train.
+    """
+
+    def __init__(self, cf=None, sr=None, simulator=None):
+        """
+        Parameters
+        ----------
+        cf : float (default: None)
+            Required: the characteristic frequency for the SGC
+        
+        sr : int (default None)
+            required : Selects the spontaneous rate group from the
+            Zilany et al (2010) model. 1 = LSR, 2 = MSR, 3 = HSR
+        
+        simulator : 'cochlea' | 'matlab' | None (default None)
+            Sets the simulator interface that will be used. All models
+            currently use the Zilany et al. model, but the simulator can
+            be run though a Python-interface directly to the Matlab code
+            as publicy available, (simulator='matlab'), or can be run through
+            Rudnicki & Hemmert's Python interface to the simulator's C code 
+            (simulator='cochlea').
+        
+        """
+        self._simulator = simulator
+        SGC.__init__(self, cf, sr)
+        self.vecstim = h.VecStim()
+
+        # this causes the terminal to receive events from the VecStim:
+        self.spike_source = self.vecstim
+
+        # just an empty section for holding the terminal
+        self.add_section(h.Section(), "soma")
+        self.status = {
+            "soma": True,
+            "axon": False,
+            "dendrites": False,
+            "pumps": False,
+            "na": None,
+            "species": None,
+            "modelType": "dummy",
+            "ttx": False,
+            "name": "DummysGC",
+            "morphology": None,
+            "decorator": None,
+            "temperature": None,
+        }
+
+    def set_spiketrain(self, times):
+        """ Set the times of spikes (in seconds) to be replayed by the cell.
+        """
+        self._spiketrain = times
+        self._stvec = h.Vector(times)
+        self.vecstim.play(self._stvec)
+
+    def set_sound_stim(self, stim, seed, simulator=None):
+        """ Set the sound stimulus used to generate this cell's spike train.
+        """
+        self._sound_stim = stim
+        spikes = self.generate_spiketrain(stim, seed, simulator)
+        self.set_spiketrain(spikes)
+
+    def generate_spiketrain(self, stim, seed, simulator=None):
+        if simulator is None:
+            simulator = self._simulator
+        spikes = an_model.get_spiketrain(
+            cf=self.cf, sr=self.sr, seed=seed, stim=stim, simulator=simulator
+        )
+        return spikes * 1000
+
+
+class SGC_TypeI(SGC):
+    """
+    Spiral ganglion cell model
+    
+    """
+
+    def __init__(
+        self,
+        morphology=None,
+        decorator=None,
+        nach=None,
+        ttx=False,
+        species="guineapig",
+        modelType="bm",
+        cf=None,
+        sr=None,
+        debug=False,
+    ):
+        """
+        Initialize a spiral ganglion Type I cell, based on a bushy cell model.
+        Modifications to the cell can be made by calling the methods below. These include
+        converting to a model with modified size and conductances (experimental), and
+        and changing the sodium channel conductances.
+        
+        Parameters
+        ----------
+        morphology : string (default: None)
+            a file name to read the cell morphology from. If a valid file is found, a cell is constructed
+            as a cable model from the hoc file.
+            If None (default), the only a point model is made, exactly according to RM03.
+            
+        decorator : Python function (default: None)
+            decorator is a function that "decorates" the morphology with ion channels according
+            to a set of rules.
+            If None, a default set of channels aer inserted into the first soma section, and the
+            rest of the structure is "bare".
+
+        nach : string (default: 'na')
+            nach selects the type of sodium channel that will be used in the model. A channel mechanim
+            by that name must exist. The default is jsrna (Rothman et al., 1993)
+        
+        ttx : Boolean (default: False)
+            If ttx is True, then the sodium channel conductance is set to 0 everywhere in the cell.
+            Currently, this is not implemented.
+        
+        species: string (default 'guineapig')
+            species defines the channel density that will be inserted for different models. Note that
+            if a decorator function is specified, this argument is ignored.
+            
+        modelType: string (default: None)
+            modelType specifies the type of the model that will be used. SGC model know about "a" (apical)
+            and "bm" (basal-middle) models, based on Liu et al., JARO, 2014.
+            modelType is passed to the decorator, or to species_scaling to adjust point models.
+
+        cf : float (default: None)
+            The CF for the auditory nerve fiber that this SGC represents.
+
+        sr : string (default: None)
+            The spontaneous rate group to which this fiber belongs. "LS", "MS", and "HS" are known values.
+
+        debug: boolean (default: False)
+            debug is a boolean flag. When set, there will be multiple printouts of progress and parameters.
+            
+        Returns
+        -------
+            Nothing
+        
+        """
+
+        super(SGC_TypeI, self).__init__(cf=cf, sr=sr)
+        if modelType == None:
+            modelType = "bm"  # modelTypes are: a (apical), bm (basal middle)
+        if nach == None:
+            nach = "jsrna"
+        self.status = {
+            "soma": True,
+            "axon": False,
+            "dendrites": False,
+            "pumps": False,
+            "na": nach,
+            "species": species,
+            "modelType": modelType,
+            "ttx": ttx,
+            "name": "SGC",
+            "morphology": morphology,
+            "decorator": decorator,
+            "temperature": None,
+        }
+
+        self.i_test_range = {
+            "pulse": [(-0.3, 0.3, 0.02), (-0.03, 0.0, 0.005)]
+        }  # include finer range as well
+        self.vrange = [-75.0, -55.0]
+        if morphology is None:
+            """
+            instantiate a basic soma-only ("point") model
+            """
+            soma = h.Section(
+                name="SGC_Soma_%x" % id(self)
+            )  # one compartment of about 29000 um2
+            soma.nseg = 1
+            self.add_section(soma, "soma")
+        else:
+            """
+            instantiate a structured model with the morphology as specified by 
+            the morphology file
+            """
+            self.set_morphology(morphology_file=morphology)
+
+        # decorate the morphology with ion channels
+        if decorator is None:  # basic model, only on the soma
+            self.mechanisms = [nach, "klt", "kht", "leak"]
+            if modelType == "a":
+                self.mechanisms.append("ihsgcApical")
+            elif modelType == "bm":
+                self.mechanisms.append("ihsgcBasalMiddle")
+            else:
+                raise ValueError("Type %s not known for SGC model" % modelType)
+            for mech in self.mechanisms:
+                self.soma.insert(mech)
+            self.soma.ek = self.e_k
+            self.soma().leak.erev = self.e_leak
+            self.species_scaling(
+                silent=True, species=species, modelType=modelType
+            )  # set the default type II cell parameters
+        else:  # decorate according to a defined set of rules on all cell compartments
+            self.decorate()
+        self.save_all_mechs()  # save all mechanisms inserted, location and gbar values...
+        self.get_mechs(self.soma)
+        if debug:
+            print("<< SGC: Spiral Ganglion Cell created >>")
+
+    def get_cellpars(self, dataset, species="guineapig", celltype="sgc-a"):
+        cellcap = data.get(
+            dataset, species=species, cell_type=celltype, field="soma_Cap"
+        )
+        chtype = data.get(
+            dataset, species=species, cell_type=celltype, field="soma_na_type"
+        )
+        pars = Params(soma_Cap=cellcap, natype=chtype)
+        for g in [
+            "soma_na_gbar",
+            "soma_kht_gbar",
+            "soma_klt_gbar",
+            "soma_ihap_gbar",
+            "soma_ihbm_gbar",
+            "soma_ihap_eh",
+            "soma_ihbm_eh",
+            "soma_leak_gbar",
+            "soma_leak_erev",
+            "soma_e_k",
+            "soma_e_na",
+        ]:
+            pars.additem(
+                g, data.get(dataset, species=species, cell_type=celltype, field=g)
+            )
+        return pars
+
+    def species_scaling(self, silent=True, species="guineapig", modelType="a"):
+        """
+        Adjust all of the conductances and the cell size according to the species requested.
+        Used ONLY for point models.
+        
+        Parameters
+        ----------
+        species : string (default: 'guineapig')
+            name of the species to use for scaling the conductances in the base point model
+            Must be one of mouse or guineapig
+        
+        modelType: string (default: 'a')
+            definition of HCN model type from Liu et al. JARO 2014:
+            'a' for apical model
+            'bm' for basal-middle model
+        
+        silent : boolean (default: True)
+            run silently (True) or verbosely (False)
+        
+        Returns
+        -------
+        Nothing
+        
+        Notes
+        -----
+        The 'guineapig' model uses the mouse HCN channel model, verbatim. This may not
+        be appropriate, given that the other conductances are scaled up.
+        
+        """
+
+        soma = self.soma
+        if modelType == "a":
+            celltype = "sgc-a"
+        elif modelType == "bm":
+            celltype = "sgc-bm"
+        else:
+            raise ValueError("SGC: unrecognized model type %s " % modelType)
+
+        if species == "mouse":
+            self._valid_temperatures = (34.0,)
+            if self.status["temperature"] is None:
+                self.set_temperature(34.0)
+            par = self.get_cellpars(
+                "sgc_mouse_channels", species=species, celltype=celltype
+            )
+        elif species == "guineapig":
+            # guinea pig data from Rothman and Manis, 2003, modelType II
+            self._valid_temperatures = (22.0,)
+            if self.status["temperature"] is None:
+                self.set_temperature(22.0)
+            par = self.get_cellpars(
+                "sgc_guineapig_channels", species=species, celltype=celltype
+            )
+
+        self.set_soma_size_from_Cm(par.soma_Cap)
+        self.adjust_na_chans(soma, gbar=par.soma_na_gbar)
+        soma().kht.gbar = nstomho(par.soma_kht_gbar, self.somaarea)
+        soma().klt.gbar = nstomho(par.soma_klt_gbar, self.somaarea)
+        if celltype == "sgc-a":
+            soma().ihsgcApical.gbar = nstomho(par.soma_ihap_gbar, self.somaarea)
+            soma().ihsgcApical.eh = par.soma_ihap_eh
+        elif celltype == "sgc-bm":
+            soma().ihsgcBasalMiddle.gbar = nstomho(par.soma_ihbm_gbar, self.somaarea)
+            soma().ihsgcBasalMiddle.eh = par.soma_ihbm_eh
+        else:
+            raise ValueError(
+                "Ihsgc modelType %s not recognized for species %s" % (celltype, species)
+            )
+        soma().leak.gbar = nstomho(par.soma_leak_gbar, self.somaarea)
+        soma().leak.erev = par.soma_leak_erev
+
+        self.status["species"] = species
+        self.status["modelType"] = modelType
+        self.check_temperature()
+        if not silent:
+            print("set cell as: ", species)
+            print(" with Vm rest = %f" % self.vm0)
+
+    def adjust_na_chans(self, soma, gbar=1000.0, debug=False):
+        """
+        adjust the sodium channel conductance
+        :param soma: a soma object whose sodium channel complement will have it's
+        conductances adjusted depending on the channel type
+        :return nothing:
+        """
+        if self.status["ttx"]:
+            gnabar = 0.0
+        else:
+            gnabar = nstomho(gbar, self.somaarea)
+        nach = self.status["na"]
+        if nach == "jsrna":
+            soma().jsrna.gbar = gnabar
+            soma.ena = self.e_na
+            if debug:
+                print("jsrna gbar: ", soma().jsrna.gbar)
+        elif nach == "nav11":
+            soma().nav11.gbar = gnabar * 0.5
+            soma.ena = self.e_na
+            soma().nav11.vsna = 4.3
+            if debug:
+                print("sgc using inva11")
+            print("nav11 gbar: ", soma().nav11.gbar)
+        elif nach in ["na", "nacn"]:
+            soma().na.gbar = gnabar
+            soma.ena = self.e_na
+            if debug:
+                print("na gbar: ", soma().na.gbar)
+        else:
+            raise ValueError("Sodium channel %s is not recognized for SGC cells", nach)
+
+    def i_currents(self, V):
+        """
+        For the steady-state case, return the total current at voltage V
+        Used to find the zero current point
+        vrange brackets the interval
+        Implemented here are the basic RM03 mechanisms
+        This function should be replaced for specific cell types.
+        """
+        for part in self.all_sections.keys():
+            for sec in self.all_sections[part]:
+                sec.v = V
+
+        h.t = 0.0
+        h.celsius = self.status["temperature"]
+        h.finitialize()
+        self.ix = {}
+        if "na" in self.mechanisms:
+            # print dir(self.soma().na)
+            self.ix["na"] = self.soma().na.gna * (V - self.soma().ena)
+        if "jsrna" in self.mechanisms:
+            # print dir(self.soma().na)
+            self.ix["jsrna"] = self.soma().jsrna.gna * (V - self.soma().ena)
+        if "klt" in self.mechanisms:
+            self.ix["klt"] = self.soma().klt.gklt * (V - self.soma().ek)
+        if "kht" in self.mechanisms:
+            self.ix["kht"] = self.soma().kht.gkht * (V - self.soma().ek)
+        if "ihsgcApical" in self.mechanisms:
+            self.ix["ihsgcApical"] = self.soma().ihsgcApical.gh * (
+                V - self.soma().ihsgcApical.eh
+            )
+        if "ihsgcBasalMiddle" in self.mechanisms:
+            self.ix["ihsgcBasalMiddle"] = self.soma().ihsgcBasalMiddle.gh * (
+                V - self.soma().ihsgcBasalMiddle.eh
+            )
+        if "leak" in self.mechanisms:
+            self.ix["leak"] = self.soma().leak.gbar * (V - self.soma().leak.erev)
+        #        print self.status['name'], self.status['type'], V, self.ix
+        return np.sum([self.ix[i] for i in self.ix])

cnmodel/cells/tests/cell_data/SGC_rat_a.pk

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cnmodel/cells/tests/cell_data/SGC_rat_bm.pk

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cnmodel/cells/tests/cell_data/bushy-mouse-typeII.pk

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cnmodel/cells/tests/cell_data/bushy_guineapig-typeII-I.pk

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cnmodel/cells/tests/cell_data/cartwheel_rat_I.pk

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cnmodel/cells/tests/cell_data/dstellate_guineapig-typeI-II.pk

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cnmodel/cells/tests/cell_data/octopus_guineapig-typeII-o.pk

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cnmodel/cells/tests/cell_data/tstellate_guineapig-typeI-c.pk

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cnmodel/cells/tests/cell_data/tstellate_guineapig-typeI-t.pk

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cnmodel/cells/tests/cell_data/tstellate_mouse-typeI-c.pk

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cnmodel/cells/tests/cell_data/tuberculoventral_mouse_I.pk

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cnmodel/cells/tests/test_cells.py

@@ -0,0 +1,227 @@
+import os, pickle, pprint
+import numpy as np
+import neuron
+
+import cnmodel
+import cnmodel.cells as cells
+from cnmodel.util import UserTester, reset
+from cnmodel.protocols import IVCurve
+
+"""
+Cell-type tests
+"""
+
+
+def test_bushy():
+    reset(raiseError=False)
+    cell = cells.Bushy.create(species="guineapig", modelType="II")
+    CellTester("bushy_guineapig-typeII", cell)
+
+
+def test_bushy21():
+    reset(raiseError=False)
+    cell = cells.Bushy.create(species="guineapig", modelType="II-I")
+    CellTester("bushy_guineapig-typeII-I", cell)
+
+
+def test_bushy_mouse():
+    reset(raiseError=False)
+    cell = cells.Bushy.create(species="mouse", modelType="II")
+    CellTester("bushy-mouse-typeII", cell)
+
+
+def test_tstellate():
+    reset(raiseError=False)
+    cell = cells.TStellate.create(species="guineapig", modelType="I-c")
+    CellTester("tstellate_guineapig-typeI-c", cell)
+
+
+def test_tstellate_mouse():
+    reset(raiseError=False)
+    cell = cells.TStellate.create(species="mouse", modelType="I-c")
+    CellTester("tstellate_mouse-typeI-c", cell)
+
+
+def test_tstellatet():
+    reset(raiseError=False)
+    cell = cells.TStellate.create(species="guineapig", modelType="I-t")
+    CellTester("tstellate_guineapig-typeI-t", cell)
+
+
+# not implemented yet
+# def test_tstellatet_mouse():
+#     reset(raiseError=False)
+#     cell = cells.TStellate.create(species='mouse', modelType='I-t')
+#     CellTester('tstellate_mouse-typeI-t', cell)
+
+
+def test_dstellate():
+    reset(raiseError=False)
+    cell = cells.DStellate.create(species="guineapig", modelType="I-II")
+    CellTester("dstellate_guineapig-typeI-II", cell)
+
+
+def test_dstellate_mouse():
+    reset(raiseError=False)
+    cell = cells.DStellate.create(species="mouse", modelType="I-II")
+    CellTester("dstellate_mouse-typeI-II", cell)
+
+
+def test_octopus():
+    reset(raiseError=False)
+    cell = cells.Octopus.create(species="guineapig", modelType="II-o")
+    CellTester("octopus_guineapig-typeII-o", cell)
+
+
+def test_pyramidal():
+    reset(raiseError=False)
+    cell = cells.Pyramidal.create(species="rat", modelType="I")
+    CellTester("pyramidal_rat_I", cell)
+
+
+def test_tuberculoventral():
+    reset(raiseError=False)
+    cell = cells.Tuberculoventral.create(species="mouse", modelType="TVmouse")
+    CellTester("tuberculoventral_mouse_I", cell)
+
+
+def test_cartwheel():
+    reset(raiseError=False)
+    cell = cells.Cartwheel.create(species="mouse", modelType="I")
+    CellTester("cartwheel_rat_I", cell)
+
+
+def test_sgc_basal_middle():
+    reset(raiseError=False)
+    cell = cells.SGC.create(species="mouse", modelType="bm")
+    CellTester("SGC_rat_bm", cell)
+
+
+def test_sgc_apical():
+    reset(raiseError=False)
+    cell = cells.SGC.create(species="mouse", modelType="a")
+    CellTester("SGC_rat_a", cell)
+
+
+#
+# Supporting functions
+#
+
+
+class CellTester(UserTester):
+    data_dir = "cell_data"
+
+    def run_test(self, cell):
+        # run I/V test on cell
+        V0 = cell.find_i0(showinfo=True)
+        rmrintau = cell.compute_rmrintau(auto_initialize=False, vrange=None)
+        iv = IVCurve()
+        self.iv = iv
+        iv.run(cell.i_test_range, cell)
+        if self.audit:
+            iv.show(cell)
+
+        info = dict(
+            temp=iv.temp,
+            icmd=iv.current_cmd,
+            spikes=iv.spike_times(),
+            rmp=iv.rest_vm(),
+            rm_taum=iv.input_resistance_tau(),
+            vpeak=iv.peak_vm(),
+            vss=iv.steady_vm(),
+            rmrintau=rmrintau,
+        )
+        return info
+
+    def assert_test_info(self, *args, **kwds):
+        try:
+            super(CellTester, self).assert_test_info(*args, **kwds)
+        finally:
+            if hasattr(self, "iv") and hasattr(self.iv, "win"):
+                self.iv.win.hide()
+
+
+# def result_file(key):
+# """
+# Return a file name to be used for storing / retrieving test results
+# given *key*.
+# """
+# path = os.path.dirname(__file__)
+# return os.path.join(path, 'cell_data', key + '.pk')
+
+# def load_cell_info(key):
+# """
+# Load prior test results for *key*.
+# If there are no prior results, return None.
+# """
+# fn = result_file(key)
+# if os.path.isfile(fn):
+# return pickle.load(open(fn, 'rb'))
+# return None
+
+# def save_cell_info(info, key):
+# """
+# Store test results for *key*.
+# """
+# fn = result_file(key)
+# dirname = os.path.dirname(fn)
+# if not os.path.isdir(dirname):
+# os.mkdir(dirname)
+# pickle.dump(info, open(fn, 'wb'))
+
+
+# The following is superseeded by the built in unit tests.
+# def CellTester(key):
+# """
+# Test *cell* and raise exception if the results do not match prior
+# data.
+# """
+# audit = cnmodel.AUDIT_TESTS
+
+## run I/V test on cell
+# iv = IVCurve()
+# iv.run(cell.i_test_range, cell)
+# iv.show(cell)
+
+# try:
+# info = dict(
+# icmd=iv.current_cmd,
+# spikes=iv.spike_times(),
+# rmp=iv.rest_vm(),
+# rm=iv.input_resistance(),
+# vpeak=iv.peak_vm(),
+# vss=iv.steady_vm(),
+# )
+
+# expect = load_cell_info(key)
+
+# if expect is not None:
+
+## Check test structures are the same
+# assert len(info) == len(expect)
+# for k in info:
+# assert k in expect
+
+## Check data matches
+# for k in info:
+# if isinstance(info[k], list):
+# assert len(info[k]) == len(expect[k])
+# for i in range(len(info[k])):
+# assert np.allclose(info[k][i], expect[k][i])
+# else:
+# assert np.allclose(info[k], expect[k])
+# else:
+# if not audit:
+# raise Exception("No prior test results for cell type '%s'. "
+# "Run test.py --audit store new test data." % key)
+
+# print "\n=== New test results for %s: ===\n" % key
+# pprint.pprint(info)
+# print "Store new test results? [y/n]",
+# yn = raw_input()
+# if yn.lower().startswith('y'):
+# save_cell_info(info, key)
+# else:
+# raise Exception("Rejected test results for '%s'" % key)
+# finally:
+# iv.win.hide()

cnmodel/cells/tuberculoventral.py

@@ -0,0 +1,619 @@
+from __future__ import print_function
+from neuron import h
+import numpy as np
+
+# import neuron as nrn
+
+from .cell import Cell
+from .. import synapses
+from ..util import nstomho
+from ..util import Params
+from .. import data
+
+__all__ = ["Tuberculoventral"]
+
+
+class Tuberculoventral(Cell):
+
+    type = "tuberculoventral"
+
+    @classmethod
+    def create(cls, model="TVmouse", **kwds):
+        if model in ["TVmouse", "I"]:
+            return Tuberculoventral(**kwds)
+        elif model == "dummy":
+            return DummyTuberculoventral(**kwds)
+        else:
+            raise ValueError("Tuberculoventral type %s is unknown", model)
+
+    def __init__(self):
+        Cell.__init__(self)
+        self.spike_source = (
+            None
+        )  # used by DummyTuberculoventral to connect VecStim to terminal
+
+    def make_psd(self, terminal, psd_type, **kwds):
+        """
+        Connect a presynaptic terminal to one post section at the specified location, with the fraction
+        of the "standard" conductance determined by gbar.
+        The default condition is to try to pass the default unit test (loc=0.5)
+        
+        Parameters
+        ----------
+        terminal : Presynaptic terminal (NEURON object)
+        
+        psd_type : either simple or multisite PSD for bushy cell
+        
+        kwds: dict of options. Two are currently handled:
+        postsize : expect a list consisting of [sectionno, location (float)]
+        AMPAScale : float to scale the ampa currents
+        
+        """
+        if (
+            "postsite" in kwds
+        ):  # use a defined location instead of the default (soma(0.5)
+            postsite = kwds["postsite"]
+            loc = postsite[1]  # where on the section?
+            uname = (
+                "sections[%d]" % postsite[0]
+            )  # make a name to look up the neuron section object
+            post_sec = self.hr.get_section(uname)  # Tell us where to put the synapse.
+        else:
+            loc = 0.5
+            post_sec = self.soma
+
+        if psd_type == "simple":
+            if terminal.cell.type in ["sgc", "dstellate", "tuberculoventral"]:
+                weight = data.get(
+                    "%s_synapse" % terminal.cell.type,
+                    species=self.species,
+                    post_type=self.type,
+                    field="weight",
+                )
+                tau1 = data.get(
+                    "%s_synapse" % terminal.cell.type,
+                    species=self.species,
+                    post_type=self.type,
+                    field="tau1",
+                )
+                tau2 = data.get(
+                    "%s_synapse" % terminal.cell.type,
+                    species=self.species,
+                    post_type=self.type,
+                    field="tau2",
+                )
+                erev = data.get(
+                    "%s_synapse" % terminal.cell.type,
+                    species=self.species,
+                    post_type=self.type,
+                    field="erev",
+                )
+                return self.make_exp2_psd(
+                    post_sec,
+                    terminal,
+                    weight=weight,
+                    loc=loc,
+                    tau1=tau1,
+                    tau2=tau2,
+                    erev=erev,
+                )
+            else:
+                raise TypeError(
+                    "Cannot make simple PSD for %s => %s"
+                    % (terminal.cell.type, self.type)
+                )
+
+        elif psd_type == "multisite":
+            if terminal.cell.type == "sgc":
+                # Max conductances for the glu mechanisms are calibrated by
+                # running `synapses/tests/test_psd.py`. The test should fail
+                # if these values are incorrect
+                self.AMPAR_gmax = (
+                    data.get(
+                        "sgc_synapse",
+                        species=self.species,
+                        post_type=self.type,
+                        field="AMPAR_gmax",
+                    )
+                    * 1e3
+                )
+                self.NMDAR_gmax = (
+                    data.get(
+                        "sgc_synapse",
+                        species=self.species,
+                        post_type=self.type,
+                        field="NMDAR_gmax",
+                    )
+                    * 1e3
+                )
+                self.Pr = data.get(
+                    "sgc_synapse", species=self.species, post_type=self.type, field="Pr"
+                )
+                # adjust gmax to correct for initial Pr
+                self.AMPAR_gmax = self.AMPAR_gmax / self.Pr
+                self.NMDAR_gmax = self.NMDAR_gmax / self.Pr
+                if "AMPAScale" in kwds:
+                    self.AMPA_gmax = (
+                        self.AMPA_gmax * kwds["AMPAScale"]
+                    )  # allow scaling of AMPA conductances
+                if "NMDAScale" in kwds:
+                    self.NMDA_gmax = self.NMDA_gmax * kwds["NMDAScale"]
+                return self.make_glu_psd(
+                    post_sec, terminal, self.AMPAR_gmax, self.NMDAR_gmax, loc=loc
+                )
+            elif terminal.cell.type == "dstellate":  # WBI input -Voigt, Nelken, Young
+                return self.make_gly_psd(post_sec, terminal, psdtype="glyfast", loc=loc)
+            elif (
+                terminal.cell.type == "tuberculoventral"
+            ):  # TV cells talk to each other-Kuo et al.
+                return self.make_gly_psd(post_sec, terminal, psdtype="glyfast", loc=loc)
+            else:
+                raise TypeError(
+                    "Cannot make PSD for %s => %s" % (terminal.cell.type, self.type)
+                )
+        else:
+            raise ValueError("Unsupported psd type %s" % psd_type)
+
+    def make_terminal(self, post_cell, term_type, **kwds):
+        pre_sec = self.soma
+        if term_type == "simple":
+            return synapses.SimpleTerminal(
+                pre_sec, post_cell, spike_source=self.spike_source, **kwds
+            )
+        elif term_type == "multisite":
+            if post_cell.type in [
+                "dstellate",
+                "tuberculoventral",
+                "pyramidal",
+                "bushy",
+                "tstellate",
+            ]:
+                nzones = data.get(
+                    "tuberculoventral_synapse",
+                    species=self.species,
+                    post_type=post_cell.type,
+                    field="n_rsites",
+                )
+                delay = data.get(
+                    "tuberculoventral_synapse",
+                    species=self.species,
+                    post_type=post_cell.type,
+                    field="delay",
+                )
+            else:
+                raise NotImplementedError(
+                    "No knowledge as to how to connect tuberculoventral cell to cell type %s"
+                    % type(post_cell)
+                )
+            pre_sec = self.soma
+            return synapses.StochasticTerminal(
+                pre_sec,
+                post_cell,
+                nzones=nzones,
+                spike_source=self.spike_source,
+                delay=delay,
+                **kwds
+            )
+        else:
+            raise ValueError("Unsupported terminal type %s" % term_type)
+
+
+class Tuberculoventral(Tuberculoventral):
+    """
+    Tuberculoventral Neuron (DCN) base model
+    Adapted from T-stellate model, using target parameters from Kuo et al. J. Neurophys. 2012
+    """
+
+    def __init__(
+        self,
+        morphology=None,
+        decorator=None,
+        nach=None,
+        ttx=False,
+        species="mouse",
+        modelType=None,
+        debug=False,
+    ):
+        """
+        Initialize a DCN Tuberculoventral cell, using the default parameters for guinea pig from
+        R&M2003, as a type I cell.
+        Modifications to the cell can be made by calling methods below. These include:
+        Converting to a type IA model (add transient K current) (species: guineapig-TypeIA).
+        Changing "species" to mouse or cat (scales conductances)
+        
+        Parameters
+        ----------
+        morphology : string (default: None)
+            a file name to read the cell morphology from. If a valid file is found, a cell is constructed
+            as a cable model from the hoc file.
+            If None (default), the only a point model is made, exactly according to RM03.
+        
+        decorator : Python function (default: None)
+            decorator is a function that "decorates" the morphology with ion channels according
+            to a set of rules.
+            If None, a default set of channels aer inserted into the first soma section, and the
+            rest of the structure is "bare".
+    
+        nach : string (default: 'na')
+            nach selects the type of sodium channel that will be used in the model. A channel mechanims
+            by that name must exist. 
+    
+        ttx : Boolean (default: False)
+            If ttx is True, then the sodium channel conductance is set to 0 everywhere in the cell.
+            Currently, this is not implemented.
+    
+        species: string (default 'guineapig')
+            species defines the channel density that will be inserted for different models. Note that
+            if a decorator function is specified, this argument is ignored.
+        
+        modelType: string (default: None)
+            modelType specifies the type of the model that will be used (e.g., "II", "II-I", etc).
+            modelType is passed to the decorator, or to species_scaling to adjust point models.
+        
+        debug: boolean (default: False)
+            debug is a boolean flag. When set, there will be multiple printouts of progress and parameters.
+        
+        Returns
+        -------
+        Nothing
+        """
+        super(Tuberculoventral, self).__init__()
+        if modelType == None:
+            modelType = "TVmouse"
+        if nach == None:
+            nach = "nacncoop"
+        self.debug = debug
+        self.status = {
+            "soma": True,
+            "axon": False,
+            "dendrites": False,
+            "pumps": False,
+            "na": nach,
+            "species": species,
+            "modelType": modelType,
+            "ttx": ttx,
+            "name": "Tuberculoventral",
+            "morphology": morphology,
+            "decorator": decorator,
+            "temperature": None,
+        }
+
+        self.i_test_range = {"pulse": [(-0.35, 1.0, 0.05), (-0.04, 0.01, 0.01)]}
+        self.vrange = [-80.0, -60.0]  # set a default vrange for searching for rmp
+
+        if morphology is None:
+            """
+            instantiate a basic soma-only ("point") model
+            """
+            if self.debug:
+                print("<< Tuberculoventral model: Creating point cell >>")
+            soma = h.Section(
+                name="Tuberculoventral_Soma_%x" % id(self)
+            )  # one compartment of about 29000 um2
+            soma.nseg = 1
+            self.add_section(soma, "soma")
+        else:
+            """
+            instantiate a structured model with the morphology as specified by 
+            the morphology file
+            """
+            if self.debug:
+                print("<< Tuberculoventral model: Creating structured cell >>")
+            self.set_morphology(morphology_file=morphology)
+
+        # decorate the morphology with ion channels
+        if decorator is None:  # basic model, only on the soma
+            self.mechanisms = ["kht", "ka", "ihvcn", "leak", nach]
+            for mech in self.mechanisms:
+                self.soma.insert(mech)
+            self.species_scaling(
+                silent=True, species=species, modelType=modelType
+            )  # adjust the default parameters
+        else:  # decorate according to a defined set of rules on all cell compartments
+            self.decorate()
+        self.save_all_mechs()  # save all mechanisms inserted, location and gbar values...
+        self.get_mechs(self.soma)
+        if self.debug:
+            print("<< Tuberculoventral cell model created >>")
+
+    def get_cellpars(self, dataset, species="mouse", celltype="TVmouse"):
+        cellcap = data.get(
+            dataset, species=species, cell_type=celltype, field="soma_Cap"
+        )
+        chtype = data.get(
+            dataset, species=species, cell_type=celltype, field="soma_na_type"
+        )
+        pars = Params(soma_cap=cellcap, soma_na_type=chtype)
+        for g in [
+            "soma_nacncoop_gbar",
+            "soma_kht_gbar",
+            "soma_ka_gbar",
+            "soma_ihvcn_gbar",
+            "soma_ihvcn_eh",
+            "soma_leak_gbar",
+            "soma_leak_erev",
+            "soma_e_k",
+            "soma_e_na",
+        ]:
+            pars.additem(
+                g, data.get(dataset, species=species, cell_type=celltype, field=g)
+            )
+        return pars
+
+    def species_scaling(self, species="guineapig", modelType="TVmouse", silent=True):
+        """
+        Adjust all of the conductances and the cell size according to the species requested.
+        Used ONLY for point models.
+        
+        Parameters
+        ----------
+        species : string (default: 'guineapig')
+            name of the species to use for scaling the conductances in the base point model
+            Must be one of mouse, cat, guineapig
+        
+        modelType: string (default: 'I-c')
+            definition of model type from RM03 models, type I-c or type I-t
+        
+        silent : boolean (default: True)
+            run silently (True) or verbosely (False)
+        """
+        soma = self.soma
+        if self.debug:
+            print("modelType: ", modelType)
+        if modelType in ["TVmouse", "I"]:
+            celltype = "TVmouse"  # modelType
+            modelType = "TVmouse"
+        else:
+            raise ValueError(
+                "Tuberuloventral: Model type %s not recognized" % modelType
+            )
+
+        if species == "mouse" and modelType in ["TVmouse", "I"]:
+            """#From Kuo 150 Mohm, 10 msec tau
+            Firing at 600 pA about 400 Hz
+            These values from brute_force runs, getting 380 Hz at 600 pA at 35C
+            Input resistance and vm is ok, time constnat is short
+                *** Rin:       168  tau:       7.8   v:  -68.4
+            Attempts to get longer time constant - cannot keep rate up.
+            """
+            # Adapted from TStellate model type I-c'
+            self.vrange = [-80.0, -58.0]
+            self._valid_temperatures = (34.0,)
+            if self.status["temperature"] is None:
+                self.set_temperature(34.0)
+
+            pars = self.get_cellpars("TV_channels", species="mouse", celltype=modelType)
+            self.set_soma_size_from_Cm(pars.soma_cap)
+            self.status["na"] = pars.soma_na_type
+            self.adjust_na_chans(soma, gbar=pars.soma_nacncoop_gbar, debug=self.debug)
+            soma().kht.gbar = nstomho(pars.soma_kht_gbar, self.somaarea)
+            soma().ka.gbar = nstomho(pars.soma_ka_gbar, self.somaarea)
+            soma().ihvcn.gbar = nstomho(pars.soma_ihvcn_gbar, self.somaarea)
+            soma().ihvcn.eh = pars.soma_ihvcn_eh
+            soma().leak.gbar = nstomho(pars.soma_leak_gbar, self.somaarea)
+            soma().leak.erev = pars.soma_leak_erev
+            self.e_leak = pars.soma_leak_erev
+            self.soma.ek = self.e_k = pars.soma_e_k
+            self.soma.ena = self.e_na = pars.soma_e_na
+
+            self.axonsf = 0.5
+        else:
+            raise ValueError(
+                "Species %s or species-type %s is not recognized for Tuberculoventralcells"
+                % (species, type)
+            )
+
+        self.status["species"] = species
+        self.status["modelType"] = modelType
+        self.check_temperature()
+
+    def channel_manager(self, modelType="TVmouse"):
+        """
+        This routine defines channel density maps and distance map patterns
+        for each type of compartment in the cell. The maps
+        are used by the ChannelDecorator class (specifically, it's private
+        _biophys function) to decorate the cell membrane.
+        
+        Parameters
+        ----------
+        modelType : string (default: 'RM03')
+            A string that defines the type of the model. Currently, 3 types are implemented:
+            RM03: Rothman and Manis, 2003 somatic densities for guinea pig
+            XM13: Xie and Manis, 2013, somatic densities for mouse
+            XM13PasDend: XM13, but with only passive dendrites, no channels.
+        
+        Returns
+        -------
+        Nothing
+        
+        Notes
+        -----
+        
+        This routine defines the following variables for the class:
+            
+            - conductances (gBar)
+            - a channelMap (dictonary of channel densities in defined anatomical compartments)
+            - a current injection range for IV's (when testing)
+            - a distance map, which defines how selected conductances in selected compartments
+                will change with distance. This includes both linear and exponential gradients,
+                the minimum conductance at the end of the gradient, and the space constant or
+                slope for the gradient.
+        
+        """
+        if modelType == "TVmouse":
+            print("decorate as tvmouse")
+            #            totcap = 95.0E-12  # Tuberculoventral cell (type I), based on stellate, adjusted for Kuo et al. TV firing
+            self.set_soma_size_from_Section(self.soma)
+            totcap = self.totcap
+            refarea = self.somaarea  # totcap / self.c_m  # see above for units
+            self.gBar = Params(
+                nabar=1520.0e-9 / refarea,
+                khtbar=160.0e-9 / refarea,
+                kltbar=0.0e-9 / refarea,
+                kabar=65.0 / refarea,
+                ihbar=1.25e-9 / refarea,
+                leakbar=5.5e-9 / refarea,
+            )
+            self.channelMap = {
+                "axon": {
+                    "nacn": 0.0,
+                    "klt": 0.0,
+                    "kht": self.gBar.khtbar,
+                    "ihvcn": 0.0,
+                    "leak": self.gBar.leakbar / 4.0,
+                },
+                "hillock": {
+                    "nacn": self.gBar.nabar,
+                    "klt": 0.0,
+                    "kht": self.gBar.khtbar,
+                    "ihvcn": 0.0,
+                    "leak": self.gBar.leakbar,
+                },
+                "initseg": {
+                    "nacn": self.gBar.nabar,
+                    "klt": 0.0,
+                    "kht": self.gBar.khtbar,
+                    "ihvcn": self.gBar.ihbar / 2.0,
+                    "leak": self.gBar.leakbar,
+                },
+                "soma": {
+                    "nacn": self.gBar.nabar,
+                    "klt": self.gBar.kltbar,
+                    "kht": self.gBar.khtbar,
+                    "ihvcn": self.gBar.ihbar,
+                    "leak": self.gBar.leakbar,
+                },
+                "dend": {
+                    "nacn": self.gBar.nabar / 2.0,
+                    "klt": 0.0,
+                    "kht": self.gBar.khtbar * 0.5,
+                    "ihvcn": self.gBar.ihbar / 3.0,
+                    "leak": self.gBar.leakbar * 0.5,
+                },
+                "apic": {
+                    "nacn": 0.0,
+                    "klt": 0.0,
+                    "kht": self.gBar.khtbar * 0.2,
+                    "ihvcn": self.gBar.ihbar / 4.0,
+                    "leak": self.gBar.leakbar * 0.2,
+                },
+            }
+            self.irange = np.linspace(-0.3, 0.6, 10)
+            self.distMap = {
+                "dend": {
+                    "klt": {"gradient": "linear", "gminf": 0.0, "lambda": 100.0},
+                    "kht": {"gradient": "linear", "gminf": 0.0, "lambda": 100.0},
+                },  # linear with distance, gminf (factor) is multiplied by gbar
+                "apic": {
+                    "klt": {"gradient": "linear", "gminf": 0.0, "lambda": 100.0},
+                    "kht": {"gradient": "linear", "gminf": 0.0, "lambda": 100.0},
+                },  # gradients are: flat, linear, exponential
+            }
+        else:
+            raise ValueError("model type %s is not implemented" % modelType)
+
+    def adjust_na_chans(self, soma, gbar=1000.0, debug=False):
+        """
+        Adjust the sodium channel conductance, depending on the type of conductance
+        
+        Parameters
+        ----------
+        soma : NEURON section object (required)
+            This identifies the soma object whose sodium channel complement will have it's
+            conductances adjusted depending on the sodium channel type
+        gbar : float (default: 1000.)
+            The "maximal" conductance to be set in the model.
+        debug : boolean (default: False)
+            A flag the prints out messages to confirm the operations applied.
+            
+        Returns
+        -------
+        Nothing
+        """
+        if self.status["ttx"]:
+            gnabar = 0.0
+        else:
+            gnabar = nstomho(gbar, self.somaarea)
+        nach = self.status["na"]
+        if nach == "nacncoop":
+            soma().nacncoop.gbar = gnabar
+            soma().nacncoop.KJ = 2000.0
+            soma().nacncoop.p = 0.25
+            soma.ena = self.e_na
+            if debug:
+                print("nacncoop gbar: ", soma().nacncoop.gbar)
+        elif nach == "jsrna":
+            soma().jsrna.gbar = gnabar
+            soma.ena = self.e_na
+            if debug:
+                print("jsrna gbar: ", soma().jsrna.gbar)
+        elif nach == "nav11":
+            soma().nav11.gbar = gnabar * 0.5
+            soma.ena = self.e_na
+            soma().nav11.vsna = 4.3
+            if debug:
+                print("Tuberculoventral using inva11")
+            print("nav11 gbar: ", soma().nav11.gbar)
+        elif nach == "na":
+            soma().na.gbar = gnabar
+            soma.ena = self.e_na
+            if debug:
+                print("na gbar: ", soma().na.gbar)
+        elif nach == "nacn":
+            soma().nacn.gbar = gnabar
+            soma.ena = self.e_na
+            if debug:
+                print("nacn gbar: ", soma().nacn.gbar)
+        else:
+            raise ValueError(
+                "Tuberculoventral setting Na channels: channel %s not known" % nach
+            )
+
+
+class DummyTuberculoventral(Tuberculoventral):
+    """ Tuberculoventral cell class with no cell body; this cell only replays a predetermined
+    spike train. Useful for testing, or replacing spike trains to determine
+    the importance of spike structures within a network.
+    """
+
+    def __init__(self, cf=None, species="mouse"):
+        """
+        Parameters
+        ----------
+        cf : float (default: None)
+            Required: the characteristic frequency for the TV cell
+            Really just for reference.
+
+        """
+
+        Tuberculoventral.__init__(self)
+        self.vecstim = h.VecStim()
+
+        # this causes the terminal to receive events from the VecStim:
+        self.spike_source = self.vecstim
+
+        # just an empty section for holding the terminal
+        self.add_section(h.Section(), "soma")
+        self.status = {
+            "soma": True,
+            "axon": False,
+            "dendrites": False,
+            "pumps": False,
+            "na": None,
+            "species": species,
+            "modelType": "Dummy",
+            "modelName": "DummyTuberculoventral",
+            "ttx": None,
+            "name": "DummyTuberculoventral",
+            "morphology": None,
+            "decorator": None,
+            "temperature": None,
+        }
+        print("<< Tuberculoventral: Dummy Tuberculoventral Cell created >>")
+
+    def set_spiketrain(self, times):
+        """ Set the times of spikes (in seconds) to be replayed by the cell.
+        """
+        self._spiketrain = times
+        self._stvec = h.Vector(times)
+        self.vecstim.play(self._stvec)

cnmodel/custom_init.hoc

@@ -0,0 +1,23 @@
+INITDUR = 100 // # ms to reach steady state
+DTSTEP = 0.1
+proc init() { local temp
+  //print "Using Custom Init"
+  finitialize(v_init)
+  t = -2*INITDUR // jump to a time "before" 0
+  temp = cvode.active()
+  if (temp != 0) { // if cvode is on, turn it off
+    cvode.active(0)
+    dt = DTSTEP
+  }
+  while (t < -INITDUR) {
+    fadvance()
+  }
+  if (temp != 0) { cvode.active(1) } // turn cvode back on if necessary
+  t = 0
+  if (cvode.active()) {
+    cvode.re_init()
+  } else {
+    fcurrent()
+  }
+  frecord_init()
+}

cnmodel/data/__init__.py

@@ -0,0 +1,18 @@
+"""
+The cnmodel.data package contains information about ion channel densities, 
+connectivity, synaptic properties, and population distributions. These values
+are used by the Cell, Synapse, Population, and related classes to determine
+all model construction parameters.
+
+Values are stored in python strings that contain human-readable tables with
+provenance documentation.
+"""
+
+
+from ._db import get, get_source, add_table_data, report_changes, setval
+
+
+from . import connectivity
+from . import synapses
+from . import populations
+from . import ionchannels

cnmodel/data/_db.py

@@ -0,0 +1,321 @@
+# -*- encoding: utf-8 -*-
+from __future__ import print_function
+from collections import OrderedDict
+import re
+
+
+# Unified collection point for all empirically-determined biophysical
+# values. Each value is a tuple (val, source).
+DATA = OrderedDict()
+
+
+def get(*args, **kwds):
+    """ Get a single value from the database using the supplied arguments
+    to query. 
+    
+    Optionally, one keyword argument may be a list of values, in which case
+    a dict will be returned containing {listval: dbval} pairs for each value in
+    the list.
+    """
+    return _lookup(0, *args, **kwds)
+
+
+def get_source(*args, **kwds):
+    """ Get the source of a single value from the database using the supplied 
+    arguments to query.
+    
+    Optionally, one keyword argument may be a list of values, in which case
+    a dict will be returned containing {listval: dbval} pairs for each value in
+    the list.
+    """
+    return _lookup(1, *args, **kwds)
+
+
+def print_table(table):
+    for k in DATA.keys():
+        if table == k[0]:
+            print("data key: ", k)
+            print(DATA[k][0])
+
+
+def get_table_info(table):
+    """
+    Return a dictionary of row and column names in the table
+    """
+    tinfo = {}
+    for k in DATA.keys():
+        if table == k[0]:
+            for p in k:
+                if not isinstance(p, tuple):
+                    continue
+                if p[0] not in tinfo.keys():
+                    tinfo[p[0]] = []
+                if p[1] not in tinfo[p[0]]:
+                    tinfo[p[0]].append(p[1])
+    return tinfo
+
+
+def _lookup(ind, *args, **kwds):
+    key = mk_key(*args, **kwds)
+    if isinstance(key, dict):
+        data = {}
+        for k, key in key.items():
+            data[k] = DATA[key][ind]
+        return data
+    else:
+        return DATA[key][ind]
+
+
+def setval(val, *args, **kwds):
+    key = mk_key(*args, **kwds)
+    oldval = None
+    # change_flag = False
+    if key in DATA:
+        #        change_flag = True  # any attempt to change key will set this
+        oldval = DATA[key]  # save the previous stored value
+    #     raise RuntimeError("Data key '%s' has already been set." % str(key))
+    DATA[key] = val
+    return oldval
+
+
+def mk_key(*args, **kwds):
+    # Make a unique key (or list of keys) used to access values from the
+    # database. The generated key is independent of the order that arguments
+    # are specified.
+    #
+    # Optionally, one keyword argument may have a list of values, in which case
+    # the function will return a dict containing {listval: key} pairs for each
+    # value in the list.
+    listkey = None
+    for k, v in kwds.items():
+        if isinstance(v, (list, tuple)):
+            if listkey is not None:
+                raise TypeError("May only specify a list of values for one key.")
+            listkey = k
+
+    if listkey is None:
+        return _mk_key(*args, **kwds)
+    else:
+        keys = {}
+        for v in kwds[listkey]:
+            kwds[listkey] = v
+            keys[v] = _mk_key(*args, **kwds)
+        return keys
+
+
+def _mk_key(*args, **kwds):
+    key = list(args) + list(kwds.items())
+    key.sort(key=lambda a: a[0] if isinstance(a, tuple) else a)
+    return tuple(key)
+
+
+def add_table_data(name, row_key, col_key, data, **kwds):
+    """
+    Read data like::
+    
+        Description
+        
+        ------------------------------------
+                col1      col2         col3
+        row1    1.2  [1]  0.9e-6 [1]   27 [2]
+        row2    1.7  [1]               [3]
+        row3    0.93 [2]  0.3e-6       3 [2]
+        
+        ------------------------------------
+        
+        [1] citation 1
+        [2] citation 2
+        [3] missing because.
+    
+    
+    """
+    if isinstance(data, str) and "\xc2" in data:
+        raise TypeError(
+            "Data table <%s> appears to contain unicode characters but"
+            "was not defined as unicode." % name
+        )
+
+    lines = data.split("\n")
+
+    # First, split into description, table, and sources using ----- lines
+    desc = []
+    table = []
+    while lines:
+        line = lines.pop(0)
+        # print ">", line
+        if re.match(r"\s*-+\s*$", line):
+            # print "match!"
+            break
+        desc.append(line)
+    while lines:
+        line = lines.pop(0)
+        # print ">", line
+        if re.match(r"\s*-+\s*$", line):
+            # print "match!"
+            break
+        table.append(line)
+
+    # print desc
+    # print table
+
+    # parse remaining lines as sources
+    sources = parse_sources(lines)
+    # print sources
+
+    #
+    # parse table
+    # table might be empty, so take care of that first.
+    if table == []:
+        return []  # no changes
+
+    while len(table[0].strip()) == 0:
+        table.pop(0)
+
+    spaces = [c == " " for c in table[0]]
+    cols = [0] + [i for i in range(1, len(spaces)) if spaces[i - 1] and not spaces[i]]
+    cols = cols + [max(map(len, table)) + 1]
+    # print spaces
+    # print cols
+    # Make sure columns are obeyed strictly
+    for i, line in enumerate(table):
+        for j, c in enumerate(cols[1:]):
+            if len(line) < c:
+                continue
+            if line[c - 1] != " ":
+                print("Table line with error: \n    ", line)
+                raise Exception(
+                    "Table <%s> line: %d, column: %s does not obey column boundaries."
+                    % (name, i, j)
+                )
+
+    # Break table into cells
+    cells = []
+    for line in table:
+        if line.strip() != "":
+            cells.append(
+                [line[cols[i] : cols[i + 1]].strip() for i in range(len(cols) - 1)]
+            )
+    # print cells
+
+    # Extract row/column names
+    col_names = cells.pop(0)[1:]
+    row_names = [cells[i].pop(0) for i in range(len(cells))]
+    if len(set(row_names)) != len(row_names):
+        for n in set(row_names):
+            row_names.remove(n)
+        raise NameError("Duplicate row names: %s" % row_names)
+
+    # Parse cell values
+    for i in range(len(cells)):
+        for j in range(len(cells[0])):
+            cell = cells[i][j].strip()
+            m = re.match(r"([^\[]*)(\[([^\]]+)\])?", cell)  # match like "0.7 [3]"
+            if m is None:
+                raise ValueError(
+                    "Table cell (%d, %d) has bad format: '%s'" % (i, j, cell)
+                )
+
+            # parse value
+            # If the value contains '±' then a tuple is returned containing the values
+            # on either side.
+            val, _, source = m.groups()
+            # val = unicode(val)  # python 2
+            val = str(val)  # python 3
+            if val.strip() == "":
+                val = None
+            else:
+                parts = val.split(u"±")
+                vals = []
+                for p in parts:
+                    try:
+                        p = int(p)
+                    except ValueError:
+                        try:
+                            p = float(p)
+                        except ValueError:
+                            try:
+                                p = str(
+                                    p.strip()
+                                )  # allow strings to identify mechanisms also
+                            except ValueError:
+                                raise ValueError(
+                                    "Table cell (%d, %d) value has bad format: '%s'"
+                                    % (i, j, val)
+                                )
+                    vals.append(p)
+                if len(vals) == 1:
+                    val = vals[0]
+                else:
+                    val = tuple(vals)
+
+            # parse source
+            if source is not None:
+                try:
+                    source = sources[source]
+                except KeyError:
+                    raise ValueError(
+                        "Table cell (%d, %d) has unknown source key: '%s'"
+                        % (i, j, source)
+                    )
+
+            cells[i][j] = (val, source)
+
+    changes = []  # a list of parameters that are changed if we are rewriting a table
+    for i, row in enumerate(row_names):
+        for j, col in enumerate(col_names):
+            kwds[row_key] = row
+            kwds[col_key] = col
+            oldval = setval(cells[i][j], name, **kwds)
+            if oldval is not None and oldval != cells[i][j]:
+                key = mk_key(name, **kwds)
+                changes.append(
+                    {"key": key, "new": cells[i][j], "old": oldval, "name": name}
+                )
+                # changes.append({'name': name, 'row': row, 'col': col, 'new': cells[i][j], 'old': oldval})
+    return changes
+
+
+def report_changes(changes):
+    """
+    For changes to data tables, give user a readout
+    """
+    if len(changes) > 0:
+        anychg = False
+        for ch in changes:
+            # print('  >>> Changing %s, %s from default (%s) to %s' % (ch['row'], ch['col'], str(ch['new'][0]), str(ch['old'][0])))
+            if str(ch["old"][0]) != str(ch["new"][0]):
+                if anychg is False:
+                    print(
+                        "\nWarning: Data Table '%s' (in memory) has been modified!"
+                        % changes[0]["name"]
+                    )
+                    anychg = True
+                print(
+                    "  >>> Changing %s, from default (%s) to %s"
+                    % (ch["key"], str(ch["old"][0]), str(ch["new"][0]))
+                )
+
+
+def parse_sources(lines):
+    sources = {}
+    key = None
+    val = []
+    for l in lines:
+        l = l.lstrip()
+        m = re.match(r"\s*\[([^\]]+)\]\s+(.*)$", l)
+        if m is not None:
+            key = m.groups()[0]
+            sources[key] = m.groups()[1].strip()
+        else:
+            if key is None:
+                if l == "":
+                    continue
+                raise ValueError(
+                    "Incorrect sources format--got text without "
+                    'citation index: "%s".' % l
+                )
+            sources[key] += "\n" + l
+    return sources
+
+
+# parse_sources('''\n\n[1] source 1\n    it's cool.\n[2] source 2 is not\n'''.split('\n'))

cnmodel/data/connectivity.py

@@ -0,0 +1,234 @@
+# -*- encoding: utf-8 -*-
+from ._db import add_table_data
+
+#: Mouse synaptic convregence table
+mouse_convergence = u"""
+
+Convergence defines the average number of presynaptic cells of a particular
+type (rows) that synapse onto a single postsynaptic cell of a particular
+type (columns).
+This connectivity matrix is currently incomplete.
+Note: Bushy and pyramidal cells are known to have no (or very few)
+collaterals within the CN, and so they are not listed as presynaptic cells in
+this table. Octopus cells have collaterals (including in granule cell domains),
+and should be added to this table when more data are available (Golding et al.,
+J. Neurosci. 15: 3138, 1995)
+
+----------------------------------------------------------------------------------------------
+                  bushy       tstellate   dstellate   octopus     pyramidal    tuberculoventral
+sgc               3.3±0.6 [2] 6.5±1.0 [2] 35±0 [3]    60±0 [2]    48±0 [5]     24±0 [5]
+dstellate         7 [1]       20 [1]      3 [1]       0 [4]       15 [5]       15 [5]
+tstellate         0 [6]       0 [6]       0 [6]       0 [6]       0 [6]        0 [6]
+tuberculoventral  6           6           0           0 [4]       21 [5]       0 [7]
+pyramidal         0           0           0           0           0            0    
+----------------------------------------------------------------------------------------------
+
+[1] Guesses based on Campagnola & Manis 2014
+
+[2] Cao, X. & Oertel, D. (2010). Auditory nerve fibers excite targets through
+    synapses that vary in convergence, strength, and short-term plasticity. 
+    Journal of Neurophysiology, 104(5), 2308–20.
+    Xie and Manis (unpublished): max EPSC = 3.4 ± 1.5 nA with ~0.3 nA steps
+    (Cao and Oertel, 2010) = ~11 AN inputs. However neither we nor Cao and Oertel
+    see that many clear steps in the responses, so use lower bound.
+    
+[3] Lower bound based on estimates from unpublished data Xie and Manis (2017)
+    Assumptions: No discernable step sizes when increasing shock intensity 
+    at ANFs in radiate multipolars (dstellate)
+     Measured: 0.034 ± 15 nA sEPSC @ -70 mV
+     Measured: Maximal current from AN stim = 1.2 ± 0.7 nA @ -70 mV
+     Assuming that each AN provides 1 input, then N = ~35
+     
+[4] Octopus cells are devoid of inhibitory input (Golding et al., J. Neurosci., 1995)
+
+[5] Convergence from Hancock and Voigt, Ann. Biomed. Eng. 27, 1999 and Zheng and Voigt,
+    Ann. Biomed. Eng., 34, 2006.  Numbers are based on models for cat and gerbil,
+    respectively. Adjusted to 1/2 to avoid overexciting TV cells in network model.
+
+[6] tstellate cells have collaterals within the CN. It has been proposed that they
+    provide auditory-driven input to the DCN (Oertel and Young, ), and also synapse
+    within the VCN (Oertel, SFN abstract). These parameters may need to be adjusted
+    once the convergence and strength is known.
+
+[7] In the models of Hancock and Voigt (1999) and Zheng and Voigt (2006), the TV cells
+    have no connections with each other. However, Kuo et al. (J. Neurophysiol., 2015)
+    did see connections between pairs of TV cells in the mouse.
+
+"""
+
+add_table_data(
+    "convergence",
+    row_key="pre_type",
+    col_key="post_type",
+    species="mouse",
+    data=mouse_convergence,
+)
+
+
+mouse_convergence_range = u"""
+
+The convergence range table describes, for each type of connection from 
+presynaptic (rows) to postsynaptic (columns), the variance in frequency of
+presynaptic cells relative to the postsynaptic cell.
+
+All values are expressed as the sigma for a lognormal distribution scaled to
+the CF of the postsynaptic cell. 
+
+----------------------------------------------------------------------------------------------
+                  bushy       tstellate   dstellate   octopus     pyramidal    tuberculoventral
+sgc               0.05 [1]    0.1 [1]     0.4 [1]     0.5 [5]     0.1 [1]      0.1 [1]
+dstellate         0.208 [2]   0.347 [2]   0.5 [1]     0           0.2 [1]      0.2 [1]      
+tstellate         0.1 [4]     0.1 [4]     0           0           0            0    
+tuberculoventral  0.069 [3]   0.111 [3]   0           0           0.15 [1]     0    
+pyramidal         0           0           0           0           0            0    
+----------------------------------------------------------------------------------------------
+
+[1] Guess based on axonal / dendritic morphology.
+
+[2] Calculated from Campagnola & Manis 2014 fig. 7C
+    Distribution widths are given in stdev(octaves), so we multiply by ln(2) to
+    get the sigma for a lognormal distribution.
+        DS->Bushy:     ln(2) * 0.3 = 0.208
+        DS->TStellate: ln(2) * 0.5 = 0.347
+
+[3] Calculated from Campagnola & Manis 2014 fig. 9C
+    Distribution widths are given in stdev(octaves), so we multiply by ln(2) to
+    get the sigma for a lognormal distribution.
+        TV->Bushy:     ln(2) * 0.10 = 0.069
+        TV->TStellate: ln(2) * 0.16 = 0.111
+
+[4] Guess based on very limited information in Campagnola & Manis 2014 fig. 12
+
+[5] Octopus cells get a wide range of ANF input (but weak on a per input basis)
+    For example, see McGinley et al., 2012 or Spencer et al., 2012.
+
+
+"""
+
+add_table_data(
+    "convergence_range",
+    row_key="pre_type",
+    col_key="post_type",
+    species="mouse",
+    data=mouse_convergence_range,
+)
+
+# --------------------------------------------------------------------------------------------
+guineapig_convergence = u"""
+
+Convergence defines the average number of presynaptic cells of a particular
+type (rows) that synapse onto a single postsynaptic cell of a particular
+type (columns).
+This connectivity matrix is currently incomplete.
+Note: Bushy and pyramidal cells are known to have no (or very few)
+collaterals within the CN, and so they are not listed as presynaptic cells in
+this table. Octopus cells have collaterals (including in granule cell domains),
+and should be added to this table when more data are available (Golding et al.,
+J. Neurosci. 15: 3138, 1995)
+
+This table is just a guess... using mouse data...
+
+----------------------------------------------------------------------------------------------
+                  bushy       tstellate   dstellate   octopus     pyramidal    tuberculoventral   mso
+sgc               3.3±0.6 [2] 6.5±1.0 [2] 35±0 [3]    60±0 [2]    48±0 [5]     24±0 [5]           0
+bushy             0           0           0           0           0            0                  12 [8]
+dstellate         7 [1]       20 [1]      3 [1]       0 [4]       15 [5]       15 [5]             0
+tstellate         0 [6]       0 [6]       0 [6]       0 [6]       0 [6]        0 [6]              0
+tuberculoventral  6           6           0           0 [4]       21 [5]       0 [7]              0
+pyramidal         0           0           0           0           0            0                  0
+----------------------------------------------------------------------------------------------
+
+[1] Guesses based on Campagnola & Manis 2014 (using mouse data on guinea pig cells)
+
+[2] Cao, X. & Oertel, D. (2010). Auditory nerve fibers excite targets through
+    synapses that vary in convergence, strength, and short-term plasticity. 
+    Journal of Neurophysiology, 104(5), 2308–20.
+    Xie and Manis (unpublished): max EPSC = 3.4 ± 1.5 nA with ~0.3 nA steps
+    (Cao and Oertel, 2010) = ~11 AN inputs. However neither we nor Cao and Oertel
+    see that many clear steps in the responses, so use lower bound.
+    
+[3] Lower bound based on estimates from unpublished data Xie and Manis (2017)
+    Assumptions: No discernable step sizes when increasing shock intensity 
+    at ANFs in radiate multipolars (dstellate)
+     Measured: 0.034 ± 15 nA sEPSC @ -70 mV
+     Measured: Maximal current from AN stim = 1.2 ± 0.7 nA @ -70 mV
+     Assuming that each AN provides 1 input, then N = ~35
+     
+[4] Octopus cells are devoid of inhibitory input (Golding et al., J. Neurosci., 1995)
+
+[5] Convergence from Hancock and Voigt, Ann. Biomed. Eng. 27, 1999 and Zheng and Voigt,
+    Ann. Biomed. Eng., 34, 2006.  Numbers are based on models for cat and gerbil,
+    respectively. Adjusted to 1/2 to avoid overexciting TV cells in network model.
+
+[6] tstellate cells have collaterals within the CN. It has been proposed that they
+    provide auditory-driven input to the DCN (Oertel and Young, ), and also synapse
+    within the VCN (Oertel, SFN abstract). These parameters may need to be adjusted
+    once the convergence and strength is known.
+
+[7] In the models of Hancock and Voigt (1999) and Zheng and Voigt (2006), the TV cells
+    have no connections with each other. However, Kuo et al. (J. Neurophysiol., 2015)
+    did see connections between pairs of TV cells in the mouse.
+
+[8] Bushy convergence to MSO is a guess
+"""
+
+add_table_data(
+    "convergence",
+    row_key="pre_type",
+    col_key="post_type",
+    species="guineapig",
+    data=guineapig_convergence,
+)
+
+
+guineapig_convergence_range = u"""
+
+The convergence range table describes, for each type of connection from 
+presynaptic (rows) to postsynaptic (columns), the variance in frequency of
+presynaptic cells relative to the postsynaptic cell.
+
+All values are expressed as the sigma for a lognormal distribution scaled to
+the CF of the postsynaptic cell. 
+
+*** This table is just a guess - using data from mouse... ****
+
+-------------------------------------------------------------------------------------------------------
+                  bushy       tstellate   dstellate   octopus     pyramidal    tuberculoventral   mso
+sgc               0.05 [1]    0.1 [1]     0.4 [1]     0.5 [5]     0.1 [1]      0.1 [1]            0
+bushy             0           0           0           0           0            0                  0.05 [6]
+dstellate         0.208 [2]   0.347 [2]   0.5 [1]     0           0.2 [1]      0.2 [1]            0
+tstellate         0.1 [4]     0.1 [4]     0           0           0            0                  0
+tuberculoventral  0.069 [3]   0.111 [3]   0           0           0.15 [1]     0                  0
+pyramidal         0           0           0           0           0            0                  0
+--------------------------------------------------------------------------------------------------------
+
+[1] Guess based on axonal / dendritic morphology.
+
+[2] Calculated from Campagnola & Manis 2014 fig. 7C (Using mouse data on guinea pig cells)
+    Distribution widths are given in stdev(octaves), so we multiply by ln(2) to
+    get the sigma for a lognormal distribution.
+        DS->Bushy:     ln(2) * 0.3 = 0.208
+        DS->TStellate: ln(2) * 0.5 = 0.347
+
+[3] Calculated from Campagnola & Manis 2014 fig. 9C (Using mouse data on guinea pig cells)
+    Distribution widths are given in stdev(octaves), so we multiply by ln(2) to
+    get the sigma for a lognormal distribution.
+        TV->Bushy:     ln(2) * 0.10 = 0.069
+        TV->TStellate: ln(2) * 0.16 = 0.111
+
+[4] Guess based on very limited information in Campagnola & Manis 2014 fig. 12
+
+[5] Octopus cells get a wide range of ANF input (but weak on a per input basis)
+    For example, see McGinley et al., 2012 or Spencer et al., 2012.
+
+[6] MSO convergence from bushy cells is a guess.
+
+"""
+
+add_table_data(
+    "convergence_range",
+    row_key="pre_type",
+    col_key="post_type",
+    species="guineapig",
+    data=guineapig_convergence_range,
+)

cnmodel/data/ionchannels.py

@@ -0,0 +1,582 @@
+# -*- encoding: utf-8 -*-
+from ._db import add_table_data
+
+"""
+Ion channel density tables
+All of the ion channel densities for the models implemented in cnmodel
+are (or should be) stated here, and should not be modified in the
+cnmodel code itself. 
+
+"""
+
+add_table_data(
+    "RM03_channels",
+    row_key="field",
+    col_key="model_type",
+    species="guineapig",
+    data=u"""
+
+This table describes the ion channel densities (and voltage shifts if necessary)
+for different cell types in the original Rothman Manis 2003 model.
+Data from Table 1, except for "octopus" cells, which is modified (see note 3)
+map to cell:    bushy-II      bushy-II-I    tstellate     tstellate-t   bushy-I-II    octopus
+-----------------------------------------------------------------------------------------------------------------------------------
+               II            II-I          I-c           I-t           I-II          II-o
+
+nacn_gbar      1000. [1]     1000. [1]     1000. [1]     1000. [1]     1000. [2]     1000. [3]
+kht_gbar       150.0 [1]     150.0 [1]     150.0 [1]     80.0  [1]     150.0 [2]     150.0 [3] 
+klt_gbar       200.0 [1]     35.0  [1]     0.0   [1]     0.0   [1]     20.0  [2]     1000. [3] 
+ka_gbar        0.0   [1]     0.0   [1]     0.0   [1]     65.0  [1]     0.0   [2]     0.0   [3]
+ih_gbar        20.0  [1]     3.5   [1]     0.5   [1]     0.5   [1]     2.0   [2]     30.0  [3]
+leak_gbar      2.0   [1]     2.0   [1]     2.0   [1]     2.0   [1]     2.0   [2]     2.0   [3]
+leak_erev      -65   [1]     -65   [1]     -65   [1]     -65   [1]     -65   [2]     -65   [3]
+na_type        nacn  [1]     nacn  [1]     nacn  [1]     nacn  [1]     nacn  [2]     nacn  [3]
+ih_type        ihvcn [1]     ihvcn [1]     ihvcn [1]     ihvcn [1]     ihvcn [2]     ihvcn [3]
+soma_Cap       12.0  [1]     12.0  [1]     12.0  [1]     12.0  [1]     12.0  [2]     25.0  [3]
+e_k            -84   [1]     -84   [1]     -84   [1]     -84   [2]     -84   [2]     -84   [2] 
+e_na           50.   [1]     50.   [1]     50.   [1]     50.   [2]     50.   [2]     50.   [2] 
+ih_eh          -43   [1]     -43   [1]     -43   [1]     -43   [2]     -43   [2]     -43   [2] 
+
+-----------------------------------------------------------------------------------------------------------------------------------
+
+[1] Rothman and Manis, 2003
+    Age "adult", Temperature=22C
+    Units are nS.
+
+[2] Rothman and manis, 2003, model I-II
+    Some low-voltage K current, based on observations of
+    a single spike near threshold and regular firing for higher
+    currents (Xie and Manis, 2017)
+    
+[3] Derived from Rothman and Manis, 2003, model II
+    Large amounts of low-voltage K current, and elevated HCN. Conductances
+    based on Rothman and Manis, 2003; concept from Cao and Oertel
+
+[4] Designation for elevated LTK and Ih for octopus cells
+
+""",
+)
+
+add_table_data(
+    "XM13_channels",
+    row_key="field",
+    col_key="model_type",
+    species="mouse",
+    data=u"""
+
+This table describes the REFERENCE ion channel densities (and voltage shifts if necessary)
+for different cell types based on the Xie and Manis 2013 models for mouse.
+
+The REFERENCE values are applied to "point" models, and to the soma of
+compartmental models.
+The names of the mechanisms must match a channel mechanism (Neuron .mod files)
+and the following _(gbar, vshift, etc) must match an attribute of that channel
+that can be accessed.
+
+-----------------------------------------------------------------------------------------------------------------------------------
+               II             II-I           I-c           I-II          I-t       
+                                                                                   
+nav11_gbar     0000.  [4]     0000.  [4]     000.   [4]    0.     [4]    3000.  [4] 
+nacn_gbar      1000.  [1]     1000.  [1]     3000.  [1]    0000.  [2]    0000.  [1] 
+na_gbar        1000.  [1]     1000.  [1]     3000.  [1]    1800.  [2]    0000.  [1] 
+kht_gbar       58.0   [1]     58.0   [1]     500.0  [1]    150.0  [2]    500.0  [1] 
+klt_gbar       80.0   [1]     20.0   [1]     0.0    [1]    14.0   [3]    0.0    [1] 
+ka_gbar        0.0    [1]     0.0    [1]     0.0    [1]    0.0    [2]    125.0  [1] 
+ihvcn_gbar     30.0   [1]     30.0   [1]     18.0   [1]    2.0    [2]    18.0   [1] 
+leak_gbar      2.0    [1]     2.0    [1]     8.0    [1]    2.0    [2]    8.0    [1] 
+leak_erev      -65    [1]     -65    [1]     -65    [1]    -65    [2]    -65    [1] 
+na_type        nacn   [1]     nav11  [1]     nacn   [1]    na     [3]    nav11  [1] 
+ih_type        ihvcn  [1]     ihvcn  [1]     ihvcn  [1]    ihvcn  [2]    ihvcn  [1] 
+soma_Cap       26.0   [1]     26.0   [1]     25.0   [1]    25.0   [2]    25.0   [1] 
+nav11_vshift   4.3    [1]     4.3    [1]     4.3    [1]    4.3    [1]    4.3    [1]
+e_k            -84    [1]     -84    [1]     -84    [1]    -70    [3]    -84    [1] 
+e_na           50.    [1]     50.    [1]     50.    [1]    55.    [3]    50.    [1] 
+ih_eh          -43    [1]     -43    [1]     -43    [1]    -43    [2]    -43    [1] 
+
+-----------------------------------------------------------------------------------------------------------------------------------
+
+[1] Uses channels from Rothman and Manis, 2003
+    Conductances are for Mouse bushy cells
+    Xie and Manis, 2013
+    Age "adult", Temperature=34C
+    Units are nS.
+
+[2] Rothman and manis, 2003, model I-II
+    Some low-voltage K current, based on observations of
+    a single spike near threshold and regular firing for higher
+    currents (Xie and Manis, 2017)
+
+[3] These values for the I-II (dstellate) are from the original checkpoint test
+    for cnmodel 12/2017. 
+
+[4] nav11 channels were used in original Xie and Manis (2013) ms, but are not
+    used for mice in the master distribution of cnmodel, which used only the nacn
+    channels.  
+
+""",
+)
+
+add_table_data(
+    "XM13_channels_compartments",
+    row_key="parameter",
+    col_key="compartment",
+    species="mouse",
+    model_type="II",
+    data=u"""
+
+This table describes the ion channel densities relative to somatic densities,
+e.g., relative to REFERENCE densities in the table XM13_channels.
+and voltage shifts, for different compartments of the specified neuron,
+Conductances will be calculated from the Model derived from Xie and Manis 2013 for mouse
+(data table: mGVC_channels).
+
+------------------------------------------------------------------------------------------------------------------------------------------------------------------
+               axon       unmyelinatedaxon     myelinatedaxon     initialsegment    hillock     soma        dendrite         primarydendrite    secondarydendrite
+                                                                                                                                                                                              
+nav11_gbar     3.0 [1]    3.0 [1]              0.0 [1]            5.0 [1]           5.0 [1]     1.0 [1]     0.5 [1]          0.50 [1]           0.25 [1]       
+kht_gbar       1.0 [1]    2.0 [1]              0.01 [1]           2.0 [1]           2.0 [1]     1.0 [1]     0.5 [1]          0.5 [1]            0.25 [1]       
+klt_gbar       1.0 [1]    1.0 [1]              0.01 [1]           1.0 [1]           1.0 [1]     1.0 [1]     0.5 [1]          0.5 [1]            0.25 [1]       
+ihvcn_gbar     0.0 [1]    0.0 [1]              0.0 [1]            0.5 [1]           0.0 [1]     1.0 [1]     0.5 [1]          0.5 [1]            0.5 [1]       
+leak_gbar      1.0 [1]    0.25 [1]             0.25e-3 [1]        1.0 [1]           1.0 [1]     1.0 [1]     0.5 [1]          0.5 [1]            0.5 [1]       
+leak_erev      -65. [1]   -65. [1]             -65. [1]           -65. [1]          -65. [1]    -65. [1]    -65. [1]         -65. [1]           -65. [1]      
+nav11_vshift   4.3  [1]   4.3  [1]             0.0 [1]            4.3  [1]          4.3  [1]    0.0 [1]     0.0 [1]          0.0 [1]            0.0 [1]       
+na_type        nav11      nav11                nav11              nav11             nav11       nav11       nav11            nav11              nav11
+ih_type        ihvcn      ihvcn                ihvcn              ihvcn             ihvcn       ihvcn       ihvcn            ihvcn              ihvcn                            
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+[1] Scaling is relative to soma scaling. Numbers are estimates based on general distribution from literature on cortical neurons.
+
+
+""",
+)
+
+
+# ***** BEGINNING OF XM13_Channels for nacncoop version of model
+
+
+add_table_data(
+    "XM13nacncoop_channels",
+    row_key="field",
+    col_key="model_type",
+    species="mouse",
+    data=u"""
+
+This table describes the REFERENCE ion channel densities (and voltage shifts if necessary)
+for different cell types based on the Xie and Manis 2013 models for mouse, but using
+the nacncoop mechanism (coooperative sodium channels)
+
+!!!!!!!!!!!! USAGE OF THIS TABLE SHOULD BE CONSIDERED EXPERIMENTAL !!!!!!!!!!!!!!
+
+The REFERENCE values are applied to "point" models, and to the soma of
+compartmental models.
+The names of the mechanisms must match a channel mechanism (Neuron .mod files)
+and the following _(gbar, vshift, etc) must match an attribute of that channel
+that can be accessed.
+
+-----------------------------------------------------------------------------------------------------------------------------------
+                 II             II-I           I-c           I-II          I-t       
+                                                                                     
+nacncoop_gbar    3000.  [4]     1000.  [4]     1000.  [4]    1000.  [4]    1000.  [4] 
+kht_gbar         58.0   [1]     58.0   [1]     500.0  [1]    150.0  [2]    500.0  [1] 
+klt_gbar         80.0   [1]     20.0   [1]     0.0    [1]    14.0   [3]    0.0    [1] 
+ka_gbar          0.0    [1]     0.0    [1]     0.0    [1]    0.0    [2]    125.0  [1] 
+ihvcn_gbar       30.0   [1]     30.0   [1]     18.0   [1]    2.0    [2]    18.0   [1] 
+leak_gbar        2.0    [1]     2.0    [1]     8.0    [1]    2.0    [2]    8.0    [1] 
+leak_erev        -65    [1]     -65    [1]     -65    [1]    -65    [2]    -65    [1] 
+na_type          nacncoop [1]   nacncoop  [1]  nacncoop [1]  nacncoop [3]  nacncoop [1] 
+ih_type          ihvcn  [1]     ihvcn  [1]     ihvcn  [1]    ihvcn  [2]    ihvcn  [1] 
+soma_Cap         26.0   [1]     26.0   [1]     25.0   [1]    25.0   [2]    25.0   [1] 
+nacncoop_vshift  0.0    [1]     0.0    [1]     0.0    [1]    0.0    [1]    0.0    [1]
+e_k              -84    [1]     -84    [1]     -84    [1]    -70    [3]    -84    [1] 
+e_na             50.    [1]     50.    [1]     50.    [1]    55.    [3]    50.    [1] 
+ih_eh            -43    [1]     -43    [1]     -43    [1]    -43    [2]    -43    [1] 
+
+-----------------------------------------------------------------------------------------------------------------------------------
+
+[1] Uses channels from Xie and Manis, 2013
+    Age "adult", Temperature=34C
+    Units are nS.
+
+[2] Rothman and manis, 2003, model I-II
+    Some low-voltage K current, based on observations of
+    a single spike near threshold and regular firing for higher
+    currents (Xie and Manis, 2017)
+
+[3] These values for the I-II (dstellate) are from the original checkpoint test
+    for cnmodel 12/2017. 
+
+[4] nav11 channels were used in original Xie and Manis (2013) ms, 
+    However, this version uses cooperative na channels for faster activation
+
+""",
+)
+
+add_table_data(
+    "XM13nacncooop_channels_compartments",
+    row_key="parameter",
+    col_key="compartment",
+    species="mouse",
+    model_type="II",
+    data=u"""
+
+!!!!!!!!!!!! USAGE OF THIS TABLE SHOULD BE CONSIDERED EXPERIMENTAL !!!!!!!!!!!!!!
+
+This table describes the ion channel densities relative to somatic densities,
+e.g., relative to REFERENCE densities in the table XM13_nacncoop_channels.
+and voltage shifts, for different compartments of the specified neuron,
+Conductances will be calculated from the Model derived from Xie and Manis 2013 for mouse
+
+------------------------------------------------------------------------------------------------------------------------------------------------------------------
+                   axon           unmyelinatedaxon     myelinatedaxon     initialsegment    hillock     soma        dendrite         primarydendrite    secondarydendrite
+                                                                                                                                                                                                      
+nacncoop_gbar      3.0 [1]        3.0 [1]              0.0 [1]            5.0 [1]           5.0 [1]     1.0 [1]     0.5 [1]          0.50 [1]           0.25 [1]       
+kht_gbar           1.0 [1]        2.0 [1]              0.01 [1]           2.0 [1]           2.0 [1]     1.0 [1]     0.5 [1]          0.5 [1]            0.25 [1]       
+klt_gbar           1.0 [1]        1.0 [1]              0.01 [1]           1.0 [1]           1.0 [1]     1.0 [1]     0.5 [1]          0.5 [1]            0.25 [1]       
+ihvcn_gbar         0.0 [1]        0.0 [1]              0.0 [1]            0.5 [1]           0.0 [1]     1.0 [1]     0.5 [1]          0.5 [1]            0.5 [1]       
+leak_gbar          1.0 [1]        0.25 [1]             0.25e-3 [1]        1.0 [1]           1.0 [1]     1.0 [1]     0.5 [1]          0.5 [1]            0.5 [1]       
+leak_erev          -65. [1]       -65. [1]             -65. [1]           -65. [1]          -65. [1]    -65. [1]    -65. [1]         -65. [1]           -65. [1]      
+nacncoop_vshift    0.0  [1]       0.0  [1]             0.0 [1]            0.0  [1]          0.0  [1]    0.0 [1]     0.0 [1]          0.0 [1]            0.0 [1]       
+na_type            nacncoop       nacncoop             nacncoop           nacncoop          nacncoop    nacncoop    nacncoop            nacncoop              nacncoop
+ih_type            ihvcn          ihvcn                ihvcn              ihvcn             ihvcn       ihvcn       ihvcn            ihvcn              ihvcn                            
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+[1] Scaling is relative to soma scaling. Numbers are estimates based on general distribution from literature on cortical neurons.
+
+
+""",
+)
+
+# ***** END OF XM13_Channels for nacncoop version of model
+
+add_table_data(
+    "mGBC_channels",
+    row_key="field",
+    col_key="model_type",
+    species="mouse",
+    data=u"""
+
+This table describes the REFERENCE ion channel densities (and voltage shifts if necessary)
+for different cell types based on the Xie and Manis 2013 models for mouse.
+
+The REFERENCE values are applied to "point" models, and to the soma of
+compartmental models.
+The names of the mechanisms must match a channel mechanism (Neuron .mod files)
+and the following _(gbar, vshift, etc) must match an attribute of that channel
+that can be accessed.
+
+-----------------------------------------------------------------------------------------------------------------------------------
+               II             II-I           I-c           I-II          I-t       
+                                                                                   
+nav11_gbar     1600.  [1]     1600.  [1]     3000.  [1]    1600.  [2]    3000.  [1] 
+kht_gbar       58.0   [1]     58.0   [1]     500.0  [1]    150.0  [2]    500.0  [1] 
+klt_gbar       80.0   [1]     14.0   [1]     0.0    [1]    20.0   [2]    0.0    [1] 
+ka_gbar        0.0    [1]     0.0    [1]     0.0    [1]    0.0    [2]    125.0    [1] 
+ihvcn_gbar     30.0   [1]     30.0   [1]     18.0   [1]    2.0    [2]    18.0   [1] 
+leak_gbar      2.0    [1]     2.0    [1]     8.0    [1]    2.0    [2]    8.0    [1] 
+leak_erev      -65    [1]     -65    [1]     -65    [1]    -65    [2]    -65    [1] 
+na_type        nav11  [1]     nav11  [1]     nav11  [1]    nav11  [1]    nav11  [1] 
+ih_type        ihvcn  [1]     ihvcn  [1]     ihvcn  [1]    ihvcn  [2]    ihvcn  [1] 
+soma_Cap       26.0   [1]     26.0   [1]     25.0   [1]    26.0   [2]    25.0   [1] 
+nav11_vshift   4.3    [1]     4.3    [1]     4.3    [1]    4.3    [1]    4.3    [1]
+e_k            -84    [1]     -84    [1]     -84    [1]    -84    [2]    -84    [1] 
+e_na           50.    [1]     50.    [1]     50.    [1]    50.    [2]    50.    [1] 
+ih_eh          -43    [1]     -43    [1]     -43    [1]    -43    [2]    -43    [1] 
+
+-----------------------------------------------------------------------------------------------------------------------------------
+
+[1] Uses channels from Rothman and Manis, 2003, except for Na channels
+    Conductances are for Mouse bushy cells
+    Xie and Manis, 2013
+    Age "adult", Temperature=34C
+    Units are nS.
+
+[2] Rothman and Manis, 2003, model I-II
+    Some low-voltage K current, based on observations of
+    a single spike near threshold and regular firing for higher
+    currents (Xie and Manis, 2017)
+
+
+""",
+)
+
+
+add_table_data(
+    "mGBC_channels_compartments",
+    row_key="parameter",
+    col_key="compartment",
+    species="mouse",
+    model_type="II",
+    data=u"""
+
+This table describes the ion channel densities relative to somatic densities,
+e.g., relative to REFERENCE densities in the table XM13_channels.
+and voltage shifts, for different compartments of the specified neuron,
+Conductances will be calculated from the Model for Xie and Manis 2013 for mouse
+(data table: XM13_channels).
+
+------------------------------------------------------------------------------------------------------------------------------------------------------------------
+               axon       unmyelinatedaxon     myelinatedaxon     initialsegment    hillock     soma        dendrite         primarydendrite    secondarydendrite
+                                                                                                                                                                                              
+nav11_gbar     3.0 [1]    3.0 [1]              0.0 [1]            3.0 [1]           2.0 [1]     1.0 [1]     0.25 [1]         0.25 [1]           0.25 [1]       
+kht_gbar       1.0 [1]    2.0 [1]              0.01 [1]           2.0 [1]           2.0 [1]     1.0 [1]     0.5 [1]          0.5 [1]            0.25 [1]       
+klt_gbar       1.0 [1]    1.0 [1]              0.01 [1]           1.0 [1]           1.0 [1]     1.0 [1]     0.5 [1]          0.5 [1]            0.25 [1]       
+ihvcn_gbar     0.0 [1]    0.0 [1]              0.0 [1]            0.5 [1]           0.0 [1]     1.0 [1]     0.5 [1]          0.5 [1]            0.5 [1]       
+leak_gbar      1.0 [1]    0.25 [1]             0.25e-3 [1]        1.0 [1]           1.0 [1]     1.0 [1]     0.5 [1]          0.5 [1]            0.5 [1]       
+leak_erev      -65. [1]   -65. [1]             -65. [1]           -65. [1]          -65. [1]    -65. [1]    -65. [1]         -65. [1]           -65. [1]      
+nav11_vshift   4.3  [1]   4.3  [1]             0.0 [1]            4.3  [1]          4.3  [1]    0.0 [1]     0.0 [1]          0.0 [1]            0.0 [1]       
+na_type        nav11      nav11                nav11              nav11             nav11       nav11       nav11            nav11              nav11
+ih_type        ihvcn      ihvcn                ihvcn              ihvcn             ihvcn       ihvcn       ihvcn            ihvcn              ihvcn                            
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+[1] Scaling is relative to soma scaling. Numbers are estimates based on general distribution from literature on cortical neurons.
+
+
+""",
+)
+
+
+add_table_data(
+    "POK_channels",
+    row_key="field",
+    col_key="cell_type",
+    species="rat",
+    data=u"""
+
+This table describes the ion channel densities and voltage shifts for rat DCN pyramidal cells,
+from Kanold and Manis, 2001
+
+------------------------------------------------------------------------------------------------------------------------------------------
+                          pyramidal   
+                                   
+soma_napyr_gbar           350.0  [1]      
+soma_nap_gbar             0.
+soma_kdpyr_gbar           80.0   [1]
+soma_kcnq_gbar            0.
+soma_kif_gbar             150.0  [1]
+soma_kis_gbar             40.0   [1]
+soma_ihpyr_gbar           2.8    [1]     
+soma_leak_gbar            2.8    [1]
+soma_leak_erev            -62.0  [1]
+soma_e_na                 50.    [1]
+soma_e_k                  -81.5  [1]
+soma_e_h                  -43.0  [1]
+soma_natype               napyr
+soma_Cap                  12     [1]
+------------------------------------------------------------------------------------------------------------------------------------------
+
+[1] Kanold and Manis, 1999, 2001, 2005
+    Age P11-14, Temperature=22C
+    Units are nS.
+[2] Adjustable q10 added for fitting
+    soma_ihpyr_adj_q10        1.0    [2]      (removed for testing)
+
+""",
+)
+
+add_table_data(
+    "CW_channels",
+    row_key="field",
+    col_key="cell_type",
+    species="mouse",
+    data=u"""
+
+This table describes the ion channel densities and voltage shifts
+for a mouse carthweel cell model.
+Ad-hoc model, based on a Purkinje cell model (ref [1]).
+
+
+-----------------------------------------------------------------------------------------------------------------------------------
+                   cartwheel   
+                           
+soma_narsg_gbar    500.0  [1]     
+soma_bkpkj_gbar    2.0
+soma_kpkj_gbar     100.   [1]
+soma_kpkj2_gbar    50.
+soma_kpkjslow_gbar 150    [1]
+soma_kpksk_gbar    25.0   [1]
+soma_lkpkj_gbar    5.0    [1]     
+soma_hpkj_gbar     5.0    [1]
+soma_e_na          50.    [1]
+soma_e_k           -80.0  [1]
+soma_hpkj_eh       -43.0  [1]
+soma_lkpkj_e       -65.0  [1]
+soma_e_ca          50.
+soma_na_type       narsg
+soma_pcabar        0.00015 [1]
+soma_Dia           18  
+
+-----------------------------------------------------------------------------------------------------------------------------------
+
+[1] Channels from Khaliq, Gouwens and Raman, J. Neurosci. 2003
+    Conductance levels modified. 
+
+""",
+)
+
+add_table_data(
+    "TV_channels",
+    row_key="field",
+    col_key="cell_type",
+    species="mouse",
+    data=u"""
+
+This table describes the ion channel densities and voltage shifts
+for a mouse tuberculoventral cell model.
+Ad-hoc model, based on the t-stellate cell model, but adjusted
+to match the data from Kuo and Trussell.
+
+-----------------------------------------------------------------------------------------------------------------------------------
+                     TVmouse   
+                              
+soma_nacncoop_gbar   5800.0   [2]      
+soma_kht_gbar        400.0    [1]
+soma_ihvcn_gbar      2.5      [2]
+soma_ka_gbar         65.0     [1]
+soma_leak_gbar       4.5      [1]
+soma_leak_erev       -72.0    [1]
+soma_e_na            50.      [1]
+soma_e_k             -81.5    [1]
+soma_ihvcn_eh        -43.0    [1]
+soma_na_type         nacncoop [2]
+soma_Cap             35       [1]
+
+-----------------------------------------------------------------------------------------------------------------------------------
+
+[1] Values obtained from brute force runs and comparision to 
+    FI curve from Kuo, Lu and Trussell, J Neurophysiol. 2012 Aug 15; 
+    108(4): 1186–1198.
+
+[2] Cooperative sodium channel model, based on (see the mechanisms folder)
+    concepts and implementation similar to Oz et al. J.Comp. Neurosci. 39: 63, 2015,
+    and Huang et al., PloSOne 7:e37729, 2012.
+
+
+""",
+)
+
+add_table_data(
+    "sgc_mouse_channels",
+    row_key="field",
+    col_key="cell_type",
+    species="mouse",
+    data=u"""
+
+This table describes the ion channel densities (and voltage shifts if necessary)
+for SGC cells, based on 
+
+-----------------------------------------------------------------------------------------------------------------------------------
+                    sgc-a         sgc-bm   
+                                        
+sgc_name            a             bm     
+soma_na_gbar        350.  [2]     350.  [2]
+soma_kht_gbar       58.0  [1]     58.0  [1]
+soma_klt_gbar       80.0  [1]     80.0  [1]
+soma_ihap_gbar      3.0   [3]     0.0   [1]
+soma_ihap_eh        -41.0 [3]     -41.0 [3]
+soma_ihbm_gbar      0.0   [3]     3.0   [3]
+soma_ihbm_eh        -41.0 [3]     -41.0 [3]
+soma_leak_gbar      2.0   [1]     2.0   [1]
+soma_leak_erev      -65   [1]     -65   [1]
+soma_na_type        jsrna [2]     jsrna [2]
+soma_Cap            12.0  [1]     12.0  [1]
+soma_e_k            -84   [1]     -84   [1]
+soma_e_na           50.   [1]     50.   [1]
+
+-----------------------------------------------------------------------------------------------------------------------------------
+
+[1] Model is based on the mouse bushy cell model (XM13, above),
+    but with a fast sodium channel from Rothman et al, 1993. and Ih currents
+    from Liu et al. 2014
+    
+[2] Sodium channel from Rothman, Young and Manis, J Neurophysiol. 1993 Dec;70(6):2562-83. 
+
+[3] Ih Currents from Liu, Manis, Davis, J Assoc Res Otolaryngol. 2014 Aug;15(4):585-99.
+    doi: 10.1007/s10162-014-0446-z. Epub 2014 Feb 21.
+    Age "P10" (cultured SGC cells), Original data temperature=22C.
+    Units are nS.
+
+""",
+)
+
+
+add_table_data(
+    "sgc_guineapig_channels",
+    row_key="field",
+    col_key="cell_type",
+    species="guineapig",
+    data=u"""
+
+This table describes the ion channel densities (and voltage shifts if necessary)
+for a model SGC cell, which is based on a bushy cell with a different Na channel.
+
+-----------------------------------------------------------------------------------------------------------------------------------
+                    sgc-a         sgc-bm   
+                                        
+sgc_name            a             bm     
+soma_na_gbar        1000. [2]     1000. [2]
+soma_kht_gbar       150.0 [1]     150.0 [1]
+soma_klt_gbar       200.0 [1]     200.0 [1]
+soma_ihap_gbar      3.0   [3]     0.0   [3]
+soma_ihap_eh        -41.0 [3]     -41.0 [3]
+soma_ihbm_gbar      0.0   [3]     3.0   [3]
+soma_ihbm_eh        -41.0 [3]     -41.0 [3]
+soma_leak_gbar      2.0   [1]     2.0   [1]
+soma_leak_erev      -65   [1]     -65   [1]
+soma_na_type        jsrna [2]     jsrna [2]
+soma_Cap            12.0  [1]     12.0  [1]
+soma_e_k            -84   [1]     -84   [1]
+soma_e_na           50.   [1]     50.   [1]
+
+-----------------------------------------------------------------------------------------------------------------------------------
+
+[1] Model is based on the guinea pig bushy cell model (RM03, above),
+    but with a fast sodium channel from Rothman et al, 1993. and Ih currents
+    from Liu et al. 2014
+    
+[2] Sodium channel from Rothman, Young and Manis, J Neurophysiol. 1993 Dec;70(6):2562-83. 
+
+[3] Ih Currents from Liu, Manis, Davis, J Assoc Res Otolaryngol. 2014 Aug;15(4):585-99.
+    doi: 10.1007/s10162-014-0446-z. Epub 2014 Feb 21.
+    Age "P10" (cultured SGC cells), Temperature=22C.
+    Units are nS.
+
+""",
+)
+
+add_table_data(
+    "MSO_principal_channels",
+    row_key="field",
+    col_key="cell_type",
+    species="guineapig",
+    data=u"""
+
+This table describes the ion channel densities
+for a putative MSO principal neuron based on the original Rothman Manis 2003 model for bushy cells.
+
+-----------------------------------------------------------------------------------------------------------------------------------
+                    MSO-principal   
+                             
+MSO_name            Principal       
+soma_na_gbar        1000. [1]
+soma_kht_gbar       150.0 [1]
+soma_klt_gbar       200.0 [1]
+soma_ka_gbar        0.0   [1]
+soma_ih_gbar        20.0  [1]
+soma_leak_gbar      2.0   [1]
+soma_leak_erev      -65   [1]
+soma_na_type        nacn  [1]
+soma_ih_type        ihvcn [1]
+soma_Cap            12.0  [1]
+soma_e_k            -84   [1]
+soma_e_na           50.   [1]
+soma_ih_eh          -43   [1]
+
+-----------------------------------------------------------------------------------------------------------------------------------
+
+[1] This MSO neuron model is basied on Rothman and Manis, 2003 bushy cell, type II
+    Age "adult", Temperature=22C
+    Units are nS.
+
+
+""",
+)

cnmodel/data/populations.py

@@ -0,0 +1,37 @@
+# -*- encoding: utf-8 -*-
+from ._db import add_table_data
+
+add_table_data(
+    "populations",
+    row_key="field",
+    col_key="cell_type",
+    species="mouse",
+    data=u"""
+
+-----------------------------------------------------------------------------------------------------
+             sgc        bushy      tstellate    dstellate     octopus     pyramidal   tuberculoventral
+                                                                                           
+n_cells      10000 [1]  6500 [2]   6500 [2]     650 [3]       5000        3000        5000   
+cf_min       2000       2000       2000         2000          2000        2000        2000   
+cf_max       90000      90000      90000        90000         90000       90000       90000
+-----------------------------------------------------------------------------------------------------
+
+[1] ?
+
+[2] Rough estimate from allen brain atlas data:
+    Volume of VCN is 0.377 mm^3, by counting voxels with 'VCO' (101) label in Common Coordinate Framework atlas.
+        753370 voxels * 0.5 * 10e-6**3 m^3/vox = 0.377 mm^3
+    Counted Slc17a7 (pan-excitatory) cell bodies in a 500x500 um chunk of VCN
+        http://mouse.brain-map.org/experiment/siv?id=69014470&imageId=68856767&initImage=ish&coordSystem=pixel&x=7616.5&y=4144.5&z=1
+        266 cells in 500x500 um = 34707 cells / mm^2
+        34707**3/2 * 0.377 mm^3 = 13084 cells total
+        Assume half are bushy, half are T-stellate
+        
+[3] Rough estimate from allen brain atlas data:
+    Similar to [2], using Gad1 inhibitory marker
+    http://mouse.brain-map.org/experiment/siv?id=75492764&imageId=75405134&initImage=ish&coordSystem=pixel&x=5320.5&y=3232.5&z=1
+    36 cells in 500x500 um = 144e6 / m^2  ~= 1728 / mm^2
+    = 651 cells total  (VCN, unilateral)
+
+""",
+)

cnmodel/data/synapses.py

@@ -0,0 +1,503 @@
+# -*- encoding: utf-8 -*-
+from ._db import add_table_data
+
+# sgc old weights:
+# bushy             tstellate          dstellate         octopus         pyramidal      tuberculoventral
+# weight       0.027 [12]        0.006 [12]         0.00064 [12]      0.0011 [12]     0.0023 [12]    0.0029 [12]
+# tau1         0.1 [5]           0.1 [5]            0.2 [5]           0.1 [5]         0.1 [5]        0.1 [5]
+# tau2         0.3 [5]           0.3 [5]            0.5 [5]           0.3 [5]         0.3 [5]        0.3 [5]
+# erev         0   [5]           0   [5]            0   [5]           0   [5]         0   [5]        0   [5]
+
+add_table_data(
+    "sgc_synapse",
+    row_key="field",
+    col_key="post_type",
+    species="mouse",
+    data=u"""
+
+AMPA_gmax and NMDA_gmax are the estimated average peak conductances (in nS) 
+resulting from an action potential in a single auditory nerve terminal, under 
+conditions that minimize the effects of short-term plasticity.
+AMPA_gmax are from values measured at -65 mV (or -70mV), and represent SINGLE TERMINAL 
+conductances
+AMPAR_gmax are the individual synapse postsynaptic conductance
+NMDA_gmax values are taken as the fraction of the current that is NMDAR dependent
+at +40 mV (see below)
+
+n_rsites is the number of release sites per SGC terminal.
+
+---------------------------------------------------------------------------------------------------------------------------------------
+                  bushy             tstellate         dstellate         octopus           pyramidal         tuberculoventral  cartwheel                                                                                                                    
+                                                                                                            
+AMPA_gmax         21.05±15.4 [1]    4.6±3.1 [2]       0.49±0.29 [7]     0.87±0.23 [3]     0.6±0.3 [8]       2.2±1.5 [8]       0
+AMPAR_gmax        4.6516398 [10]    4.632848  [10]    1.7587450 [10]    16.975147 [10]    0.9 [8]           2.2  [8]          0
+NMDA_gmax         10.8±4.6 [1]      2.4±1.6 [2]       0.552±0.322 [7]   0.17±0.046 [3]    0.4±0.33 [8]      2.4±1.6 [8]       0
+NMDAR_gmax        0.4531933 [10]    1.2127097 [10]    0.9960820 [10]    0.6562702 [10]    0.2 [8]           1.2127097 [8]     0
+NMDAR_vsh         -15.0 [12]        -15.0 [12]        -15.0 [12]        -15.0 [12]        -15.0 [12]        -15.0 [12]        0
+NMDAR_vshift      0.0   [12]        0.0   [12]        0.0   [12]        0.0   [12]        0.0   [12]        0.0   [12]        0
+EPSC_cv           0.12 [8]          0.499759 [9]      0.886406 [9]      1.393382 [9]      0.499 [8]         0.499 [8]         0
+Pr                1.000 [11]        1.000 [11]        1.000 [11]        1.000 [11]        1.000 [8]         1.000 [8]         0
+n_rsites          100 [5]           4 [6]             1 [4]             1 [4]             2 [8]             2 [8]             0
+delay             0.600             0.600             0.600             0.600             0.600             0.600             0
+weight            0.020377          0.003679          0.000457          0.001311          0.000327          0.000808          0
+tau1              0.158             0.174             0.152             0.125             0.167             0.157             0
+tau2              0.246             1.501             1.652             0.251             1.489             1.641             0
+erev              0.0               0.0               0.0               0.0               0.0               0.0               0
+----------------------------------------------------------------------------------------------------------------------------------------
+
+[1] Derived from Cao, X. & Oertel, D. (2010). Single-terminal conductance was
+    reported as 21.5±15.4 nS (1.4±1.0 nA at -65 mV). The ratio of NMDA current to 
+    total current is 0.3, so AMPA and NMDA currents are:
+       AMPA_gmax = 21.5±15.4 nS (measured at -65 mV)
+       NMDA_gmax = 21.5±15.4 nS * 0.3 = 10.8±4.6 nS
+    Age>p17, Temperature=33C, [Mg2+]=1.3mM, [Ca2+]=2.4mM
+    Units are nS.
+    See also Pliss et al., J. Neurophys., 2009 (and note [12])
+
+[2] Derived from Cao, X. & Oertel, D. (2010). Single-terminal conductance was
+    estimated as 4.6±3.1 nS. The ratio of NMDA current to 
+    total current is 0.53, so AMPA and NMDA currents are:
+       AMPA_gmax = 4.6±3.1 nS
+       NMDA_gmax = 4.6±3.1 nS * 0.53 = 2.4±1.6 nS
+    Estimated number of inputs per AN fiber:
+        0.3 nA step, 0.08 nA mini size = ~ 4 inputs per AN fiber
+    Age>p17, Temperature=33C, [Mg2+]=1.3mM, [Ca2+]=2.4mM
+    Units are nS
+
+[3] Derived from Cao, X. & Oertel, D. (2010). Single-terminal conductance was
+    estimated as 52±14 nS / 60 = 0.87±0.23 nS. The ratio of NMDA current to 
+    total current is 0.2, so AMPA and NMDA currents are:
+       AMPA_gmax = 0.87±0.23 nS
+       NMDA_gmax = 0.87±0.23 nS * 0.2 = 0.17±0.046 nS
+    Age>p17, Temperature=33C, [Mg2+]=1.3mM, [Ca2+]=2.4mM
+    Units are nS
+
+[4] Assumption based on mini size and lack of discernable EPSC step (guess).
+    Should be verified.
+
+[5] Oleskevich & Walmsley ~2002, Wang & Manis 2005. Units are nS
+
+[6] A value of 45 would be chosen to satisfy the CV of EPSC amplitude determined in [9].
+    However, those measures are for simultaneous stimulation of multiple AN fibers.
+    A value of 4 is included here to correspond to measures in Cao and Oertel (2010)
+    (see note [2])
+
+[7] (Xie and Manis, Frontiers in Neural Circuits, 2017):
+    Measurements from CBA/CaJ mouse "radiate" multipolar cells in the AVCN.
+    Single terminal conductance = (1.2 ± 0.70 nA/70 mV)/ 35 inputs = 0.490 ± 0.286 nS
+    (see connections.py) 
+    Single terminal conductance from mini = 34 pA/70 mV = 0.486 nS (single mini)
+    Assume same AMPA/NMDA ratio as tstellate cells, but measures made where NMDA = 0
+    (at negative V):
+       AMPA_gmax = 0.490±0.286 nS
+       NMDA_gmax = 0.490±0.286 nS * 0.53/0.47 = 0.552±0.322 nS
+    Age > P35, Temperature=34C, [Mg2+]=1.5mM, [Ca2+]=2.5mM
+
+[8] Thin air.  These are for testing the software, not necessarily for performing
+    real simulations.  Note: Pyramidal cell strength has been reduced 
+    because of large convergence and high input resistance of the reference cell model.
+    Release 1 (Nov 2017):
+     pyramidal    
+                  
+     0.6 ±1.05 [8]
+     1.8 [8]      
+     0.8±0.66 [8] 
+     0.4 [8]      
+     -15.0 [12]   
+     0.499 [8]    
+     1.000 [8]    
+     2 [8]        
+     
+     
+[9] Reanalysis of evoked EPSCs in stellate cells (Manis/Xie, 2014)
+
+[10]  Maximum AMPA open conductance per synaptic site (units are pS). 
+      These values are calculated by running python cnmodel/synapses/tests/test_psd.py
+      for a specific cell type (if the cell uses the receptor mechanisms; this is 
+      not necessary for simple exp2syn style mechanisms)
+      to ensure that maximum AMPA conductance during PSG matches [1, 2 or 3]
+      For a bushy cell, the original default values  (bushy cell) were:
+          AMPAR_gmax   3.314707700918133
+          NMDAR_gmax   0.4531929783503451
+      These values will also depend on the number of release sites per
+      synapse (the total conductance is produce of site gmax and nsites).
+      
+      A note on the precision of these values: This precision is only
+      required for the tests of the model, as a way of ensuring numerical
+      equivalency after potential modifications of the code. The precision
+      of the value is in no way intended to specificy biological precision.
+
+      For example, a change in the rate constants in the AMPA_Trussell AMPA
+      receptor model could (and probably would) change the open probability,
+      and therefore the maximal conductance of an EPSC. However, as this is
+      only a representation of the EPSC, the "receptor" conductance should
+      be scaled so that the computed EPSC has the same maximal conductance
+      as prior to the kinetic modifications. Because the receptor model is
+      numerically computed (and not analytically tractable without
+      additional knowledge of the ligand time course), a numerical solution
+      is required.
+
+[11]  Pr is the initial release probability. The value can be computed by
+      setting Pr to 1 in this file, and running the cnmodel test_synapses.py
+      with the appropriate presynaptic source and postsynaptic target,
+      once all other parameters are set. The Pr is used to rescale
+      the AMPAR_gmax so that the total current matches the data in 
+      AMPA_gmax in the table (on average).
+
+[12]  NMDA_vshift is the voltage shift for the activation of the NMDAR's, relative
+      to 0 (standard in the NMDA_Kampa model). A negative value shifts the voltage
+      dependence to the right (depolarizing).
+      The value of the shift here (-15 mV) was chosen based on an exploration
+      of fitting functions against the NMDA-Kampa IV curve in an SGC-bushy cell
+      model, and comparing them against data. The functions were the modified
+      Woodhull function and a Boltzmann function, yielding values of 1.19 mM for 
+      k0 and 0.78 for delta (tau decay at +40 mV of 16.4 ms), and Vr -3 mV, Vh
+      16 mV for the Boltzmann fit. These are close to the values reported in 
+      for NMDA currents in p14-p26 CBA/CaJ mice in Pliss et al. (J. Neurophys. 
+      102, 2627, 2009). Note: Pliss et al. agree with Cao and Oertel regarding
+      an approximate 10-fold difference between AMPA and NMDA conductance in
+      mouse bushy cells. An exact fit was not obtained, but no other parameters
+      of the NMDA_Kampa model were changed. 
+   
+[13]  weight is the weight to use in a netcon object (NEURON) for "simple"
+      synapses based on the exp2syn mechanism.
+      Parameters Weight, tau1, tau2, delay and erev from comare_simple_multisynapses 
+      run and curve fitting (all cells)
+
+""",
+)
+
+
+add_table_data(
+    "sgc_ampa_kinetics",
+    row_key="field",
+    col_key="post_type",
+    species="mouse",
+    data=u"""
+AMPA receptor kinetic values obtained by fitting the model of Raman and 
+Trussell (1992) to measured EPSCs in the mouse VCN.
+
+Ro1, Ro2, Rc1, Rc2, and PA are kinetic constants affecting the AMPA receptor
+mechanism. tau_g and A affect the speed and amplitude of transmitter release
+(implemented in the presynaptic release mechanism).
+These parameters were selected to fit the model output to known EPSC shapes.
+
+PA is a polyamine block parameter ued in the AMPAR mechanism (concentration in micromolar).
+
+------------------------------------------------------------------------------------------------
+             bushy              tstellate        dstellate        pyramidal    octopus         tuberculoventral       mso     
+                                                                                                                                
+Ro1          107.85 [4]         39.25 [4]        39.25 [7]        39.25 [4]    107.85 [5]      39.25 [7]              107.85 [4]
+Ro2          0.6193 [4]         4.40 [4]         4.40 [7]         4.40 [4]     0.6193 [5]      4.40 [7]               0.6193 [4]
+Rc1          3.678 [4]          0.667 [4]        0.667 [7]        0.667 [4]    3.678 [5]       0.667 [7]              3.678 [4] 
+Rc2          0.3212 [4]         0.237 [4]        0.237 [7]        0.237 [4]    0.3212 [5]      0.237 [7]              0.3212 [4]
+tau_g        0.10 [4]           0.25 [4]         0.25 [7]         0.25 [4]     0.10 [5]        0.25 [4]               0.10 [4]  
+amp_g        0.770 [4]          1.56625 [4]      1.56625 [7]      1.56625 [4]  0.770 [5]       1.56625 [4]            0.770 [4] 
+                                                                                                                                
+PA           45 [12]            0.1 [12]         0.1 [7]          0.1 [12]     45 [5]          0.1 [7]                45 [12]   
+
+------------------------------------------------------------------------------------------------
+
+[4] Xie & Manis 2013, Table 2
+
+[5] copied from bushy cells; no direct data.
+
+[7] Data copied from t-stellate column (no literature on these cells). Unpublished data suggests these
+    should be slightly different, but is complicated by electrotonically distant synaptic sites that
+    preclude accurate measurement of kinetics.
+
+[12] Wang & Manis (unpublished)
+
+""",
+)
+
+
+add_table_data(
+    "sgc_epsp_kinetics",
+    row_key="field",
+    col_key="post_type",
+    species="mouse",
+    data=u"""
+
+EPSC shape parameters obtained from fits of Xie & Manis 2013 Equation 3 to measured EPSCs.
+
+------------------------------------------------------------------------------------------------
+             bushy         tstellate        dstellate        pyramidal    octopus         tuberculoventral
+                                                                          
+tau_r        0.253 [11]    0.19 [11]                                      0.253 [13]
+tau_f        0.16 [11]     1.073 [11]                                     0.16 [13]
+tau_s        0.765 [11]    3.3082 [11]                                    0.765 [13]
+F            0.984 [11]    0.917 [11]                                     0.984 [13]
+                                            
+------------------------------------------------------------------------------------------------
+
+[11] Xie & Manis 2013, Table 3
+[13] Copied from bushy cells; no direct data
+
+""",
+)
+
+
+add_table_data(
+    "sgc_release_dynamics",
+    row_key="field",
+    col_key="post_type",
+    species="mouse",
+    data=u"""
+
+Kinetic parameters correspond to variables as described by Dittman et al. 
+(2000), their Table 1.
+
+F: ~ Resting release probability
+
+---------------------------------------------------------------------------------------------------------------
+             bushy             tstellate         dstellate        pyramidal     octopus        tuberculoventral
+                                                                               
+F            0.29366 [1]       0.43435 [1]       0.43435 [2]      0.43435 [1]   0.29366 [14]   0.43435 [1]   
+k0           0.52313 [1]       0.06717 [1]       0.06717 [2]      0.06717 [1]   0.52313 [14]   0.06717 [1]   
+kmax         19.33805 [1]      52.82713 [1]      52.82713 [2]     52.82713 [1]  19.33805 [14]  52.82713 [1]  
+kd           0.11283 [1]       0.08209 [1]       0.08209 [2]      0.08209 [1]   0.11283 [14]   0.08209 [1]   
+ks           11.531 [1]        14.24460 [1]      14.24460 [2]     14.24460 [1]  11.531 [14]    14.24460 [1]  
+kf           17.78 [1]         18.16292 [1]      18.16292 [2]     18.16292 [1]  17.78 [14]     18.16292 [1]  
+taud         15.16 [1]         3.98 [1]          3.98 [2]         3.98 [1]      15.16 [14]     3.98 [1]      
+taus         17912.2 [1]       16917.120 [1]     16917.120 [2]    16917.120 [1] 17912.2 [14]   16917.120 [1] 
+tauf         9.75 [1]          11.38 [1]         11.38 [2]        11.38 [1]     9.75 [14]      11.38 [1]     
+dD           0.57771 [1]       2.46535 [1]       2.46535 [2]      2.46535 [1]   0.57771 [14]   2.46535 [1]   
+dF           0.60364 [1]       1.44543 [1]       1.44543 [2]      1.44543 [1]   0.60364 [14]   1.44543 [1]   
+
+---------------------------------------------------------------------------------------------------------------
+
+[1] Xie & Manis 2013, Table 1. Although independently measured in > P30 CBA/CaJ mice,
+    the values are similar to the measurements from Yang and Xu-Friedman, 2008
+    in P14-P21 CBA/CaJ mice.
+
+[2] Data copied from t-stellate column (no literature on these cells)
+
+[14] Data copied from bushy cell column (no literature on these cells)
+""",
+)
+
+
+add_table_data(
+    "gly_kinetics",
+    row_key="field",
+    col_key="post_type",
+    species="mouse",
+    data=u"""
+
+Kinetic parameters for glycine receptor mechanisms.
+
+These are currently used for both DS and TV synapses, but should probably be 
+separated in the future.
+
+KV, KU, and XMax are kinetic parameters for the cleft transmitter mechanism.
+
+
+------------------------------------------------------------------------------------------------
+             bushy        tstellate        dstellate     pyramidal         tuberculoventral
+                                                                            
+KV           1e9 [1]      531.0 [1]        531.0 [1]     531.0 [2]         531.0 [2]
+KU           4.46 [1]     4.17 [1]         4.17 [1]      4.17 [2]          4.17 [2] 
+XMax         0.733 [1]    0.731 [1]        0.731 [1]     0.731 [2]         0.731 [2]
+
+------------------------------------------------------------------------------------------------
+
+[1] Xie & Manis 2013
+
+[2] Copied from tstellate data (Kuo et al., J. Neurophysiol. indicate glycinergic IPSCs in TV
+    and pyramidal cells are fast, with a decay time constant similar to that seen in tstellate
+    cells). In pyramidal cells, this is consistent with the brief cross-correlation tip (Voigt
+    and Young, 1980) and brief somatic current source (Manis and Brownell, 1983).
+
+
+""",
+)
+
+add_table_data(
+    "dstellate_synapse",
+    row_key="field",
+    col_key="post_type",
+    species="mouse",
+    data=u"""
+
+DStellate Synapse values
+gly_gmax is the default value in the program (scaled by Po for the receptors). See synapses/gly_psd.py
+IPSC_cv is the coefficient of variation of the IPSC. (Not currently used in the model)
+Pr is the release probabilty (not currently used); built into release mechanism for multisite synapses.
+n_rsites is the number of release sites per dstellate terminal.
+
+---------------------------------------------------------------------------------------------------------------------------------------
+                  bushy             tstellate         dstellate         octopus           pyramidal         tuberculoventral  cartwheel
+                                                                                                                              
+gly_gmax          2.5 [1]           1.0 [5]           1.0 [2]           0. [2]            2.0 [3]           2.0 [3]           0±0 [2]
+IPSC_cv           0.3 [3]           0.3 [3]           0.3 [3]           0.3 [3]           0.3 [3]           0.3 [3]           0.3 [3]    
+Pr                1.000 [4]         1.000 [4]         1.000 [4]         1.000 [4]         1.000 [4]         1.000 [4]         1.000 [4]  
+n_rsites          10 [5]            5 [5]             5 [5]             0 [2]             5 [5]             25 [5]            0 [2]      
+delay             0.000             0.000             0.000             0                 0.000             0.000             0
+weight            0.004131          0.004455          0.0               0                 0.002228          0.012097          0
+tau1              0.187             0.152             0.152             0                 0.152             0.152             0
+tau2              7.953             1.247             1.247             0                 1.247             1.247             0
+erev              -70.0             -70.0             -70.0             0                 -70.0             -70.0             0
+---------------------------------------------------------------------------------------------------------------------------------------
+
+[1] Estimate
+
+[2] No evidence for dstellate inputs to other d stellate cells or cartwheel cells.
+    Octopus cells do not get inhibitory input
+    
+[3] Guess
+
+[4] Default value
+
+[5] Guess *educated* DS->TS from Xie and Manis, 2013. 99 pA mini @ 50 mV driving ~ 2 nS
+
+[6] delay from pre to post; default is 0
+
+[7] Parameters Weight, tau1, tau2, delay and erev from comare_simple_multisynapses run and curve fitting (all cells)
+
+""",
+)
+
+
+add_table_data(
+    "tuberculoventral_synapse",
+    row_key="field",
+    col_key="post_type",
+    species="mouse",
+    data=u"""
+
+Tuberculventral Synapse values
+gly_gmax is the default value in the program (scaled by Po for the receptors). See synapses/gly_psd.py
+IPSC_cv is the coefficient of variation of the IPSC. (Not currently used in the model)
+Pr is the release probabilty (not currently used)
+n_rsites is the number of release sites per tuberculoventral terminal.
+
+-----------------------------------------------------------------------------------------------------------------------------------
+                  bushy             tstellate         dstellate         octopus           pyramidal         tuberculoventral  cartwheel
+                                                                                                                              
+gly_gmax          5.0 [3]           3.0 [3]           3.0 [3]           0. [2]            2.1±2.9 [6]       1.8±2.3 [6]       0±0 [6]
+IPSC_cv           0.3 [3]           0.3 [3]           0.3 [3]           0.3 [3]           1.0 [3]           0.3 [3]           0.3 [3]    
+Pr                1.000 [4]         1.000 [4]         1.000 [4]         1.000 [4]         1.000 [4]         1.000 [4]         1.000 [4]  
+n_rsites          6 [5]             6 [5]             0 [1]             0 [2]             6 [5]             6 [5]             6 [5]      
+delay             0.600             0.600             0                 0                 0.600             0.600             0
+weight            0.002371          0.008114          0                 0                 0.002705          0.002705          0
+tau1              0.190             0.149             0                 0                 0.149             0.149             0
+tau2              7.952             1.250             0                 0                 1.250             1.250             0
+erev              -70.0             -70.0             0                 0                 -70.0             -70.0             0
+-----------------------------------------------------------------------------------------------------------------------------------
+
+[1] Default value from GlyPSD
+
+[2] No evidence for tuberculo inputs to other d stellate cells or cartwheel cells.
+    Octopus cells do not get inhibitory input
+    
+[3] Guess
+
+[4] Default value
+
+[5] Guess
+
+[6] Mouse data
+    TV conductance onto pyr cells: 2.1 nS SD 2.9 nS (Kuo et al., 2012)
+    TV conductance onto TV cells: 1.8 ns SD 2.3 nS.
+
+[7] Parameters Weight, tau1, tau2, delay and erev from comare_simple_multisynapses run and curve fitting (all cells)
+    Fitting done against 200 rep average for bushy, 500 rep average for all others.
+
+""",
+)
+
+add_table_data(
+    "cartwheel_synapse",
+    row_key="field",
+    col_key="post_type",
+    species="mouse",
+    data=u"""
+
+Cartwheel cell synapse values
+gly_gmax is the default value in the program (scaled by Po for the receptors). See synapses/gly_psd.py
+IPSC_cv is the coefficient of variation of the IPSC. (Not currently used in the model)
+Pr is the release probabilty (not currently used)
+n_rsites is the number of release sites per cartwheel cell terminal.
+
+-----------------------------------------------------------------------------------------------------------------------------------
+             bushy             tstellate          dstellate         octopus         pyramidal      tuberculoventral  cartwheel
+                                                                                                                
+gly_gmax     0.0 [3]           0.0 [3]            0.0 [3]           0. [2]          2.1±2.9 [6]    0±0 [6]           1.8±2.3 [6]    
+IPSC_cv      0.3 [3]           0.3 [3]            0.3 [3]           0.3 [3]         1.0 [3]        0.3 [3]           0.3 [3]       
+Pr           1.000 [4]         1.000 [4]          1.000 [4]         1.000 [4]       1.000 [4]      1.000 [4]         1.000 [4]       
+n_rsites     6 [5]             6 [5]              0 [1]             0 [2]           6 [5]          6 [5]             6 [5]            
+delay        0 [7]             0                  0                 0               0              0                 0
+weight       0.01              0.01               0.01              0.0             0.01           0.01              0.01 
+tau1         0.3 [5]           0.3 [5]            0.3 [5]           0.3 [5]         0.3 [5]        0.3 [5]           0.3 [5]
+tau2         2.0 [5]           2.0 [5]            2.0 [5]           2.0 [5]         2.0 [5]        2.0 [5]           2.0 [5]
+erev         -70 [5]           -70 [5]            -70 [5]           -70 [5]         -70 [5]        -70 [5]           -70 [5]
+-----------------------------------------------------------------------------------------------------------------------------------
+
+[1] Default value from GlyPSD
+
+[2] No evidence for cartwheel inputs to Dstellate, bushy or tstellate cells.
+    Octopus cells do not get inhibitory input
+    
+[3] Guess
+
+[4] Default value
+
+[5] Guess
+
+[6] Mouse data
+    TV conductance onto pyr cells: 2.1 nS SD 2.9 nS (Kuo et al., 2012)
+    TV conductance onto TV cells: 1.8 ns SD 2.3 nS.
+
+[7] delay from pre to post; default is just 0
+
+""",
+)
+
+add_table_data(
+    "bushy_synapse",
+    row_key="field",
+    col_key="post_type",
+    species="mouse",
+    data=u"""
+
+AMPA_gmax and NMDA_gmax are the estimated average peak conductances (in nS) 
+resulting from an action potential in a single presynaptic terminal under 
+conditions that minimize the effects of short-term plasticity.
+AMPA_gmax are from values measured at -65 mV (or -70mV), and represent SINGLE TERMINAL 
+conductances
+AMPAR_gmax are the individual synapse postsynaptic conductance
+NMDA_gmax values are taken as the fraction of the current that is NMDAR dependent
+at +40 mV (see below)
+
+n_rsites is the number of release sites per terminal.
+
+-----------------------------------------------------------------------------------------------------------------------------------
+             mso           
+                          
+AMPA_gmax    21.05±15.4 [1]
+AMPAR_gmax   4.6516398 [2]
+NMDA_gmax    0 [3]  
+NMDAR_gmax   0 [3]
+EPSC_cv      0.12 [4]      
+Pr           1.000 [5]    
+n_rsites     36 [6]         
+weight       0.01
+delay        0
+-----------------------------------------------------------------------------------------------------------------------------------
+
+[1] Taken from the mouse bushy cell model.
+    Units are nS.
+    
+[2] See note [10] for the SGC-bushy synapse
+
+[3] Assume no NMDA receptors at this synapse
+
+[4] See SGC-bushy synapse
+
+[5] Just to scale with the multisite synapse model
+
+[6] This is a guess.
+
+""",
+)

cnmodel/data/tests/test_db.py

@@ -0,0 +1,72 @@
+# -*- encoding: utf-8 -*-
+import pytest
+import sys
+from cnmodel import data
+
+
+table = u"""
+
+Description of data
+It has multiple lines
+
+And empty lines.
+
+-------------------------------------------------------------------------------
+          col1         col2           col3
+param1    15±6.5 [1]   2.2±1.5 [2]    0.87±0.23 [3]
+param2    3            5              0.87±0.23 [3]
+param3      3.4                       7 [2]
+param4    1 [12]           
+-------------------------------------------------------------------------------
+
+[1] Source 1
+[2] Multiline source
+        #2
+    end of #2
+[3] Multiline source
+        #3
+    end of #3
+
+[12] another
+     source
+
+
+"""
+
+data.add_table_data(
+    "test_data", row_key="param", col_key="col", data=table, extra="test_kwd"
+)
+
+
+def test_db():
+    # this should only be a problem with Python 2, so we need to
+    # check which version we are running under before letting the test
+    # throw the exception:
+    if sys.version_info[0] == 2:
+        with pytest.raises(TypeError):
+            # raise exception if unicode is given in ono-unicode string
+            data.add_table_data("test_data", row_key="param", col_key="col", data=u"±")
+
+    d = data.get("test_data", param="param1", col="col2", extra="test_kwd")
+    assert d == (2.2, 1.5)
+
+    d = data.get(
+        "test_data", param="param1", col=["col1", "col2", "col3"], extra="test_kwd"
+    )
+    assert d == {"col1": (15, 6.5), "col2": (2.2, 1.5), "col3": (0.87, 0.23)}
+
+    d = data.get(
+        "test_data", param="param2", col=["col1", "col2", "col3"], extra="test_kwd"
+    )
+    assert d == {"col1": 3, "col2": 5, "col3": (0.87, 0.23)}
+
+    d = data.get(
+        "test_data", param="param3", col=["col1", "col2", "col3"], extra="test_kwd"
+    )
+    assert d == {"col1": 3.4, "col2": None, "col3": 7}
+
+    s = data.get_source("test_data", param="param1", col="col2", extra="test_kwd")
+    assert "end of #2" in s
+
+    s = data.get_source("test_data", param="param2", col="col2", extra="test_kwd")
+    assert s is None

cnmodel/decorator/__init__.py

@@ -0,0 +1,9 @@
+#!/usr/bin/python
+#
+# Channel decorator class for cnmodel
+#
+# Paul B. Manis, Ph.D. January 2016
+#
+from neuron import h
+
+from .decorator import Decorator

cnmodel/decorator/decorator.py

@@ -0,0 +1,392 @@
+from __future__ import print_function
+
+__author__ = "pbmanis"
+
+"""
+Decorator:
+A class to insert biophysical mechanisms into a model.
+This function attempts to automatically decorate a hoc-imported model set of sections
+with appropriate conductances.
+
+The class takes as input the object hf, which is an instance of morphology 
+It also takes the cellType, a string that directs how conductances should be inserted.
+
+"""
+
+import string
+import numpy as np
+import cnmodel.util as nu
+from cnmodel.util import Params
+
+
+class Decorator:
+    def __init__(self, cell, parMap=None, verify=False):
+
+        cellType = cell.type.lower()
+        self.channelInfo = Params(
+            newCm=1.0,
+            newRa=150.0,  # standard value
+            newg_leak=0.000004935,
+            eK_def=-85,
+            eNa_def=50,
+            ca_init=70e-6,  # free calcium in molar
+            v_init=-80,  # mV
+            pharmManip={
+                "TTX": False,
+                "ZD": False,
+                "Cd": False,
+                "DTX": False,
+                "TEA": False,
+                "XE": False,
+            },
+            cellType=cell.status["cellClass"],
+            modelType=cell.status["modelType"],
+            modelName=cell.status["modelName"],
+            distanceMap=cell.hr.distanceMap,
+            parMap=parMap,
+        )
+        self.excludeMechs = []  # ['ihvcn', 'kht', 'klt', 'nav11']
+        print("modelType in dec: ", cell.status["modelType"])
+        print("modelName in dec is ", cell.status["modelName"])
+        print("cell type in dec: ", cellType)
+        cell.channel_manager(
+            modelName=cell.status["modelName"], modelType=cell.status["modelType"]
+        )
+        #        print 'Cell: \n', dir(cell)
+        #        print 'mechanisms: ', cell.hr.mechanisms
+        # gmapper allows us tor remap the names of mechanisms and their conductance names, which may
+        # vary in the mod files.
+        # The versions in the mechanisms directory here have been systematized, but this
+        # dictionary may help when adding other conductances.
+
+        self.gbar_mapper = {
+            "nacn": "gbar",
+            "kht": "gbar",
+            "klt": "gbar",
+            "leak": "gbar",
+            "ihvcn": "gbar",
+            "jsrna": "gbar",
+            "nav11": "gbar",
+            "nacncoop": "gbar",
+            "hcnobo": "gbar",
+        }
+        self.erev_mapper = {
+            "nacn": "ena",
+            "kht": "ek",
+            "klt": "ek",
+            "leak": "erev",
+            "ihvcn": "eh",
+            "jsrna": "ena",
+            "nav11": "ena",
+            "nacncoop": "ena",
+            "hcnobo": "eh",
+        }
+        self.vshift_mapper = {
+            "nacn": None,
+            "kht": None,
+            "klt": None,
+            "leak": None,
+            "ihvcn": None,
+            "jsrna": None,
+            "nav11": "vsna",
+            "nacncoop": "vsna",
+            "hcnobo": None,
+        }
+        self._biophys(cell, verify=verify)
+        print(
+            "\033[1;31;40m Decorator: Model Decorated with channels (if this appears more than once per cell, there is a problem)\033[0m"
+        )
+
+    def _biophys(self, cell, verify=False):
+        """
+        Inputs: run parameter structure, model parameter structure
+        verify = True to run through the inserted mechanisms and see that they are really there.
+        Outputs: None
+        Action: Channel insertion into model
+        Side Effects:
+            Sets conductances in every different kind of section
+            Does not update any class variables (via self).
+
+        original hoc code: Paul B. Manis, Ph.D.
+        25 Sept. 2007
+        Modified to use gca for HH formulation of calcium current
+        14 Oct 2007
+        converted for Python, 17 Oct 2012 (PB Manis)
+        modified to use new hf hoc_reader class to access section types and mechanisms 10-14 Feb 2014 pbmanis
+        """
+        # check to see if we already did this
+        # createFlag = False
+        cellType = self.channelInfo.cellType
+        parMap = self.channelInfo.parMap
+        dmap = self.channelInfo.distanceMap
+        if self.channelInfo is None:
+            raise Exception("biophys - no parameters or info passed!")
+        if verify:
+            print(
+                "Biophys: Inserting channels as if cell type is {:s} with modelType {:s}".format(
+                    cellType, self.channelInfo.modelType
+                )
+            )
+
+        cell.hr.mechanisms = []
+        for s in list(cell.hr.sec_groups.keys()):
+            sectype = self.remapSectionType(s.rsplit("[")[0])
+            if sectype not in cell.channelMap.keys():
+                print(
+                    "encountered unknown section group type: %s  Not decorating"
+                    % sectype
+                )
+                print("channels in map: ", cell.channelMap.keys())
+                continue
+            # print 'Biophys: Section type: ', sectype, 'from: ', s
+            # print sectype
+            # print 'channel mapping keys: ', cell.channelMap[sectype].keys()
+
+            # here we go through all themechanisms in the ionchannels table for this cell and compartment type
+            # note that a mechanism may have multiple parameters in the table (gbar, vshft), so we:
+            #   a. only insert the mechanism once
+            #   b. only adjust the relevant parameter
+
+            for mechname in list(cell.channelMap[sectype].keys()):
+                mech = mechname.split("_")[0]  # get the part before the _
+                parameter = mechname.split("_")[1]  # and the part after
+                if mech not in self.gbar_mapper.keys():
+                    print("Mechanism %s not found? " % mech)
+                    continue
+                if mech in self.excludeMechs:
+                    continue
+                if verify:
+                    print(
+                        "Biophys: section group: {:s}  insert mechanism: {:s} at {:.8f}".format(
+                            s, mech, cell.channelMap[sectype][mech]
+                        )
+                    )
+                if mech not in cell.hr.mechanisms:
+                    cell.hr.mechanisms.append(
+                        mech
+                    )  # just add the mechanism to our list
+                x = nu.Mechanism(mech)
+                if cell.hr.sec_groups[s] == set():
+                    continue  # no sections of this type
+                for sec in cell.hr.sec_groups[
+                    s
+                ]:  # insert into all the sections of this type (group)
+                    try:
+                        x.insert_into(cell.hr.get_section(sec))
+                    except:
+                        raise ValueError(
+                            "Failed with mech: %s " % mech
+                        )  # fail if cannot insert.
+                    if verify:
+                        print("   inserted %s into section " % mech, sec)
+
+                gbar_setup = None
+                gbar = 0.0
+                if parameter == "gbar":
+                    gbar = self.gbarAdjust(
+                        cell, sectype, mechname, sec
+                    )  # map density by location/distance
+                    gbar_setup = "%s_%s" % (
+                        self.gbar_mapper[mech],
+                        mech,
+                    )  # map name into .mod file name
+                    # if parMap is not None and mech in parMap.keys():  # note, this allows parmap to have elements BESIDES mechanisms
+                    #     if verify:
+                    #         print 'parMap[mech]', mech, parMap[mech], gbar,
+                    #     gbar = gbar * parMap[mech]  # change gbar here...
+                    #     if verify:
+                    print("####### new gbar: ", gbar)
+
+                vshift_setup = None
+                vshift = 0.0
+                # print 'Parameter: ', parameter, mech, self.vshift_mapper[mech]
+                if parameter == "vshift" and self.vshift_mapper[mech] is not None:
+                    vshift_setup = "%s_%s" % (
+                        self.vshift_mapper[mech],
+                        mech,
+                    )  # map voltage shift
+                    vshift = cell.channelMap[sectype][mechname]
+                    print(
+                        "*********   mech: gbar, vshift: ", gbar, vshift, vshift_setup
+                    )
+                    exit()
+
+                cell.hr.h.Ra = self.channelInfo.newRa
+                for sec in cell.hr.sec_groups[
+                    s
+                ]:  # now set conductances and other parameters as requested
+                    cell.hr.get_section(sec).Ra = self.channelInfo.newRa  # set Ra here
+                    if gbar_setup is not None:
+                        setattr(
+                            cell.hr.get_section(sec), gbar_setup, gbar
+                        )  # set conductance magnitude
+                    #   print('gbar_setup: %s %s' % (sectype, gbar_setup), gbar)
+                    if vshift_setup is not None:
+                        try:
+                            setattr(
+                                cell.hr.get_section(sec), vshift_setup, vshift
+                            )  # set conductance magnitude
+                        except:
+                            print(dir(cell.hr.get_section(sec)))
+                            raise ValueError(
+                                " cannot set mechanism attribute %s  ... %s "
+                                % (vshift_setup, vshift)
+                            )
+                        # print('vshift_setup: %s %s' % (sectype, vshift_setup), vshift)
+
+                    if hasattr(
+                        cell, "channelErevMap"
+                    ):  # may not always have this mapping
+                        secobj = cell.hr.get_section(
+                            sec
+                        )  # get the NEURON section object
+                        mechsinsec = cell.get_mechs(
+                            secobj
+                        )  # get list of mechanisms in this section
+                        if (
+                            mech in mechsinsec
+                        ):  # confirm that the mechanism is really there
+                            setrev = (
+                                False
+                            )  # We try two ways for different mechanisms - just flag it
+                            try:
+                                setattr(
+                                    secobj,
+                                    self.erev_mapper[mech],
+                                    cell.channelErevMap[sectype][mech],
+                                )
+                                setrev = True
+                                continue  # don't bother with second approach
+                            except:
+                                raise ValueError("erev set failed")
+                                pass  # no error
+                            try:
+                                setattr(
+                                    secobj(),
+                                    self.erev_mapper[mech] + "_" + mech,
+                                    cell.channelErevMap[sectype][mech],
+                                )
+                                setrev = True
+                            except:
+                                raise ValueError("Erev2 set failed")
+                                pass  # no error report
+                            if (
+                                not setrev
+                            ):  # here is our error report - soft, not crash.
+                                print(
+                                    "Failed to set reversal potential in section %s for mechanism %s"
+                                    % (sec, mech)
+                                )
+                        # if mech in mechinsec and mech in self.vshift_mapper.keys():
+                        #     try:
+                        #         setattr(secobj, self.vshift_mapper[mech], cell.channelVshiftMap[sectype][mech])
+                        #     except:
+                        #         raise ValueError('Failed to set vshift for mech: %s  sectpe: %s' % (mech, sectype[mech]))
+
+        if verify:
+            self.channelValidate(cell)
+        return cell
+
+    def gbarAdjust(self, cell, sectype, mech, sec):
+        gbar = cell.channelMap[sectype][mech]
+        gbar_orig = gbar
+        if sectype not in cell.distMap.keys():  # no map for this section type
+            return gbar
+        elif mech not in cell.distMap[sectype].keys():
+            return gbar
+        # mecanism exists in the distMap, so we will map gbar to distance from soma
+        method = cell.distMap[sectype][mech]["gradient"]  # grab the type
+        gminf = cell.distMap[sectype][mech]["gminf"]
+        rate = cell.distMap[sectype][mech]["lambda"]
+        #  print('sectype: %s  mech: %s method: %s  rate: %s' % (sectype, mech, method, rate))
+        if method == "flat":
+            return gbar
+        if sec in self.channelInfo.distanceMap.keys():
+            dist = self.channelInfo.distanceMap[sec]
+        else:  # the sec should be in the map, but there could be a coding error that would break that relationship
+            raise NameError(
+                "gbarAdjust in channel_decorate.py: section %s not in distance map"
+                % sec
+            )
+        if method == "linear":  # rate is "half" point drop
+            # print('doing linear, orig gbar: ', gbar)
+            gbar = gbar - dist * (gbar - gminf) / rate
+            if gbar < 0.0:
+                gbar = 0.0  # clip
+        #    print('sec dist: %f  final gbar: %f' % (dist, gbar))
+        elif method in ["exp", "expdown"]:
+            gbar = (gbar - gminf) * np.exp(-dist / rate) + gminf
+        if gbar < 0.0:
+            gbar = 0.0
+        # print 'gbaradjust: orig/adj: ', gbar_orig, gbar, method, dist, sectype
+        return gbar
+
+    def channelValidate(self, cell, verify=False):
+        """
+         verify mechanisms insertions -
+         go through all the groups, and find inserted conductances and their values
+         print the results to the terminal
+        """
+        print("\nChannel Validation")
+        print("  Looking for sec_groups: ", sorted(cell.hr.sec_groups.keys()))
+        print("  Available Channel Maps: ", sorted(cell.channelMap.keys()))
+        secstuff = {}
+        for s in list(cell.hr.sec_groups.keys()):
+            sectype = self.remapSectionType(s.rsplit("[")[0])
+            if sectype not in cell.channelMap.keys():
+                if sectype in ["undefined"]:  # skip undefined sections
+                    continue
+                print(
+                    "\033[1;31;40m Validation: encountered unknown section group type: %s  Cannot Validate"
+                    % sectype
+                )
+                print("Cell morphology file: %s \033[0m" % cell.morphology_file)
+                continue
+            #            print 'Validating Section: %s' % s
+            for mech in list(cell.channelMap[sectype].keys()):
+                if mech not in self.gbar_mapper.keys():
+                    continue
+                if mech in self.excludeMechs:
+                    continue
+                if verify:
+                    print(
+                        "\tSection: %-15ss  found mechanism: %-8ss at %.5f"
+                        % (s, mech, cell.channelMap[sectype][mech])
+                    )
+                x = nu.Mechanism(
+                    mech
+                )  # , {gmapper[mech]: self.channelMap[cellType][sectype][mech]})
+                setup = "%s_%s" % (self.gbar_mapper[mech], mech)
+                for sec in cell.hr.sec_groups[s]:
+                    bar = getattr(cell.hr.get_section(sec), setup)
+                    # print 'mech', mech
+                    # print 'bar: ', bar
+                    try:
+                        Erev = getattr(cell.hr.get_section(sec), self.erev_mapper[mech])
+                    except:
+                        Erev = -999.0
+                    # print 'erev: ', Erev
+                    if sec in secstuff.keys():
+                        secstuff[sec] += ", g_%s = %g [%.1f]" % (mech, bar, Erev)
+                    else:
+                        secstuff[sec] = "(%10s) g_%-6s = %g [%.1f] " % (
+                            sectype,
+                            mech,
+                            bar,
+                            Erev,
+                        )
+        if verify:
+            for i, k in enumerate(secstuff.keys()):
+                print("**%-20s " % k, secstuff[k])
+
+    def remapSectionType(self, sectype):
+        if sectype in ["AXON_0"]:
+            sectype = "axon"
+
+        if sectype in ["initseg", "initialsegment"]:
+            sectype = "initialsegment"
+        if sectype in ["dendscaled_0", "dendscaled_1", "dendscaled_2", "dendrite"]:
+            sectype = "dendrite"
+        if sectype in ["apical_dendrite"]:
+            sectype = "secondarydendrite"
+        return sectype

cnmodel/mechanisms/CaPCalyx.mod

@@ -0,0 +1,109 @@
+TITLE CaPCalyx.mod  The presynaptic calcium current at the MNTB calyx of Held
+
+COMMENT
+
+NEURON implementation of Calcium current from Borst and Sakmann, 1998.
+Equations are basic HH; parameters are taken from Figure 8 legend in that paper.
+
+Original implementation by Paul B. Manis, October 2007
+
+Contact: pmanis@med.unc.edu
+
+Modified 2/2014: Use Derivative block format.
+
+ENDCOMMENT
+
+UNITS {
+        (mA) = (milliamp)
+        (mV) = (millivolt)
+        (nA) = (nanoamp)
+}
+
+NEURON {
+THREADSAFE
+    SUFFIX CaPCalyx
+    USEION ca READ eca WRITE ica
+    RANGE gbar, gcap, ica
+    GLOBAL minf, taum, alpha, beta
+}
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+PARAMETER {
+        v (mV)
+:        celsius = 22 (degC)  : model is defined on measurements made at 23-24 deg in Germany
+        celsius (degC)
+        dt (ms)
+:        eca = 43.9 (mV)
+        eca (mV)
+        gbar = 0.01 (mho/cm2) <0,1e9> : target is 48.9 nS total
+        alpha (1/ms)
+        beta (1/ms)
+}
+
+STATE {
+        m
+}
+
+ASSIGNED {
+    ica (mA/cm2)
+    gcap (mho/cm2)
+    minf (1)
+    taum (ms)
+
+}
+
+LOCAL mexp
+
+BREAKPOINT {
+    SOLVE states METHOD cnexp
+
+    gcap = gbar*m*m
+    ica = gcap*(v - eca)
+}
+
+UNITSOFF
+
+INITIAL {
+    trates(v)
+    m = minf
+}
+
+DERIVATIVE states {  : Computes state variables m
+    rates(v)         : at the current v and dt.
+    m' = (minf - m)/taum
+    :m = m + mexp*(minf-m)
+:VERBATIM
+:	return 0;
+:ENDVERBATIM
+}
+
+LOCAL q10
+
+PROCEDURE rates(v) {  :Computes rate and other constants at current v.
+                      :Call once from HOC to initialize inf at resting v.
+
+    q10 = 3^((celsius - 24)/10) : if you don't like room temp, it can be changed!
+
+    minf = (1 / (1 + exp(-(v + 23.2) / 9.1)))
+
+    alpha = 1.78*exp(v/23.3)
+    beta = 0.140*exp(-v/15.0)
+    taum = 1/(alpha + beta)
+}
+
+PROCEDURE trates(v) {  :Computes rate and other constants at current v.
+                      :Call once from HOC to initialize inf at resting v.
+    LOCAL minc
+    TABLE minf, mexp
+    DEPEND dt, celsius FROM -150 TO 150 WITH 300
+
+    rates(v)    : not consistently executed from here if usetable_hh == 1
+        : so don't expect the tau values to be tracking along with
+        : the inf values in hoc
+
+    minc = -dt * q10
+    mexp = 1 - exp(minc/taum)
+}
+
+UNITSON

cnmodel/mechanisms/Gly5GC.mod

@@ -0,0 +1,134 @@
+TITLE detailed model of Glycine receptors
+
+COMMENT
+-----------------------------------------------------------------------------
+
+    Kinetic model of Glycine-A receptors
+    ====================================
+
+    C -- C1 -- C2 -- O1
+          |     |
+         D1 -- D2 -- D3
+
+-----------------------------------------------------------------------------
+
+    This Model is based on:
+    Gentet LJ, Clements JD Binding site stoichiometry and the effects of
+    phosphorylation on human alpha1 homomeric glycine receptors J Physiol (Lond)
+    2002 vol. 544 (Pt 1) pp. 97-106, Figure 7.
+
+    Written by Paul Manis, UNC Chapel Hill, 2009
+    Kinetic values are estimated from VCN glycine receptors.
+
+    This model has desensitization states.
+
+-----------------------------------------------------------------------------
+
+    This mod file does not include mechanisms for the release and time course
+    of transmitter; it is to be used in conjunction with a sepearate mechanism
+    to describe the release of transmitter and that provides the concentration
+    of transmitter in the synaptic cleft (to be connected to pointer C here).
+
+-----------------------------------------------------------------------------
+
+ENDCOMMENT
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+NEURON {
+	POINT_PROCESS GLYaGC
+	POINTER XMTR
+	RANGE C0, C1, C2, D1, D2, D3, O1,  Open
+	RANGE g, gmax, f1, f2
+	RANGE Erev
+    RANGE  k1, km1, a1, b1, d1, r1, d2, r2, d3, r3, rd, dd
+	NONSPECIFIC_CURRENT i
+}
+
+UNITS {
+	(nA) = (nanoamp)
+	(mV) = (millivolt)
+	(pS) = (picosiemens)
+	(umho) = (micromho)
+	(mM) = (milli/liter)
+	(uM) = (micro/liter)
+}
+
+PARAMETER {
+
+	Erev	= -70  (mV)	: reversal potential
+	gmax	= 500  (pS)	: maximal conductance
+
+: Rates
+: bushy cell
+
+	k1	= 12.81	(/uM /ms)	: binding
+	km1 = 0.0087	(/ms)	: unbinding
+	a1	= 0.0194	(/ms)	: opening
+	b1	= 1.138	(/ms)	: closing
+	r1	= 5.19	(/ms)	: desense 1
+	d1	= 0.000462  (/ms)	: return from d1
+    r2 = 0.731 (/ms)     : return from deep state
+    d2 = 1.641 (/ms)     : going to deep state
+    r3 = 3.817 (/ms)     : return from deep state
+    d3 = 1.806 (/ms)     : going to deep state
+    rd = 1.0 (/ms)
+    dd = 1.0 (/ms)
+}
+
+
+ASSIGNED {
+	v		(mV)		: postsynaptic voltage
+	i 		(nA)		: current = g*(v - Erev)
+	g 		(pS)		: conductance
+	XMTR 	(mM)		: pointer to glycine concentration
+
+	f1		(/ms)    : binding
+	f2		(/ms)    : binding
+    Open    (1)
+}
+
+STATE {
+	: Channel states (all fractions)
+	C0		: unbound
+	C1		: single bound
+	C2		: double bound
+    D1      : desense, bound
+	O1		: open
+	D2		: Desense
+    D3      : Desense
+}
+
+INITIAL {
+	XMTR = 0
+    C0 = 1
+	C1 = 0
+	C2 = 0
+	O1 = 0
+	D1 = 0
+    D2 = 0
+    D3 = 0
+}
+
+BREAKPOINT {
+	SOLVE kstates METHOD sparse
+    Open = (O1)
+	g = gmax * Open
+	i = (1e-6) * g * (v - Erev)
+}
+
+KINETIC kstates {
+
+	f1 = k1 * (1e3) * XMTR
+	f2 = k1 * (1e3) * XMTR
+
+	~ C0 <-> C1	(f1,km1)
+	~ C1 <-> C2	(f2,12.5*km1)
+	~ C2 <-> O1	(a1,b1)
+    ~ C1 <-> D1 (r1, d1)
+	~ C2 <-> D2	(r2, d2)
+    ~ D1 <-> D2 (rd, dd)
+    ~ D2 <-> D3 (r3, d3)
+
+	CONSERVE C0+C1+C2+D1+D2+D3+O1 = 1
+}

cnmodel/mechanisms/Gly5PL.mod

@@ -0,0 +1,149 @@
+TITLE detailed model of Glycine receptors
+
+COMMENT
+-----------------------------------------------------------------------------
+
+    Kinetic model of Glycine-A receptors: Pascal Legendre (Mauthner Cell)
+    ====================================
+
+
+
+    C0--C1--C2--O1
+            |
+            C3--O2
+
+-----------------------------------------------------------------------------
+
+
+    This mod file does not include mechanisms for the release and time course
+    of transmitter; it is to be used in conjunction with a sepearate mechanism
+    to describe the release of transmitter and that provides the concentration
+    of transmitter in the synaptic cleft (to be connected to pointer C here).
+
+
+-----------------------------------------------------------------------------
+
+    Modified Paul Manis, UNC Chapel Hill, 2009
+    Name, pointer name, kinetics are range variables, and kinetic values
+    are estimated from VCN glycine receptors.
+
+    Note: This model does not have a desensitization state.
+
+-----------------------------------------------------------------------------
+
+ENDCOMMENT
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+NEURON {
+	POINT_PROCESS GLYaPL
+	POINTER XMTR
+	RANGE C0, C1, C2, C3, O1, O2, Open
+	RANGE g, gmax, f1, f2
+	RANGE Erev
+    RANGE  kon, koff, a1, b1, a2, b2, r, d
+    RANGE CellType : 0 for bushy, 1 for stellate
+	NONSPECIFIC_CURRENT i
+}
+
+UNITS {
+	(nA) = (nanoamp)
+	(mV) = (millivolt)
+	(pS) = (picosiemens)
+	(umho) = (micromho)
+	(mM) = (milli/liter)
+	(uM) = (micro/liter)
+}
+
+PARAMETER {
+
+	Erev	= -70  (mV)	: reversal potential
+	gmax	= 500  (pS)	: maximal conductance
+    CellType = 1 (1) : define cell type parameters
+
+: Rates
+
+: Stellate cell fit (1/1/10; excellent fit)
+
+:	kon	= 0.0236	(/uM /ms)	: binding
+:	koff = 2.4	(/ms)	: unbinding
+:	a1	= 1.707 (/ms)	: opening
+:	b1	= 8.95	(/ms)	: closing
+:	a2	= 0.325	(/ms)	: opening
+:	b2	= 5.871  (/ms)	: closing
+:    r = 2.019 (/ms)     : return from deep state
+:    d = 28.87 (/ms)     : going to deep state
+
+:if psdtype == 'glyfast':  fit from 3/5/2010. error =  0.174 maxopen = 0.0385
+: See synapses.py
+    a1 =        1.000476 (/ms) : opening
+    a2 =        0.137903 (/ms) : opening
+    b1 =        1.700306 (/ms) : closing
+    koff =     13.143132 (/ms) : unbinding
+    kon =       0.038634 (/ms) : binding
+    r =         0.842504 (/ms) : return from deep state
+    b2 =        8.051435 (/ms) : closing
+    d =        12.821820 (/ms) : going to deep state
+
+}
+
+
+ASSIGNED {
+	v		(mV)		: postsynaptic voltage
+	i 		(nA)		: current = g*(v - Erev)
+	g 		(pS)		: conductance
+	XMTR 	(mM)		: pointer to glycine concentration
+
+	f1		(/ms)    : binding
+	f2		(/ms)    : binding
+    koff2   (/ms)
+    Open    (1)
+}
+
+STATE {
+	: Channel states (all fractions)
+	C0		: unbound
+	C1		: single bound
+	C2		: double bound
+    C3      : bound but closed state to O2
+	O1		: open
+	O2		: open
+}
+
+INITIAL {
+
+    XMTR = 0.0
+	C0 = 1
+	C1 = 0
+	C2 = 0
+    C3 = 0
+	O1 = 0
+	O2 = 0
+}
+
+BREAKPOINT {
+	SOLVE kstates METHOD sparse
+:VERBATIM
+:   if (CGly > 0.0) {
+:      fprintf(stderr, "t = %f   Xmtr = %f\n", t, XMTR);
+:   }
+:   ENDVERBATIM
+    Open = (O1 + O2)
+	g = gmax * Open
+	i = (1e-6) * g * (v - Erev)
+}
+
+KINETIC kstates {
+
+	f1 = 2.0 * kon * (1e3) * XMTR
+	f2 = kon * (1e3) * XMTR
+    koff2 = 2.0 * koff
+
+	~ C0 <-> C1	(f1,koff)
+	~ C1 <-> C2	(f2,koff2)
+	~ C2 <-> O1	(a1,b1)
+    ~ C2 <-> C3 (d, r)
+	~ C3 <-> O2	(a2,b2)
+
+	CONSERVE C0+C1+C2+C3+O1+O2 = 1
+}

cnmodel/mechanisms/Gly5State.mod

@@ -0,0 +1,147 @@
+TITLE detailed model of Glycine receptors
+
+COMMENT
+-----------------------------------------------------------------------------
+
+    Kinetic model of Glycine-A receptors
+    ====================================
+
+
+
+    C -- C1 -- C2
+         |     |
+         O1    O2
+
+-----------------------------------------------------------------------------
+
+
+    This mod file does not include mechanisms for the release and time course
+    of transmitter; it is to be used in conjunction with a sepearate mechanism
+    to describe the release of transmitter and that provides the concentration
+    of transmitter in the synaptic cleft (to be connected to pointer C here).
+
+-----------------------------------------------------------------------------
+
+    Based on models
+
+    Destexhe, A., Mainen, Z.F. and Sejnowski, T.J.  Kinetic models of
+    synaptic transmission.  In: Methods in Neuronal Modeling (2nd edition;
+    edited by Koch, C. and Segev, I.), MIT press, Cambridge, 1998, pp. 1-25.
+
+    (electronic copy available at http://cns.iaf.cnrs-gif.fr)
+
+    Written by Alain Destexhe, Laval University, 1995
+
+-----------------------------------------------------------------------------
+
+    Modified Paul Manis, UNC Chapel Hill, 2009
+    Changed name, pointer name, kinetics are range variables, and kinetic values
+    are estimated from VCN glycine receptors.
+
+    This model does not have a desensitization state.
+
+-----------------------------------------------------------------------------
+
+ENDCOMMENT
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+NEURON {
+	POINT_PROCESS GLYa5
+	POINTER XMTR
+	RANGE C0, C1, C2, O1, O2, Open
+	RANGE g, gmax, f1, f2
+	RANGE Erev
+    RANGE  kf1, kf2, kb1, kb2, a1, b1, a2, b2
+	NONSPECIFIC_CURRENT i
+}
+
+UNITS {
+	(nA) = (nanoamp)
+	(mV) = (millivolt)
+	(pS) = (picosiemens)
+	(umho) = (micromho)
+	(mM) = (milli/liter)
+	(uM) = (micro/liter)
+}
+
+PARAMETER {
+
+	Erev	= -70  (mV)	: reversal potential
+	gmax	= 500  (pS)	: maximal conductance
+
+: Rates
+
+: from fits to averaged ipsc data, stellate cells 1/1/10
+
+:    kf1	= 0.002930   (/uM /ms)	: binding
+:	kf2	= 0.005936   (/uM /ms)	: binding
+:	kb1	= 2.793	(/ms)	: unbinding
+:	kb2	= 1.445	(/ms)	: unbinding
+:	a1	= 1e-6	(/ms)	: opening
+:	b1	= 129.0	(/ms)	: closing
+:	a2	= 5.10	(/ms)	: opening
+:	b2	= 2.79  (/ms)	: closing
+
+: from fits to averaged ipsc data, bushy cells 1/1/10
+
+	kf1	= 0.0278   (/uM /ms)	: binding
+	kf2	= 1e-6   (/uM /ms)	: binding
+	kb1	= 0.000054	(/ms)	: unbinding
+	kb2	= 0.000855	(/ms)	: unbinding
+	a1	= 1e-6	(/ms)	: opening
+	b1	= 129.0	(/ms)	: closing
+	a2	= 5.10	(/ms)	: opening
+	b2	= 2.79  (/ms)	: closing
+
+}
+
+
+ASSIGNED {
+	v		(mV)		: postsynaptic voltage
+	i 		(nA)		: current = g*(v - Erev)
+	g 		(pS)		: conductance
+	XMTR 	(mM)		: pointer to glycine concentration
+
+	f1		(/ms)    : binding
+	f2		(/ms)    : binding
+    Open    (1)
+}
+
+STATE {
+	: Channel states (all fractions)
+	C0		: unbound
+	C1		: single bound
+	C2		: double bound
+	O1		: open
+	O2		: open
+}
+
+INITIAL {
+	C0 = 1
+	C1 = 0
+	C2 = 0
+	O1 = 0
+	O2 = 0
+    XMTR = 0.0
+}
+
+BREAKPOINT {
+	SOLVE kstates METHOD sparse
+    Open = (O1 + O2)
+	g = gmax * Open
+	i = (1e-6) * g * (v - Erev)
+}
+
+KINETIC kstates {
+
+	f1 = kf1 * (1e3) * XMTR
+	f2 = kf2 * (1e3) * XMTR
+
+	~ C0 <-> C1	(f1,kb1)
+	~ C1 <-> C2	(f2,kb2)
+	~ C1 <-> O1	(a1,b1)
+	~ C2 <-> O2	(a2,b2)
+
+	CONSERVE C0+C1+C2+O1+O2 = 1
+}

cnmodel/mechanisms/Gly6S.mod

@@ -0,0 +1,131 @@
+TITLE Model of glycine receptors
+
+COMMENT
+-----------------------------------------------------------------------------
+
+    Kinetic model of glycine receptors
+    ===============================
+
+    6-state gating model with
+    2 open states provide dual exponential response.
+
+                O1
+                |
+    C0 -- C1 -- C2 -- O2
+                |
+                D1
+
+-----------------------------------------------------------------------------
+
+    This mod file does not include mechanisms for the release and time course
+    of transmitter; it is to be used in conjunction with a sepearate mechanism
+    to describe the release of transmitter and that provides the concentration
+    of transmitter in the synaptic cleft (to be connected to pointer C here).
+
+    Default parameters are set for a miniature EPSC.
+
+
+-----------------------------------------------------------------------------
+
+Paul B. Manis, Ph.D. 28 Dec 2009
+
+-----------------------------------------------------------------------------
+ENDCOMMENT
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+NEURON {
+    POINT_PROCESS Gly6S
+    POINTER XMTR
+    RANGE C0, C1, C2, D1, O1, O2, Open
+    RANGE Erev
+    RANGE Rb, Ru1, Ru2, Rd, Rr, Ro1, Rc1, Ro2, Rc2
+    RANGE g, rb, gmax
+    RANGE CellType : 0 is bushy, 1 is stellate
+    NONSPECIFIC_CURRENT i
+}
+
+UNITS {
+    (nA) = (nanoamp)
+    (mV) = (millivolt)
+    (pS) = (picosiemens)
+    (umho) = (micromho)
+    (mM) = (milli/liter)
+    (uM) = (micro/liter)
+}
+
+PARAMETER {
+
+    Erev     = -70    (mV) : reversal potential
+    gmax     = 500  (pS)  : maximal conductance
+    CellType = 0 (1)   : Cell type definition.
+: Rates
+: Bushy cell IPSCs: (rates can be changed externally)
+: Set per Fits 8 March 2010 (see Synapses.py as well)
+    Rd  = 1.177999   (/ms)  : desensitization
+    Rr  = 0.000005 (/ms)    : resensitization
+    Rb  = 0.009403   (/mM /ms): binding
+                : diffusion limited
+    Ru2 = 0.000086  (/ms)    : unbinding (2nd site)
+    Ro1 = 0.187858   (/ms)  : opening (fast)
+    Ro2 = 1.064426    (/ms)   : opening (slow)
+    Ru1 = 0.028696  (/ms)    : unbinding (1st site)
+    Rc1 = 0.103625    (/ms)  : closing
+    Rc2 = 1.730578    (/ms) : closing
+
+}
+
+ASSIGNED {
+    v       (mV)    : postsynaptic voltage
+    i       (nA)    : current = g*(v - Erev)
+    g       (pS)    : conductance
+    XMTR    (mM)    : pointer to glutamate concentration
+    rbind   (/ms)   : binding
+    Open    (1)
+}
+
+STATE {
+    : Channel states (all fractions)
+    C0      : unbound
+    C1      : single gly bound
+    C2      : double gly bound
+    D1       : double gly bound, desensitized
+    O1      : double gly bound, open state 1
+    O2      : double gly bound, open state 2
+}
+
+INITIAL {
+
+    XMTR = 0
+	C0 = 1
+    C1 = 0
+    C2 = 0
+    D1  = 0
+    O1 = 0
+    O2 = 0
+}
+
+BREAKPOINT {
+    SOLVE kstates METHOD sparse
+:VERBATIM
+:   if (XMTR > 0.1) {
+:      fprintf(stderr, "t = %f   XMTR = %f\n", t, XMTR);
+:   }
+:   ENDVERBATIM
+    Open = (O1 + O2)
+    g = gmax * Open
+	i = (1e-6)*g*(v-Erev)
+}
+
+
+KINETIC kstates {
+
+    rbind = Rb * (1e3) * XMTR
+
+    ~ C0 <-> C1 (rbind,Ru1)
+    ~ C1 <-> C2 (rbind*2.0,Ru2)
+    ~ C2 <-> O1 (Ro1,Rc1)
+    ~ C2 <-> D1  (Rd,Rr)
+    ~ C2 <-> O2 (Ro2,Rc2)
+    CONSERVE C0+C1+C2+D1+O1+O2 = 1
+}

cnmodel/mechanisms/Iclamp2.mod

@@ -0,0 +1,64 @@
+COMMENT
+
+Iclamp2.mod: Electrode current injection, revised for our own use
+I specifically needed more step changes in a single waveform than 
+were provided by the default code. This routine gives us 5 levels:
+holding, 3 steps, and a final (holding?) level.
+
+This version generates a pulse of duration at amplitude I for
+onset times in the vector onset[STEP] and durations in dur[STEP]
+Each pulse is independent and pulses _could_ overlap.
+
+Since this is an electrode current, positive values of I depolarize the cell
+and in the presence of the extracellular mechanism there will be a change
+in vext since I is not a transmembrane current but a current injected
+directly to the inside of the cell.
+
+(modified and borrowed extensively from other Neuron code, 
+2001-2002. Paul B. Manis)
+
+ENDCOMMENT
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+DEFINE NSTEP 5     : maximum number of steps supported in this mechanism 
+
+NEURON {
+    POINT_PROCESS IClamp2
+    RANGE  onset, dur, amp, i
+    ELECTRODE_CURRENT i
+}
+UNITS {
+    (nA) = (nanoamp)
+}
+PARAMETER {
+}
+
+ASSIGNED {
+	i (nA)
+	index
+	onset[NSTEP] (ms)
+	dur[NSTEP] (ms)
+	amp[NSTEP] (nA)
+	pu
+}
+
+INITIAL {
+	i = 0 (nA)
+	index = 0
+	pu = 0
+}
+:
+: at each onset time, a pulse of duration dur is generated. 
+:
+
+BREAKPOINT {
+	i = 0
+	FROM index = 0 TO NSTEP-1 {
+    		if(t > onset[index]) {
+			if(t < (dur[index]+onset[index])) {
+		        	i = i + amp[index] : allows overlap
+			} : end of if condition
+		} :end of second if condition
+	} : end of "FROM" (for) loop
+} : end of breakpoint

cnmodel/mechanisms/NMDA.mod

@@ -0,0 +1,142 @@
+TITLE NMDA receptor--one of the two input stimulation of our model
+
+: This mechanism is taken from the Neuron data base "exp2syn.mod" 
+: The original comment are below between "COMMENT" and "ENDCOMMENT".
+: 
+: Our modifications:
+: 
+: 1.We added a single receptor conductance factor: "g_max=0.000045 (uS)".
+:   An event of weight 1 generates a peak conductance of 1*g_max.
+:   The weight is equal to the number of ampa receptors open at peak conductance
+:
+: 2.The NMDA receptors are simulated using a slow rise time constant 
+:   and a double-expontial decay time constant
+
+: The kinetic rate constants and channel conductance are taken from Franks KM, Bartol TM and Sejnowski TJ 
+: A Monte Carlo model reveals independent signaling at central glutamatergic synapses 
+: J Biophys (2002) 83(5):2333-48
+: and Spruston N, Jonas P and Sakmann B
+: Dendritic glutamate receptor channels in rat hippocampal CA3 and CA1 neurons
+: J Physiol (1995) 482(2): 325-352
+: correctd for physiological tempterature with Q10 from Hestrin S, Sah P and Nicoll RA  
+: Mechanisms generating the time course of dual component excitatory synaptic currents 
+: recorded in hippocampal slices
+: Neuron (1990) 5: 247-253
+:
+: Written by Lei Tian on 04/12/06 
+
+
+
+COMMENT
+Two state kinetic scheme synapse described by rise time tau1,
+and decay time constant tau2. The normalized peak condunductance is 1.
+Decay time MUST be greater than rise time.
+
+The solution of A->G->bath with rate constants 1/tau1 and 1/tau2 is
+ A = a*exp(-t/tau1) and
+ G = a*tau2/(tau2-tau1)*(-exp(-t/tau1) + exp(-t/tau2))
+	where tau1 < tau2
+
+If tau2-tau1 -> 0 then we have a alphasynapse.
+and if tau1 -> 0 then we have just single exponential decay.
+
+The factor is evaluated in the
+initial block such that an event of weight 1 generates a
+peak conductance of 1.
+
+Because the solution is a sum of exponentials, the
+coupled equations can be solved as a pair of independent equations
+by the more efficient cnexp method.
+
+ENDCOMMENT
+
+
+NEURON {
+THREADSAFE
+	POINT_PROCESS nmda
+	RANGE tau1, tau2, tau3, e, i, g_max, g, A, B, C	,k
+	NONSPECIFIC_CURRENT i
+	GLOBAL total,i2,g2 
+:	EXTERNAL Area_canmda
+}
+
+UNITS {
+	(nA) = (nanoamp)
+	(mA) = (milliamp)
+	(mV) = (millivolt)
+	(uS) = (microsiemens)
+}
+
+PARAMETER {
+	tau1 = 3.18     (ms) <1e-9,1e9>     :rise time constant
+	tau2 = 57.14      (ms) <1e-9,1e9>	:decay time constant
+	tau3 = 2000     (ms) <1e-9,1e9>	    :decay time constant
+	
+	g_max= 0.000045 (uS)			: single channel conductance
+	e    = 0 (mV)
+	mg   = 1 (mM)
+
+	Area	(cm2)
+	k = 1e-06 (mA/nA)
+		Area_canmda = 1
+}
+
+ASSIGNED {
+	v (mV)
+	i (nA)
+	factor
+	total (uS)
+	g (uS)
+	
+	g2 (uS)	: plot 'g' and 'i' in "nmda.mod".
+	i2 (mA/cm2)	: global variables read in "canmda.mod" as 'inmda' and 'gnmda' to give us
+}
+
+STATE {
+	A (uS)
+	B (uS)
+	C (uS)
+}
+
+INITIAL {
+	LOCAL t_peak
+	total = 0
+	if (tau1/tau2 > .9999) {
+		tau1 = .9999*tau2
+	}
+	A = 0
+	B = 0
+	C = 0
+	
+	factor=0.8279	:from matlab to make the peak of the conductance curve shape to be 1*weight (then multiply with g_max)
+	factor = 1/factor
+	
+	Area = Area_canmda 
+}
+
+BREAKPOINT {
+	SOLVE state METHOD cnexp
+	
+	g = g_max*(B*0.8+C*0.2-A)
+	i = g*(v - e)*1/(1+(exp(0.08(/mV) * -v)*(mg / 0.69)))	
+	
+	g2=g			:global variable can be read in 'canmda.mod' 
+	i2=i*k/Area		:to get a current in 'mA/cm2' and send it to 'canmda.mod'
+}
+
+DERIVATIVE state {
+	A' = -A/tau1
+	B' = -B/tau2
+	C' = -C/tau3
+}
+
+NET_RECEIVE(weight (uS)) {
+	state_discontinuity(A, weight*factor)
+	state_discontinuity(B, weight*factor)
+	state_discontinuity(C, weight*factor)
+	total = total+weight
+	
+}
+
+
+

cnmodel/mechanisms/NMDA_Kampa.mod

@@ -0,0 +1,212 @@
+TITLE kinetic NMDA receptor model
+
+COMMENT
+-----------------------------------------------------------------------------
+
+	Kinetic model of NMDA receptors
+	===============================
+
+	10-state gating model:
+	Kampa et al. (2004) J Physiol
+  
+	  U -- Cl  --  O
+         \   | \	    \
+          \  |  \      \
+         UMg --  ClMg - OMg
+		 |	|
+		D1	|
+		 | \	|
+		D2  \	|
+		   \	D1Mg
+		    \	|
+			D2Mg
+-----------------------------------------------------------------------------
+
+  Based on voltage-clamp recordings of NMDA receptor-mediated currents in 
+  nucleated patches of  rat neocortical layer 5 pyramidal neurons (Kampa 2004), 
+  this model was fit with AxoGraph directly to experimental recordings in 
+  order to obtain the optimal values for the parameters.
+
+-----------------------------------------------------------------------------
+
+  This mod file does not include mechanisms for the release and time course
+  of transmitter; it should to be used in conjunction with a separate mechanism
+  to describe the release of transmitter and timecourse of the concentration
+  of transmitter in the synaptic cleft (to be connected to pointer XMTR here).
+
+-----------------------------------------------------------------------------
+
+  See details of NEURON kinetic models in:
+
+  Destexhe, A., Mainen, Z.F. and Sejnowski, T.J.  Kinetic models of 
+  synaptic transmission.  In: Methods in Neuronal Modeling (2nd edition; 
+  edited by Koch, C. and Segev, I.), MIT press, Cambridge, 1996.
+
+
+  Written by Bjoern Kampa in 2004 
+  Lightly modified, Paul Manis 2010.
+	Note that data were taken at 23 deg C
+	Q10 was taken from native receptors:
+	Korinek M, Sedlacek M, Cais O, Dittert I, Vyklicky L Jr. Temperature
+dependence of N-methyl-D-aspartate receptor channels and N-methyl-D-aspartate
+receptor excitatory postsynaptic currents. Neuroscience. 2010 Feb
+3;165(3):736-48. Epub 2009 Oct 31. PubMed PMID: 19883737.
+	
+-----------------------------------------------------------------------------
+ENDCOMMENT
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+NEURON {
+THREADSAFE
+
+	POINT_PROCESS NMDA_Kampa
+	POINTER XMTR
+	RANGE U, Cl, D1, D2, Open, MaxOpen, UMg, ClMg, D1Mg, D2Mg, OMg
+	RANGE g, gmax, vshift, Erev, rb, rmb, rmu, rbMg,rmc1b,rmc1u,rmc2b,rmc2u
+	GLOBAL mg, Rb, Ru, Rd1, Rr1, Rd2, Rr2, Ro, Rc, Rmb, Rmu
+	GLOBAL RbMg, RuMg, Rd1Mg, Rr1Mg, Rd2Mg, Rr2Mg, RoMg, RcMg
+	GLOBAL Rmd1b,Rmd1u,Rmd2b,Rmd2u,rmd1b,rmd1u,rmd2b,rmd2u
+	GLOBAL Rmc1b,Rmc1u,Rmc2b,Rmc2u
+	GLOBAL vmin, vmax, valence, memb_fraction
+	NONSPECIFIC_CURRENT i
+}
+
+UNITS {
+	(nA) = (nanoamp)
+	(mV) = (millivolt)
+	(pS) = (picosiemens)
+	(umho) = (micromho)
+	(mM) = (milli/liter)
+	(uM) = (micro/liter)
+}
+
+PARAMETER {
+
+	Erev	= 5    	(mV)	: reversal potential
+	gmax	= 500  	(pS)	: maximal conductance
+	mg	= 1  	(mM)	: external magnesium concentration
+	vmin 	= -120	(mV)
+	vmax 	= 100	(mV)
+	valence = -2		: parameters of voltage-dependent Mg block
+	memb_fraction = 0.8
+	vshift = 0.0 (mV)
+	Q10 = 2.0 : temperature sensitivity (see above)
+	
+    : Maximum open probability with Mode=0 (no rectification). 
+    : This is determined empirically by holding XMTR at a large
+    : value and v=40mV for 100 timesteps and measuring the 
+    : maximum value of Open.
+    MaxOpen = 0.01988893957 (1) 
+
+: Rates
+
+	Rb		= 10e-3   	(/uM /ms)	: binding 		
+	Ru		= 5.6e-3  	(/ms)	: unbinding		
+	Ro		= 10e-3   	(/ms)	: opening
+	Rc		= 273e-3   	(/ms)	: closing 
+:	Rd1		= 2.2e-3   	(/ms)	: fast desensitisation
+	Rd1		= 0.1		(/ms) 	: fast desensitisation
+	Rr1		= 1.6e-3   	(/ms)	: fast resensitisation
+:	Rd2 	= 0.43e-3 	(/ms)	: slow desensitisation
+	Rd2 	= 1e-4 		(/ms) 	: slow desensitisation
+	Rr2 	= 0.5e-3	(/ms)	: slow resensitisation
+	Rmb		= 0.05e-3	(/uM /ms)	: Mg binding Open
+	Rmu		= 12800e-3	(/ms)	: Mg unbinding Open
+	Rmc1b		= 0.00005e-3	(/uM /ms)	: Mg binding Closed
+	Rmc1u		= 2.438312e-3	(/ms)	: Mg unbinding Closed
+	Rmc2b		= 0.00005e-3	(/uM /ms)	: Mg binding Closed2
+	Rmc2u		= 5.041915e-3	(/ms)	: Mg unbinding Closed2
+	Rmd1b		= 0.00005e-3	(/uM /ms)	: Mg binding Desens1
+	Rmd1u		= 2.98874e-3	(/ms)	: Mg unbinding Desens1
+	Rmd2b		= 0.00005e-3	(/uM /ms)	: Mg binding Desens2
+	Rmd2u		= 2.953408e-3	(/ms)	: Mg unbinding Desens2
+	RbMg		= 10e-3		(/uM /ms)	: binding with Mg
+	RuMg		= 17.1e-3	(/ms)	: unbinding with Mg
+	RoMg		= 10e-3		(/ms)	: opening with Mg
+	RcMg		= 548e-3	(/ms)	: closing with Mg
+	Rd1Mg		= 2.1e-3	(/ms)	: fast desensitisation with Mg
+	Rr1Mg		= 0.87e-3	(/ms)	: fast resensitisation with Mg
+	Rd2Mg		= 0.26e-3	(/ms)	: slow desensitisation with Mg
+	Rr2Mg		= 0.42e-3	(/ms)	: slow resensitisation with Mg
+}
+
+ASSIGNED {
+	v		(mV)	: postsynaptic voltage
+	i 		(nA)	: current = g*(v - Erev)
+	g 		(pS)	: conductance
+	XMTR 		(mM)	: pointer to glutamate concentration
+
+	rb		(/ms)   : binding, [glu] dependent
+	rmb		(/ms)	: blocking V and [Mg] dependent
+	rmu		(/ms)	: unblocking V and [Mg] dependent
+	rbMg		(/ms)	: binding, [glu] dependent
+	rmc1b		(/ms)	: blocking V and [Mg] dependent
+	rmc1u		(/ms)	: unblocking V and [Mg] dependent
+	rmc2b		(/ms)	: blocking V and [Mg] dependent
+	rmc2u		(/ms)	: unblocking V and [Mg] dependent
+	rmd1b		(/ms)	: blocking V and [Mg] dependent
+	rmd1u		(/ms)	: unblocking V and [Mg] dependent
+	rmd2b		(/ms)	: blocking V and [Mg] dependent
+	rmd2u		(/ms)	: unblocking V and [Mg] dependent
+
+	qfac  : Q10
+    celsius (degC)
+}
+
+STATE {
+	: Channel states (all fractions)
+	U		: unbound
+	Cl		: closed
+	D1		: desensitised 1
+	D2		: desensitised 2
+	Open		: open
+	UMg		: unbound with Mg
+	ClMg		: closed with Mg
+	D1Mg		: desensitised 1 with Mg
+	D2Mg		: desensitised 2 with Mg
+	OMg		: open with Mg
+}
+
+INITIAL {
+	U = 1
+	qfac = Q10^((celsius-23)/10 (degC))}
+
+BREAKPOINT {
+	SOLVE kstates METHOD sparse
+
+	g = gmax * Open / MaxOpen
+	i = (1e-6) * g * (v - Erev)
+}
+
+KINETIC kstates {
+
+	rb 	= Rb 	* (1e3) * XMTR
+	rbMg 	= RbMg 	* (1e3) * XMTR
+	rmb 	= Rmb 	* mg * (1e3) * exp((v-40+vshift) * valence * memb_fraction /25 (mV))
+	rmu 	= Rmu 	* exp((-1)*(v-40+vshift) * valence * (1-memb_fraction) /25 (mV))
+	rmc1b 	= Rmc1b * mg * (1e3) * exp((v-40+vshift) * valence * memb_fraction /25 (mV))
+	rmc1u 	= Rmc1u * exp((-1)*(v-40+vshift) * valence * (1-memb_fraction) /25 (mV))
+	rmc2b 	= Rmc2b * mg * (1e3) * exp((v-40+vshift) * valence * memb_fraction /25 (mV))
+	rmc2u 	= Rmc2u * exp((-1)*(v-40+vshift) * valence * (1-memb_fraction) /25 (mV))
+	rmd1b 	= Rmd1b * mg * (1e3) * exp((v-40+vshift) * valence * memb_fraction /25 (mV))
+	rmd1u 	= Rmd1u * exp((-1)*(v-40+vshift) * valence * (1-memb_fraction) /25 (mV))
+	rmd2b 	= Rmd2b * mg * (1e3) * exp((v-40+vshift) * valence * memb_fraction /25 (mV))
+	rmd2u 	= Rmd2u * exp((-1)*(v-40+vshift) * valence * (1-memb_fraction) /25 (mV))
+
+	~ U <-> Cl	(rb*qfac,Ru*qfac)
+	~ Cl <-> Open	(Ro*qfac,Rc*qfac)
+	~ Cl <-> D1	(Rd1*qfac,Rr1*qfac)
+	~ D1 <-> D2	(Rd2*qfac,Rr2*qfac)
+	~ Open <-> OMg	(rmb*qfac,rmu*qfac)
+	~ UMg <-> ClMg 	(rbMg*qfac,RuMg*qfac)
+	~ ClMg <-> OMg 	(RoMg*qfac,RcMg*qfac)
+	~ ClMg <-> D1Mg (Rd1Mg*qfac,Rr1Mg*qfac)
+	~ D1Mg <-> D2Mg (Rd2Mg*qfac,Rr2Mg*qfac)
+	~ U <-> UMg     (rmc1b*qfac,rmc1u*qfac)
+	~ Cl <-> ClMg	(rmc2b*qfac,rmc2u*qfac)
+	~ D1 <-> D1Mg	(rmd1b*qfac,rmd1u*qfac)
+	~ D2 <-> D2Mg	(rmd2b*qfac,rmd2u*qfac)
+
+	CONSERVE U+Cl+D1+D2+Open+UMg+ClMg+D1Mg+D2Mg+OMg = 1
+}

cnmodel/mechanisms/adex.mod

@@ -0,0 +1,125 @@
+: AdEx GIF model
+
+: This implementation is for the equations in:
+: Naud R, Marcille N, Clopath C, Gerstner W. Firing patterns in the adaptive
+: exponential integrate-and-fire model. Biol Cybern. 2008 Nov;99(4-5):335-47. doi: 
+: 10.1007/s00422-008-0264-7. Epub 2008 Nov 15. PubMed PMID: 19011922; PubMed
+: Central PMCID: PMC2798047.
+
+: Which in turn is based on:
+: Brette R, Gerstner W. Adaptive exponential integrate-and-fire model as an
+: effective description of neuronal activity. J Neurophysiol. 2005
+: Nov;94(5):3637-42. Epub 2005 Jul 13. PubMed PMID: 16014787.
+:
+: Paul B. Manis
+: 9 Nov 2017, Washington DC
+:
+
+
+NEURON {
+:	ARTIFICIAL_CELL AdEx
+    SUFFIX AdEx
+    RANGE gl, el, delt, vt, vr, w, b, cm, is, a, tauw
+    RANGE refract, Vm
+    NONSPECIFIC_CURRENT i
+	: m plays the role of voltage
+}
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+PARAMETER {
+	cm = 200 (pF)
+    el = -70 (mV)  : leak (RMP)
+    gl = 10 (nS)  : resting input R
+    delt = 2 (mV)  : spike threshold sharpness
+    vt = -50 (mV)  
+    vr = -58 (mV): reset value after a spike
+    a = 2 (nS)
+    b = 0 (pA)
+    is = 0 (pA)
+    tauw = 30 (ms)
+	refract = 1 (ms)
+}
+
+ASSIGNED {
+    i       (mA/cm2)
+	t0(ms)  : time of last spike
+	refractory  : flag indicating when in a refractory period
+}
+
+STATE {
+    w
+    Vm
+}
+
+INITIAL {
+	Vm = el
+	t0 = t
+    w = 0
+    
+	refractory = 0 : 0-integrates input, 1-refractory
+}
+
+BREAKPOINT {
+    SOLVE states METHOD cnexp
+
+    if (refractory == 0 && Vm <= 0.) {
+        states()
+    }
+    if (refractory == 1) {
+        if ((t-t0) >= refract){
+            refractory = 0
+            Vm = vr
+            states()
+        }
+        else {
+            Vm = 0.
+        }
+    }
+    if (refractory == 0 && Vm > 0.) {
+        refractory = 1
+        t0 = t
+        Vm = 0.
+        w = w + b
+    }
+}
+
+
+DERIVATIVE states {  : update adaptation variable w
+    LOCAL eterm, et
+    w' = (a*(Vm - el) - w)/tauw
+    eterm = (Vm-vt)/delt
+    if (eterm > 700 ) { : prevent overflow of the exponential term 
+                        : (it would be better to estimate the value... but for this 
+                        : implementation, not necessary as this will be the term
+                        : that drives the model to spike - after that V is reset
+                        : so the time evolution no longer matters)
+         et = 700.
+    }
+    else {
+        et = exp(eterm)
+    }
+    Vm' = ( -gl*(Vm-el) + gl*delt*et + is - w)/cm
+}
+
+
+COMMENT
+NET_RECEIVE (w) {
+	if (refractory == 0) { : inputs integrated only when excitable
+        i = -gl*(v-el) + gl*delt*exp((Vm-vt)/delt) - w
+        m = i/cm
+		t0 = t
+        states()
+        if (m > 0) {
+			refractory = 1
+			m = 0
+			net_send(refractory, refractory)
+			net_event(t)
+		}
+	} else if (flag == 1) { : ready to integrate again
+		t0 = t
+		refractory = 0
+		m = vr
+	}
+}
+ENDCOMMENT

cnmodel/mechanisms/ampa_trussell.mod

@@ -0,0 +1,224 @@
+TITLE Model of AMPA receptors
+
+COMMENT
+-----------------------------------------------------------------------------
+
+    Kinetic model of AMPA receptors
+    ===============================
+
+    6-state gating model:
+    (scheme 1 from Raman and Trussell, Neuron 9:173-186, 1992)
+    2 open states provide dual exponential response.
+
+               O1
+               |
+    C -- C1 -- C2 -- O2
+               |
+               D
+
+-----------------------------------------------------------------------------
+
+    This mod file does not include mechanisms for the release and time course
+    of transmitter; it is to be used in conjunction with a separate mechanism
+    to describe the release of transmitter and that provides the concentration
+    of transmitter in the synaptic cleft (to be connected to pointer C here).
+
+    Default parameters are set for a miniature EPSC.
+
+-----------------------------------------------------------------------------
+    Code based on Destexhe's ampa5.mod
+
+    B. Graham, Dept. of Computing Science & Maths, University of Stirling
+    (Contact: b.graham@cs.stir.ac.uk)
+    (previously IANC, Division of Informatics, University of Edinburgh)
+
+    CNS 2000 Version (19/11/02)
+
+-----------------------------------------------------------------------------
+
+    Further modified:
+
+    Paul Manis (Otolaryngology/HNS and Cell and Molecular Physiology,
+    UNC Chapel Hill. contact: pmanis@med.unc.edu)
+
+    3/15/2005 Modifications:
+
+    1. Added Q10/qfac to allow temperature scaling. All rates in the state model
+    are changed by the same factor. A Q10 of 1.5 gives a decay tau (single
+    exponential fit using Praxis algorithm in NEURON; using ampa_kinetics.hoc)
+    of about 850 usec at 22 deg C and 570 usec at 33 deg C. These are consistent
+    with the Raman and Trussell 1992 measurements in avians. The 850 usec is a
+    bit fast for an EPSC, and could probably be tuned by adjustment of some of
+    the parameters below.
+
+    2. Brought several variables out to global (rather than range) so that we
+    can change them - Q10 and gmax in particular. note that gmax is in pS. Only
+    local conductance etc. is in specified as RANGE.
+
+    3. Max open probability is less than unity, so a gmax of 2500 yields 100 pA
+    at -60 mV. Therefore scaling by mini size must take this into account.
+
+    3/28/2005 Paul B. Manis
+    Added rectification to AMPA R. Rectification is controlled by
+    polyamine-style block of receptor. See Donevan and Rogawski, 1995; Washburn
+    et al., 1997. The equations used here are from Washburn et al. The values
+    given in the equation at the break point were determined from EPSCs in 5
+    21-d old DBA mice. Blocker = 45 (uM), Kd = 31.32, zd = 1.029. Note that this
+    should also reduce the maximal conductance. Mode: if 1, use rectifying; if
+    0, use non-rectifying. Default is 1
+
+    This point process uses XMTR as the transmitter concentration to operate on
+    the receptor kinetics. XMTR should be provided by another process that
+    controls release (e.g., COH calyx of Held, etc). An advantage of this is
+    that whatever release process is present, glutamate accumulates in the
+    cleft, and can drive desensitization etc.
+
+-----------------------------------------------------------------------------
+ENDCOMMENT
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+NEURON {
+THREADSAFE
+    POINT_PROCESS AMPATRUSSELL
+    POINTER XMTR
+    RANGE C0, C1, C2, D, O1, O2
+
+    RANGE Rb, Ru1, Ru2, Rd, Rr, Ro1, Rc1, Ro2, Rc2, Open, MaxOpen
+    GLOBAL vmin, vmax
+	GLOBAL Q10, Mode
+	GLOBAL zd, Kd0
+    RANGE g, rb, gmax, PA, Erev
+    NONSPECIFIC_CURRENT i
+}
+
+UNITS {
+    (nA) = (nanoamp)
+    (mV) = (millivolt)
+    (pS) = (picosiemens)
+    (umho) = (micromho)
+    (mM) = (milli/liter)
+    (uM) = (micro/liter)
+}
+
+PARAMETER {
+
+    Erev    = 7    (mV) : reversal potential
+    gmax    = 10  (pS)  : maximal conductance
+    vmin = -120 (mV)
+    vmax = 100  (mV)
+	Q10 = 1.5 : temperature sensitivity
+    Mode = 0 : flag to control rectification calculation
+
+: polyamine block parameters (Wang & Manis unpublished data)
+	zd = 1.032     
+	PA = 45
+	Kd0 = 31.e-6
+
+: Rates
+
+    Rb  = 13   (/mM /ms): binding
+                : diffusion limited (DO NOT ADJUST)
+    Ru1 = 0.3  (/ms)    : unbinding (1st site)
+    Ru2 = 200  (/ms)    : unbinding (2nd site)
+    Rd  = 30.0   (/ms)  : desensitization (WAS30.0)
+    Rr  = 0.02 (/ms)    : resensitization
+    Ro1 = 100    (/ms)  : opening (fast)
+    Rc1 = 2    (/ms)  : closing
+    Ro2 = 2    (/ms)   : opening (slow)
+    Rc2 = 0.25    (/ms) : closing
+
+    Open = 0 (1) : total of all open states
+    
+    : Maximum open probability with Mode=0 (no rectification). 
+    : This is determined empirically by holding XMTR at a large
+    : value for 100 timesteps and measuring the maximum value
+    : of Open.
+    MaxOpen = 0.72418772400 (1)  
+    
+	aflag = 1 : Flag for control of printout of initial values.....
+
+}
+
+ASSIGNED {
+    v       (mV)    : postsynaptic voltage
+    i       (nA)    : current = g*(v - Erev)
+    g       (pS)    : conductance
+	g0		(pS)	: conductance for voltage-dependent block by polyamines
+	gvdep   (pS)   : voltage-dependence of conductance
+    XMTR    (mM)    : pointer to glutamate concentration
+    rb      (/ms)   : binding
+	qfac   : q10 factor for rate scaling
+    celsius (degC)
+
+}
+
+STATE {
+    : Channel states (all fractions)
+    C0      : unbound
+    C1      : single glu bound
+    C2      : double glu bound
+    D       : single glu bound, desensitized
+    O1      : open state 1
+    O2      : open state 2
+}
+
+INITIAL {
+    usetable = 0
+	C0=1
+    C1=0
+    C2=0
+    D=0
+    O1=0
+    O2=0
+    Open = 0
+	qfac = Q10^((celsius-22)/10)
+:	VERBATIM
+:	fprintf(stdout, "AMPA.MOD gmax: %f    Q10 = %f  celsius = %f\n", gmax, Q10, celsius);
+:    ENDVERBATIM
+    gvdepcalc(v)
+}
+
+BREAKPOINT {
+    SOLVE kstates METHOD sparse
+ :   VERBATIM
+ :   fprintf(stderr, "kstates @ t=%7.2f Rb: %f  XMTR: %f:  gmax: %f, o1: %f   o2: %f\n", t, Rb, XMTR, gmax, O1, O2);
+ :   ENDVERBATIM
+
+    gvdepcalc(v)
+    Open = O1 + O2
+    g = gmax * Open / MaxOpen
+    if ( Mode == 1) {
+		g0 = 1.0 + 0.6*exp((v-50)/40)  : eq. 5 of Washburn et al., 1997, slightly modified
+		gvdep = g0*(1/(1+PA/(Kd0*exp(-zd*v/25.3))))
+		i = (1e-6) * g * gvdep * (v - Erev)
+	}
+	else {
+		i = (1e-6)*g*(v-Erev)
+	}
+}
+
+KINETIC kstates {
+
+    rb = Rb * XMTR
+
+    ~ C0 <-> C1 (rb*qfac,Ru1*qfac)
+    ~ C1 <-> C2 (rb*qfac,Ru2*qfac)
+    ~ C2 <-> D  (Rd*qfac,Rr*qfac)
+    ~ C2 <-> O1 (Ro1*qfac,Rc1*qfac)
+    ~ C2 <-> O2 (Ro2*qfac,Rc2*qfac)
+    CONSERVE C0+C1+C2+D+O1+O2 = 1
+}
+
+LOCAL g0
+PROCEDURE gvdepcalc(v) {
+	TABLE gvdep DEPEND PA, Kd0, zd FROM -100 TO 100 WITH 200
+ :   VERBATIM
+ :   fprintf(stderr, "gvdepcalc starts ");
+ :   ENDVERBATIM
+    g0 = 1.0 + 0.6*exp((v-50)/40)  : eq. 5 of Washburn et al., 1997, slightly modified
+    gvdep = g0*(1/(1+PA/(Kd0*exp(-zd*v/25.3))))
+  :  VERBATIM
+  :  fprintf(stderr, "& ends\n");
+  :  ENDVERBATIM
+}

cnmodel/mechanisms/atm.mod

@@ -0,0 +1,129 @@
+: ATM GIF model
+
+: This implementation the adaptive theshold model (ATM) is for the equations in:
+: Fontaine, B., Benichourx, V., Joris, P.X., and Brette, R. Prediciting
+: spike timing in hhigy synchronous auditory neurons at different sound
+: levels. J. Neurophysiol. 110: 1672-1688, 2013.
+
+: Which in turn is based on:
+: Brette R, Gerstner W. Adaptive exponential integrate-and-fire model as an
+: effective description of neuronal activity. J Neurophysiol. 2005
+: Nov;94(5):3637-42. Epub 2005 Jul 13. PubMed PMID: 16014787.
+:
+: Paul B. Manis
+: 2 December 2017, Chapel Hill, NC
+:
+: Incomplete version
+
+NEURON {
+:	ARTIFICIAL_CELL ATM
+    SUFFIX ATM
+    RANGE gl, el, delt, vt, vr, alpha, beta, cm, is, a, tauw
+    RANGE refract, Vm
+    NONSPECIFIC_CURRENT i
+	: m plays the role of voltage
+}
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+PARAMETER {
+	cm = 200 (pF)
+    el = -70 (mV)  : leak (RMP)
+    gl = 10 (nS)  : resting input R
+    delt = 2 (mV)  : spike threshold sharpness
+    vr = -58 (mV): reset value after a spike
+    a = 2 (1)
+    b = 2 (1)
+    beta = 0 (1)
+    alpha = 0
+    is = 0 (pA)
+    taut = 30 (ms) : threshold tau
+	refract = 1 (ms)
+}
+
+ASSIGNED {
+    i       (mA/cm2)
+	t0      (ms)    : time of last spike
+	refractory      : flag indicating when in a refractory period
+}
+
+STATE {
+    w
+    Vm
+    vt
+}
+
+INITIAL {
+	Vm = el
+	t0 = t
+    a = 0
+    b = 0
+    
+	refractory = 0 : 0-integrates input, 1-refractory
+}
+
+BREAKPOINT {
+    SOLVE states METHOD cnexp
+
+    if (refractory == 0 && Vm <= 0.) {
+        states()
+    }
+    if (refractory == 1) {
+        if ((t-t0) >= refract){
+            refractory = 0
+            Vm = vr
+            states()
+        }
+        else {
+            Vm = 0.
+        }
+    }
+    if (refractory == 0 && Vm > 0.) {
+        refractory = 1
+        t0 = t
+        Vm = 0.
+        w = w + b
+    }
+}
+
+
+DERIVATIVE states {  : update adaptation variable w
+    LOCAL eterm, et
+    vt' = (a*i - vt)/taut
+COMMENT
+    eterm = (Vm-vt)/delt
+    if (eterm > 700 ) { : prevent overflow of the exponential term 
+                        : (it would be better to estimate the value... but for this 
+                        : implementation, not necessary as this will be the term
+                        : that drives the model to spike - after that V is reset
+                        : so the time evolution no longer matters)
+         et = 700.
+    }
+    else {
+        et = exp(eterm)
+    }
+ENDCOMMENT
+    Vm' = gl*( -(Vm-el) + i)/cm
+}
+
+
+COMMENT
+NET_RECEIVE (w) {
+	if (refractory == 0) { : inputs integrated only when excitable
+        i = -gl*(v-el) + gl*delt*exp((Vm-vt)/delt) - w
+        m = i/cm
+		t0 = t
+        states()
+        if (m > 0) {
+			refractory = 1
+			m = 0
+			net_send(refractory, refractory)
+			net_event(t)
+		}
+	} else if (flag == 1) { : ready to integrate again
+		t0 = t
+		refractory = 0
+		m = vr
+	}
+}
+ENDCOMMENT

cnmodel/mechanisms/bkpkj.mod

@@ -0,0 +1,98 @@
+: BK-type Purkinje calcium-activated potassium current
+: Created 8/19/02 - nwg
+
+NEURON {
+       THREADSAFE
+       SUFFIX bkpkj
+       USEION k READ ek WRITE ik
+       USEION ca READ cai
+       RANGE gbar, ik, gbkpkj
+       GLOBAL minf, mtau, hinf, htau, zinf, ztau
+       GLOBAL m_vh, m_k, mtau_y0, mtau_vh1, mtau_vh2, mtau_k1, mtau_k2
+       GLOBAL z_coef, ztau
+       GLOBAL h_y0, h_vh, h_k, htau_y0, htau_vh1, htau_vh2, htau_k1, htau_k2
+}
+
+UNITS {
+      (mV) = (millivolt)
+      (mA) = (milliamp)
+      (mM) = (milli/liter)
+}
+
+PARAMETER {
+          v            (mV)
+          gbar = 0.007 (mho/cm2)
+
+          m1 (ms)
+          m_vh = -28.9           (mV)
+          m_k = 6.2            (mV)
+          mtau_y0 = 0.505 (ms) : 0.000505     (s)
+          mtau_vh1 = -33.3     (mV)
+          mtau_k1 = -10         (mV)
+          mtau_vh2 = 86.4       (mV)
+          mtau_k2 = 10.1        (mV)
+
+          z_coef = 0.001        (mM)
+          ztau = 1              (ms)
+
+          h_y0 = 0.085
+          h_vh = -32          (mV)
+          h_k = 5.8             (mV)
+          htau_y0 = 1.9 (ms) : 0.0019      (s)
+          htau_vh1 = -54.2       (mV)
+          htau_k1 = -12.9       (mV)
+          htau_vh2 = 48.5      (mV)
+          htau_k2 = 5.2        (mV)
+
+          ek           (mV)
+          cai          (mM)
+}
+
+ASSIGNED {
+         gbkpkj (mho/cm2)
+         minf
+         mtau          (ms)
+         hinf
+         htau          (ms)
+         zinf
+
+         ik            (mA/cm2)
+}
+
+STATE {
+      m   FROM 0 TO 1
+      z   FROM 0 TO 1
+      h   FROM 0 TO 1
+}
+
+BREAKPOINT {
+           SOLVE states METHOD cnexp
+           gbkpkj = gbar * m * m * m * z * z * h
+           ik =  gbkpkj * (v - ek)
+}
+
+DERIVATIVE states {
+        rates(v)
+
+        m' = (minf - m) / mtau
+        h' = (hinf - h) / htau
+        z' = (zinf - z) / ztau
+}
+
+PROCEDURE rates(Vm (mV)) {
+          LOCAL v
+          v = Vm + 5
+          minf = 1 / (1 + exp(-(v - (m_vh)) / m_k))
+          m1 = mtau_y0 + (1. (ms)/(exp((v+ mtau_vh1)/mtau_k1)))
+          mtau =  m1 + (1. (ms)) * exp((v+mtau_vh2)/mtau_k2)
+          zinf = 1/(1 + z_coef / cai)
+          hinf = h_y0 + (1-h_y0) / (1+exp((v - h_vh)/h_k))
+          htau =  (htau_y0 + (1 (ms))/(exp((v + htau_vh1)/htau_k1)+exp((v+htau_vh2)/htau_k2)))
+}
+
+INITIAL {
+        rates(v)
+        m = minf
+        z = zinf
+        h = hinf
+}

cnmodel/mechanisms/cabpump.mod

@@ -0,0 +1,157 @@
+TITLE Calcium ion accumulation and diffusion with pump
+: The internal coordinate system is set up in PROCEDURE coord_cadifus()
+: and must be executed before computing the concentrations.
+: The scale factors set up in this procedure do not have to be recomputed
+: when diam or DFree are changed.
+: The amount of calcium in an annulus is ca[i]*diam^2*vol[i] with
+: ca[0] being the second order correct concentration at the exact edge
+: and ca[NANN-1] being the concentration at the exact center
+
+? interface
+NEURON {
+THREADSAFE
+	SUFFIX cadifpmp
+	USEION ca READ cao, ica WRITE cai, ica
+	RANGE ica_pmp, last_ica_pmp, k1, k2, k3, k4, DFree
+	GLOBAL vol, pump0
+}
+
+DEFINE NANN  10
+
+UNITS {
+	(mV)	= (millivolt)
+	(molar) = (1/liter)
+	(mM)	= (millimolar)
+	(um)	= (micron)
+	(mA)	= (milliamp)
+	(mol)	= (1)
+	FARADAY = (faraday)	 (coulomb)
+	PI	= (pi)		(1)
+	R 	= (k-mole)	(joule/degC)
+}
+
+PARAMETER {
+	DFree = 0.6	(um2/ms) <0,1e9>
+	beta = 50		<0, 1e9>
+
+        k1 = 5e8        (/mM-s) <0, 1e10>:optional mm formulation
+        k2 = .25e6      (/s)	<0, 1e10>
+        k3 = .5e3       (/s)	<0, 1e10>
+        k4 = 5e0        (/mM-s)	<0, 1e10>
+	pump0 = 3e-14 (mol/cm2) <0, 1e9> : set to 0 in hoc if this pump not wanted
+}
+
+ASSIGNED {
+	celsius		(degC)
+	diam		(um)
+	v		(millivolt)
+	cao		(mM)
+	cai		(mM)
+	ica		(mA/cm2)
+	vol[NANN]	(1)	: gets extra cm2 when multiplied by diam^2
+        ica_pmp (mA/cm2)
+        area1    (um2)
+        c1      (1+8 um5/ms)
+        c2      (1-10 um2/ms)
+        c3      (1-10 um2/ms)
+        c4      (1+8 um5/ms)
+	ica_pmp_last (mA/cm2)
+}
+
+CONSTANT {
+	volo = 1 (liter)
+}
+
+STATE {
+	ca[NANN]	(mM) <1e-6> : ca[0] is equivalent to cai
+        pump    (mol/cm2)	<1e-15>
+        pumpca  (mol/cm2)	<1e-15>
+}
+
+INITIAL {LOCAL total
+	parms()
+	FROM i=0 TO NANN-1 {
+		ca[i] = cai
+	}
+	pumpca = cai*pump*c1/c2
+	total = pumpca + pump
+	if (total > 1e-9) {
+		pump = pump*(pump/total)
+		pumpca = pumpca*(pump/total)
+	}
+	ica_pmp = 0
+	ica_pmp_last = 0
+}
+
+BREAKPOINT {
+	SOLVE state METHOD sparse
+	ica_pmp_last = ica_pmp
+	ica = ica_pmp
+:	printf("Breakpoint t=%g v=%g cai=%g ica=%g\n", t, v, cai, ica)
+}
+
+LOCAL frat[NANN] 	: gets extra cm when multiplied by diam
+
+PROCEDURE coord() {
+	LOCAL r, dr2
+	: cylindrical coordinate system  with constant annuli thickness to
+	: center of cell. Note however that the first annulus is half thickness
+	: so that the concentration is second order correct spatially at
+	: the membrane or exact edge of the cell.
+	: note ca[0] is at edge of cell
+	:      ca[NANN-1] is at center of cell
+	r = 1/2					:starts at edge (half diam)
+	dr2 = r/(NANN-1)/2			:half thickness of annulus
+	vol[0] = 0
+	frat[0] = 2*r
+	FROM i=0 TO NANN-2 {
+		vol[i] = vol[i] + PI*(r-dr2/2)*2*dr2	:interior half
+		r = r - dr2
+		frat[i+1] = 2*PI*r/(2*dr2)	:exterior edge of annulus
+					: divided by distance between centers
+		r = r - dr2
+		vol[i+1] = PI*(r+dr2/2)*2*dr2	:outer half of annulus
+	}
+}
+
+KINETIC state {
+:	printf("Solve begin t=%g v=%g cai=%g ica_pmp=%g\n", t, v, cai, ica_pmp)
+	COMPARTMENT i, (1+beta)*diam*diam*vol[i]*1(um) {ca}
+	COMPARTMENT (1e10)*area1 {pump pumpca}
+	COMPARTMENT volo*(1e15) {cao}
+? kinetics
+	~ pumpca <-> pump + cao		(c3, c4)
+	ica_pmp = (1e-4)*2*FARADAY*(f_flux - b_flux)/area1
+	: all currents except pump
+	~ ca[0] << (-(ica-ica_pmp_last)*PI*diam*1(um)*(1e4)*frat[0]/(2*FARADAY))
+	:diffusion
+	FROM i=0 TO NANN-2 {
+		~ ca[i] <-> ca[i+1] (DFree*frat[i+1]*1(um), DFree*frat[i+1]*1(um))
+	}
+	:pump
+	~ ca[0] + pump <-> pumpca	(c1, c2)
+	cai = ca[0] : this assignment statement is used specially by cvode
+:	printf("Solve end cai=%g ica=%g ica_pmp=%g ica_pmp_last=%g\n",
+:		 cai, ica, ica_pmp,ica_pmp_last)
+}
+	
+PROCEDURE parms() {
+	coord()
+	area1 = 2*PI*(diam/2) * 1(um)
+        c1 = (1e7)*area1 * k1
+        c2 = (1e7)*area1 * k2
+        c3 = (1e7)*area1 * k3
+        c4 = (1e7)*area1 * k4  
+}
+
+FUNCTION ss() (mM) {
+	SOLVE state STEADYSTATE sparse
+	ss = cai
+}
+
+COMMENT
+At this time, conductances (and channel states and currents are
+calculated at the midpoint of a dt interval.  Membrane potential and
+concentrations are calculated at the edges of a dt interval.  With
+secondorder=2 everything turns out to be second order correct.
+ENDCOMMENT

cnmodel/mechanisms/cadiff.mod

@@ -0,0 +1,50 @@
+: Ca diffusion in a Purkinje cell
+: Created 8/15/02 - nwg
+
+NEURON {
+       SUFFIX cadiff
+       USEION ca READ ica, cai WRITE cai
+       RANGE ca
+       GLOBAL depth, beta
+}
+
+UNITS {
+      (mV) = (millivolt)
+      (mA) = (milliamp)
+      (mM) = (milli/liter)
+      (um) = (micron)
+}
+
+CONSTANT {
+      F = 9.6485e4 (coul)
+}
+
+PARAMETER {
+          cai      (mM)
+          dt       (ms)
+
+          depth  = 0.1  (um)
+          beta = 1 (/ms)
+}
+
+ASSIGNED {
+         ica       (mA/cm2)
+}
+
+STATE {
+      ca           (mM)
+}
+
+INITIAL {
+        ca = 0.0001
+}
+
+BREAKPOINT {
+        ca = ca + (10000.0) * dt * ( ( -1/(2*F)*ica / (depth)) - (0.0001) * beta * ca )
+
+        if ( ca < 1e-4 ) {: minimum 100 nM Ca
+           ca = 1e-4
+        }
+
+        cai = ca
+}

cnmodel/mechanisms/cadyn.mod

@@ -0,0 +1,100 @@
+TITLE decay of submembrane calcium concentration
+:
+: Internal calcium concentration due to calcium currents and pump.
+: Differential equations.
+:
+: This file contains two mechanisms:
+:
+: 1. Simple model of ATPase pump with 3 kinetic constants (Destexhe 1992)
+:
+:       Cai + P <-> CaP -> Cao + P  (k1,k2,k3)
+:
+:   A Michaelis-Menten approximation is assumed, which reduces the complexity
+:   of the system to 2 parameters: 
+:       kt = <tot enzyme concentration> * k3  -> TIME CONSTANT OF THE PUMP
+:	kd = k2/k1 (dissociation constant)    -> EQUILIBRIUM CALCIUM VALUE
+:   The values of these parameters are chosen assuming a high affinity of 
+:   the pump to calcium and a low transport capacity (cfr. Blaustein, 
+:   TINS, 11: 438, 1988, and references therein).  
+:
+:   For further information about this this mechanism, see Destexhe, A. 
+:   Babloyantz, A. and Sejnowski, TJ.  Ionic mechanisms for intrinsic slow 
+:   oscillations in thalamic relay neurons. Biophys. J. 65: 1538-1552, 1993.
+:
+:
+: 2. Simple first-order decay or buffering:
+:
+:       Cai + B <-> ...
+:
+:   which can be written as:
+:
+:       dCai/dt = (cainf - Cai) / taur
+:
+:   where cainf is the equilibrium intracellular calcium value (usually
+:   in the range of 200-300 nM) and taur is the time constant of calcium 
+:   removal.  The dynamics of submembranal calcium is usually thought to
+:   be relatively fast, in the 1-10 millisecond range (see Blaustein, 
+:   TINS, 11: 438, 1988).
+:
+: All variables are range variables
+:
+: Written by Alain Destexhe, Salk Institute, Nov 12, 1992
+:
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+NEURON {
+	SUFFIX cadyn
+	USEION ca READ ica, cai WRITE cai
+	RANGE depth,kt,kd,cainf,taur
+}
+
+UNITS {
+	(molar) = (1/liter)			: moles do not appear in units
+	(mM)	= (millimolar)
+	(um)	= (micron)
+	(mA)	= (milliamp)
+	(msM)	= (ms mM)
+}
+
+CONSTANT {
+	FARADAY = 96489		(coul)		: moles do not appear in units
+}
+
+PARAMETER {
+	depth	= .1	(um)		: depth of shell
+	taur	= 1e10	(ms)		: remove first-order decay
+	cainf	= 1.4e-1 (mM)
+	kt	= 1e-4	(mM/ms)
+	kd	= 1e-4	(mM)
+}
+
+STATE {
+	cai		(mM) 
+}
+
+INITIAL {
+	cai = kd
+}
+
+ASSIGNED {
+	ica		(mA/cm2)
+	drive_channel	(mM/ms)
+	drive_pump	(mM/ms)
+}
+	
+BREAKPOINT {
+	SOLVE state METHOD cnexp
+}
+
+DERIVATIVE state { 
+
+	drive_channel =  - (10000) * ica / (2 * FARADAY * depth)
+
+	if (drive_channel <= 0.) { drive_channel = 0. }	: cannot pump inward
+
+	drive_pump = -kt * cai / (cai + kd )		: Michaelis-Menten
+
+	cai' = drive_channel + drive_pump + (cainf-cai)/taur
+}
+

cnmodel/mechanisms/cap.mod

@@ -0,0 +1,86 @@
+: HH P-type Calcium current
+: Created 8/13/02 - nwg
+
+NEURON {
+	THREADSAFE
+    SUFFIX cap
+	USEION ca READ cai, cao WRITE ica
+	RANGE pcabar, ica
+	RANGE minf, mtau
+	RANGE monovalConc, monovalPerm
+}
+
+UNITS {
+	(mV) = (millivolt)
+	(mA) = (milliamp)
+	(mM) = (milli/liter)
+	F = 9.6485e4   (coul)
+	R = 8.3145 (joule/degC)
+}
+
+PARAMETER {
+	v (mV)
+	celsius		(degC)
+
+	pcabar = 0.00005	(cm/s)
+	monovalConc = 140     (mM)
+	monovalPerm = 0
+
+	cai             (milli/liter)
+	cao             (milli/liter)
+}
+
+ASSIGNED {
+	ica            (mA/cm2)
+    minf
+	mtau           (ms)
+	T              (degC)
+	E              (volts)
+}
+
+STATE {
+	m
+}
+
+INITIAL {
+	rates(v)
+	m = minf
+}
+
+BREAKPOINT {
+	SOLVE states METHOD cnexp
+	ica = (1e3) * pcabar * m * ghk(v, cai, cao, 2)
+}
+
+DERIVATIVE states {
+	rates(v)
+	m' = (minf - m)/mtau
+}
+
+FUNCTION ghk( v(mV), ci(mM), co(mM), z)  (coul/cm3) { LOCAL Ci
+	T = celsius + 273.19  : Kelvin
+    E = (1e-3) * v
+    Ci = ci + (monovalPerm) * (monovalConc)        : Monovalent permeability
+	if (fabs(1-exp(-z*(F*E)/(R*T))) < 1e-6) { : denominator is small -> Taylor series
+		ghk = (1e-6) * z * F * (Ci-co*exp(-z*(F*E)/(R*T)))*(1-(z*(F*E)/(R*T)))
+	} else {
+		ghk = (1e-6) * z^2*(E*F^2)/(R*T)*(Ci-co*exp(-z*(F*E)/(R*T)))/(1-exp(-z*(F*E)/(R*T)))
+	}
+}
+
+PROCEDURE rates (v (mV)) {
+    UNITSOFF
+	minf = 1/(1+exp(-(v - (-19)) / 5.5))
+    mtau = (mtau_func(v)) * 1e3
+    UNITSON
+}
+
+FUNCTION mtau_func( v (mV) ) (ms) {
+    UNITSOFF
+    if (v > -50) {
+	    mtau_func = .000191 + .00376*exp(-((v-(-41.9))/27.8)^2)
+    } else {
+	    mtau_func = .00026367 + .1278 * exp(.10327*v)
+    }
+    UNITSON
+}

cnmodel/mechanisms/capmp.mod

@@ -0,0 +1,71 @@
+NEURON {
+	SUFFIX capmp
+	USEION ca READ cao, ica, cai WRITE cai, ica
+	RANGE tau, width, cabulk, ica, pump0
+}
+	
+UNITS {
+	(um)	=	(micron)
+	(molar) =	(1/liter)
+	(mM)	=	(millimolar)
+	(uM)	= 	(micromolar)
+	(mA)	=	(milliamp)
+	(mol)	=	(1)
+	FARADAY = 	(faraday)	(coulomb)
+}
+
+PARAMETER {
+	width = 0.1 (um)
+	tau = 1 (ms)
+	k1 = 5e8	(/mM-s)
+	k2 = 0.25e6	(/s)
+	k3 = 0.5e3	(/s)
+	k4 = 5e0	(/mM-s)
+	cabulk = 0.1 (uM)
+	pump0 = 3e-14 (mol/cm2)
+}
+
+ASSIGNED {
+	cao (mM) : 2
+	cai (mM) : 100e-6
+	ica (mA/cm2)
+	ica_pmp (mA/cm2)
+	ica_pmp_last (mA/cm2)
+}
+
+STATE {
+	cam (uM)	<1e-6>
+	pump (mol/cm2)  <1e-16>
+	capump (mol/cm2) <1e-16>
+}
+
+INITIAL {
+	ica = 0
+	ica_pmp = 0
+	ica_pmp_last = 0
+	SOLVE pmp STEADYSTATE sparse
+}
+
+BREAKPOINT {
+	SOLVE pmp METHOD sparse
+	ica_pmp_last = ica_pmp
+	ica = ica_pmp
+}
+
+KINETIC pmp {
+	~ cabulk <-> cam (width/tau, width/tau)
+	~ cam + pump <-> capump ((1e7)*k1, (1e10)*k2)
+	~ capump <-> cao + pump ((1e10)*k3, (1e10)*k4)
+	ica_pmp = (1e-7)*2*FARADAY*(f_flux - b_flux)
+
+	: ica_pmp_last vs ica_pmp needed because of STEADYSTATE calculation
+	~ cam << (-(ica - ica_pmp_last)/(2*FARADAY)*(1e7))
+
+	CONSERVE pump + capump = (1e13)*pump0
+	COMPARTMENT width {cam}	: volume has dimensions of um
+	COMPARTMENT (1e13) {pump capump}	: area is dimensionless
+	COMPARTMENT 1(um) {cabulk}
+	COMPARTMENT (1e3)*1(um) {cao}
+
+	cai = (0.001)*cam
+}

cnmodel/mechanisms/capump.mod

@@ -0,0 +1,32 @@
+NEURON {
+	        SUFFIX capump
+	        USEION ca READ cai WRITE ica
+	        RANGE vmax, kmp, ica
+	}
+	
+	UNITS {
+	        (uM) = (micro/liter)
+	        (mM) = (milli/liter)
+	        (mA) = (milliamp)
+	}
+	
+	PARAMETER {
+	        vmax = .0667 (mA/cm2) <0, 1e6>: at 6.3 deg, Q10 = 3
+	        kmp = .2 (uM) <0, 1e6>
+	}
+	
+	ASSIGNED {
+	        celsius (degC)
+	        ica (mA/cm2)
+	        cai (mM)
+	}
+	
+	LOCAL Q, s_celsius
+	
+	BREAKPOINT {
+ 	        if (s_celsius*1(degC) != celsius) {
+	                s_celsius = celsius
+	                Q = 3^((celsius - 6.3)/10 (degC))
+	        }               
+	        ica = vmax*Q*cai/(cai + (.001)*kmp) / 5.18
+}

cnmodel/mechanisms/cleftXmtr.mod

@@ -0,0 +1,88 @@
+COMMENT
+cleftXmtr
+
+This is simple state model that generates "cleft" transmitter, through
+the following scheme:
+
+A netreceive block receives the driving event. This forces XV (the vesicle
+state) to be set to XMax to mimic the release of a vesicle.
+Then:
+XV --> XC --> XU
+where XV is the vesicle transmitter, XC is the cleft transmitter and
+XU is transmitter that has been taken up. The forward rates are finite, and the
+reverse rates are 0 (XU is an absorbing state)
+
+The forward rate kv1 mimics simple first-order diffusion across the cleft
+The forward rate ku1 mimics simple first-order uptake from the cleft
+
+The concentration XC is available to the program as Xmtr.
+XMax is the max cleft concentration of transmitter.
+
+Because vesicle release events at a single presynaptic terminal can be nearly
+simultaneous, it is important that this mechanism does not have a refractory
+period. We also assume that the uptake mechanism is not saturable.
+
+Paul B. Manis, Ph.D.
+UNC Chapel Hill
+3 Jan 2010
+
+ENDCOMMENT
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+DEFINE NSTEP 5
+
+NEURON {
+    POINT_PROCESS cleftXmtr
+    POINTER pre
+    RANGE  KV, KU, XMax
+    RANGE CXmtr, preThresh
+}
+UNITS {
+    (nA) = (nanoamp)
+}
+
+PARAMETER { : Parameters are chosen from best fit to stellate cell data in VCN
+    KV = 531 (/ms)   <0,1e9> : release rate from vesicle
+	KU = 4.17 (/ms) <0,1e3>  : uptake rate
+    XMax = 0.731 (mM)
+    preThresh = 0
+}
+
+ASSIGNED {
+    pre
+    CXmtr (mM)
+    preLast (1)
+    tLast
+}
+
+STATE {
+	XV : Vesicle transmitter (just released)
+    XC : Cleft transmitter (e.g., at receptor)
+    XU : Uptake state (dead state... )
+}
+
+INITIAL {
+    XV = 0
+    XC = 0 (mM)
+    XU = 0
+    CXmtr = 0.0
+    preLast = 0.0
+    tLast = 0.0
+}
+
+BREAKPOINT {
+    SOLVE kstates METHOD sparse
+    CXmtr = XC*XMax
+}
+
+KINETIC kstates {
+    ~ XV <-> XC	(KV, 0.0)
+	~ XC <-> XU	(KU, 0.0)
+    : note that this mechanism has no CONSERVATION : XU can accumulate as much
+    : as needed.
+}
+
+NET_RECEIVE(conc (mM)) { : detect and cause a release event
+	XV = XV + 1
+}

cnmodel/mechanisms/gly.mod

@@ -0,0 +1,65 @@
+TITLE Gly synapse
+
+COMMENT
+	MODIFIED to be a faster GLY synapse, taken from GABA synapse
+	Paul B. Manis - 7 Feb 2000
+
+        simple alpha-synapse that generates a single PSP   
+        *********************************************
+        reference:	McCormick, Wang & Huguenard (1993) 
+			Cerebral Cortex 3(5), 387-398
+        found in:       cat reticular nucleus of thalamus
+        *********************************************
+	Assembled for MyFirstNEURON by Arthur Houweling
+
+
+ENDCOMMENT
+					       
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+NEURON {
+	POINT_PROCESS GlySynapse
+	USEION cl READ ecl VALENCE 1
+	: negative valence not accepted by nrnivmodl
+	RANGE onset, gmaxIPSP, e, g, i, w
+	NONSPECIFIC_CURRENT i
+}
+
+UNITS {
+	(nA) 	= (nanoamp)
+	(mV)	= (millivolt)
+	(nS) 	= (nanomho)
+}
+
+PARAMETER {
+	onset= 25	(ms)
+	gmaxIPSP= 0	(nS)
+	w= 1 				: weight factor for gmaxIPSP
+	ecl		(mV)
+	v		(mV)
+	celsius		(degC)
+}
+
+ASSIGNED { 
+	i 		(nA)  
+	g 		(nS)
+	tadj
+}
+
+UNITSOFF
+INITIAL {
+        tadj = 3^((celsius-23.5)/10)
+}    
+
+BREAKPOINT { LOCAL tt
+	tt= (t-onset)*tadj
+	if ((t>onset)&&(tt<740)) {
+	: the exp() function does not accept arguments smaller than -745
+	  g = w*gmaxIPSP * exp(-tt/15) * (1-exp(-tt/0.5))/0.84
+	}
+	else {g = 0}
+	: -ecl because negative valences can not be specified
+	i = g * (v-(-ecl))	
+}
+UNITSON
+

cnmodel/mechanisms/gly2.mod

@@ -0,0 +1,196 @@
+COMMENT
+-----------------------------------------------------------------------------
+Simple synaptic mechanism derived for first order kinetics of
+binding of transmitter to postsynaptic receptors.
+
+A. Destexhe & Z. Mainen, The Salk Institute, March 12, 1993.
+Last modif. Sept 8, 1993.
+
+Reference:
+
+Destexhe, A., Mainen, Z. and Sejnowski, T.J.  An efficient method for 
+computing synaptic conductances based on a kinetic model of receptor binding.
+Neural Computation, 6: 14-18, 1994.
+-----------------------------------------------------------------------------
+
+During the arrival of the presynaptic spike (detected by threshold 
+crossing), it is assumed that there is a brief pulse (duration=Cdur)
+of neurotransmitter C in the synaptic cleft (the maximal concentration
+of C is Cmax).  Then, C is assumed to bind to a receptor Rc according 
+to the following first-order kinetic scheme:
+
+Rc + C ---(Alpha)--> Ro							(1)
+       <--(Beta)--- 
+
+where Rc and Ro are respectively the closed and open form of the 
+postsynaptic receptor, Alpha and Beta are the forward and backward
+rate constants.  If R represents the fraction of open gates Ro, 
+then one can write the following kinetic equation:
+
+dR/dt = Alpha * C * (1-R) - Beta * R					(2)
+
+and the postsynaptic current is given by:
+
+Isyn = gmax * R * (V-Erev)						(3)
+
+where V is the postsynaptic potential, gmax is the maximal conductance 
+of the synapse and Erev is the reversal potential.
+
+If C is assumed to occur as a pulse in the synaptic cleft, such as
+
+C     _____ . . . . . . Cmax
+      |   |
+ _____|   |______ . . . 0 
+     t0   t1
+
+then one can solve the kinetic equation exactly, instead of solving
+one differential equation for the state variable and for each synapse, 
+which would be greatly time consuming...  
+
+Equation (2) can be solved as follows:
+
+1. during the pulse (from t=t0 to t=t1), C = Cmax, which gives:
+
+   R(t-t0) = Rinf + [ R(t0) - Rinf ] * exp (- (t-t0) / Rtau )		(4)
+
+where 
+   Rinf = Alpha * Cmax / (Alpha * Cmax + Beta) 
+and
+   Rtau = 1 / (Alpha * Cmax + Beta)
+
+2. after the pulse (t>t1), C = 0, and one can write:
+
+   R(t-t1) = R(t1) * exp (- Beta * (t-t1) )				(5)
+
+There is a pointer called "pre" which must be set to the variable which
+is supposed to trigger synaptic release.  This variable is usually the
+presynaptic voltage but it can be the presynaptic calcium concentration, 
+or other.  Prethresh is the value of the threshold at which the release is
+initiated.
+
+Once pre has crossed the threshold value given by Prethresh, a pulse
+of C is generated for a duration of Cdur, and the synaptic conductances
+are calculated accordingly to eqs (4-5).  Another event is not allowed to
+occur for Deadtime milliseconds following after pre rises above threshold.
+
+The user specifies the presynaptic location in hoc via the statement
+	connect pre_GABA[i] , v.section(x)
+
+where x is the arc length (0 - 1) along the presynaptic section (the currently
+specified section), and i is the synapse number (Which is located at the
+postsynaptic location in the usual way via
+	postsynaptic_section {loc_GABA(i, x)}
+Notice that loc_GABA() must be executed first since that function also
+allocates space for the synapse.
+-----------------------------------------------------------------------------
+  GLY SYNAPSE (GLY receptors)
+
+	currently parameters are same as GABA-A until I get the Harty data in here
+	P. Manis 2/10/2000
+
+-----------------------------------------------------------------------------
+ENDCOMMENT
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+NEURON {
+	POINT_PROCESS GLY2
+	POINTER pre
+	RANGE C, R, R0, R1, g, gmax, Erev, lastrelease, Prethresh
+	NONSPECIFIC_CURRENT i
+	GLOBAL Cmax, Cdur, Alpha, Beta, Deadtime, Rinf, Rtau
+}
+UNITS {
+	(nA) = (nanoamp)
+	(mV) = (millivolt)
+	(umho) = (micromho)
+	(mM) = (milli/liter)
+}
+
+PARAMETER {
+
+	Cmax	= 1	(mM)		: max transmitter concentration
+	Cdur	= 1	(ms)		: transmitter duration (rising phase)
+	Alpha	= 0.53	(/ms mM)	: forward (binding) rate
+	Beta	= 0.18	(/ms)		: backward (unbinding) rate
+	Erev	= -80	(mV)		: reversal potential
+	Prethresh = 0 			: voltage level nec for release
+	Deadtime = 1	(ms)		: mimimum time between release events
+	gmax		(umho)		: maximum conductance
+}
+
+ASSIGNED {
+	v		(mV)		: postsynaptic voltage
+	i 		(nA)		: current = g*(v - Erev)
+	g 		(umho)		: conductance
+	C		(mM)		: transmitter concentration
+	R				: fraction of open channels
+	R0				: open channels at start of release
+	R1				: open channels at end of release
+	Rinf				: steady state channels open
+	Rtau		(ms)		: time constant of channel binding
+	pre 				: pointer to presynaptic variable
+	lastrelease	(ms)		: time of last spike
+}
+
+INITIAL {
+	R = 0
+	C = 0
+	R0 = 0
+	R1 = 0
+	Rinf = Cmax*Alpha / (Cmax*Alpha + Beta)
+	Rtau = 1 / ((Alpha * Cmax) + Beta)
+	lastrelease = -999
+}
+
+BREAKPOINT {
+	SOLVE release
+	g = gmax * R
+	i = g*(v - Erev)
+}
+
+PROCEDURE release() { LOCAL q
+	:will crash if user hasn't set pre with the connect statement 
+
+	q = ((t - lastrelease) - Cdur)		: time since last release ended
+
+						: ready for another release?
+	if (q > Deadtime) {
+		if (pre > Prethresh) {		: spike occured?
+			C = Cmax			: start new release
+			R0 = R
+			lastrelease = t
+		}
+						
+	} else if (q < 0) {			: still releasing?
+	
+		: do nothing
+	
+	} else if (C == Cmax) {			: in dead time after release
+		R1 = R
+		C = 0.
+	}
+
+	if (C > 0) {				: transmitter being released?
+
+	   R = Rinf + (R0 - Rinf) * exptable (- (t - lastrelease) / Rtau)
+				
+	} else {				: no release occuring
+
+  	   R = R1 * exptable (- Beta * (t - (lastrelease + Cdur)))
+	}
+
+	VERBATIM
+	return 0;
+	ENDVERBATIM
+}
+
+FUNCTION exptable(x) { 
+	TABLE  FROM -10 TO 10 WITH 2000
+
+	if ((x > -10) && (x < 10)) {
+		exptable = exp(x)
+	} else {
+		exptable = 0.
+	}
+}

cnmodel/mechanisms/hcno.mod

@@ -0,0 +1,101 @@
+TITLE h current for Octopus cells of Cochlear Nucleus
+: From Bal and Oertel (2000)
+: M.Migliore Oct. 2001
+: Modified, P. Manis July 2014.
+
+NEURON {
+    THREADSAFE
+    SUFFIX hcno
+    NONSPECIFIC_CURRENT i
+    RANGE  gbar, eh, gh
+    GLOBAL hinf, tau1, tau2
+}
+
+UNITS {
+    (mA) = (milliamp)
+    (mV) = (millivolt)
+    (pS) = (picosiemens)
+    (um) = (micron)
+    R = (k-mole)(joule/degC)
+    F = (faraday)(kilocoulombs)
+}
+
+PARAMETER {
+    gbar = 0.0005       (mho/cm2)   
+                                
+    vhalf1  = -50   (mV)        : v 1/2 for forward
+    vhalf2  = -84   (mV)        : v 1/2 for backward    
+    gm1   = 0.3 :(mV)           : slope for forward
+    gm2   = 0.6  :    (mV)      : slope for backward
+    zeta1   = 3 :   (/ms)       
+    zeta2   = 3 :   (/ms)       
+    a01 = 0.008  (/ms)
+    a02 = 0.0029 (/ms)
+    frac = 0.0
+    c0 = 273.16  (degC)
+    thinf  = -66    (mV)        : inact inf slope   
+    qinf  = 7   (mV)        : inact inf slope 
+    q10tau = 4.5             : from Magee (1998)
+    v       (mV)
+    q10g = 2.0    : Rothman...
+}
+
+
+ASSIGNED {
+    celsius (degC)
+    i       (mA/cm2)
+    gh      (mho/cm2)
+    eh      (mV)    : must be explicitly def. in hoc
+    hinf 
+    tau1 (ms)
+    tau2 (ms) 
+    qg ()  : computed q10 for gnabar based on q10g
+    q10 ()
+	ssih
+}
+ 
+
+STATE { h1 h2 }
+
+BREAKPOINT {
+    SOLVE states METHOD derivimplicit
+    gh = qg*gbar*(h1*frac + h2*(1.0-frac))
+    i = gh * (v - eh)
+} 
+
+INITIAL {
+    qg = q10g^((celsius-33.0)/10.0 (degC))  :note original measurements made at 33 C
+    q10 = q10tau^((celsius - 22.0)/10.0 (degC)) : if you don't like room temp, it can be changed!
+    rates(v)
+    h1=hinf
+    h2=hinf
+	ssih = 0.
+}
+
+DERIVATIVE states {   
+    rates(v)      
+    h1' = (hinf - h1)/tau1
+    h2' = (hinf - h2)/tau2
+}
+
+PROCEDURE rates(v (mV)) {  
+    tau1 = bet1(v)/(q10*a01*(1.0+alp1(v)))
+    tau2 = bet2(v)/(q10*a02*(1.0+alp2(v)))
+    hinf = 1.0/(1.0+exp((v-thinf)/qinf))
+}
+
+FUNCTION alp1(v(mV)) {
+  alp1 = exp(1e-3*zeta1*(v-vhalf1)*F/(R*(c0+celsius))) 
+}
+
+FUNCTION bet1(v(mV)) {
+  bet1 = exp(1.e-3*zeta1*gm1*(v-vhalf1)*F/(R*(c0+celsius)))
+}
+
+FUNCTION alp2(v(mV)) {
+  alp2 = exp(1.e-3*zeta2*(v-vhalf2)*F/(R*(c0+celsius))) 
+}
+
+FUNCTION bet2(v(mV)) {
+  bet2 = exp(1.e-3*zeta2*gm2*(v-vhalf2)*F/(R*(c0+celsius))) 
+}

cnmodel/mechanisms/hcno_bo.mod

@@ -0,0 +1,103 @@
+TITLE h current for Octopus cells of Cochlear Nucleus
+: From Bal and Oertel (2000)
+
+: Modified, P. Manis July 2014, 2017 
+: Parameters from McGinley et al. paper
+
+NEURON {
+    THREADSAFE
+    SUFFIX hcnobo
+    NONSPECIFIC_CURRENT i
+    RANGE  gbar, eh, gh, q10tau
+    GLOBAL hinf, tau1, tau2
+}
+
+UNITS {
+    (mA) = (milliamp)
+    (mV) = (millivolt)
+    (pS) = (picosiemens)
+    (um) = (micron)
+    R = (k-mole)(joule/degC)
+    F = (faraday)(kilocoulombs)
+}
+
+PARAMETER {
+    gbar = 0.0005       (mho/cm2)   
+                                
+    vhalf1  = -70 (mV) :  -50   (mV)        : v 1/2 for forward
+    vhalf2  = -84   (mV)        : v 1/2 for backward    
+    gm1   = 0.3 :(mV)           : slope for forward
+    gm2   = 0.6  :    (mV)      : slope for backward
+    zeta1   = 3 :   (/ms)       
+:    zeta2   = 3 :   (/ms)       
+    a01 = 4.8e-3 (/ms)  : was 0.008
+    a02 = 2.9e-3 (/ms) : was 0.0029 (/ms)
+    frac = 0.8
+    c0 = 273.16  (degC)
+    thinf  = -72.4    (mV)        : inact inf slope   
+    qinf  = 5.3   (mV)        : inact inf slope 
+    q10tau = 4.5             : from Magee (1998)
+    v       (mV)
+}
+
+
+ASSIGNED {
+    celsius (degC)
+    i       (mA/cm2)
+    gh      (mho/cm2)
+    eh      (mV)    : must be explicitly def. in hoc
+    hinf 
+    tau1 (ms)
+    tau2 (ms) 
+    q10 ()
+	ssih
+    ct
+}
+ 
+
+STATE { h1 h2 }
+
+BREAKPOINT {
+    SOLVE states METHOD cnexp
+    : SOLVE states METHOD derivimplicit
+    gh = gbar*(h1*frac + h2*(1.0-frac))
+    i = gh * (v - eh)
+} 
+
+INITIAL {
+    ct = 1e-3*zeta1*F/(R*(c0+celsius))
+    
+    q10 = q10tau^((celsius - 33.0)/10.0 (degC)) : Measurements at 33
+    rates(v)
+    h1=hinf
+    h2=hinf
+	ssih = 0.
+}
+
+DERIVATIVE states {   
+    rates(v)      
+    h1' = (hinf - h1)/tau1
+    h2' = (hinf - h2)/tau2
+}
+
+PROCEDURE rates(v (mV)) {  
+    tau1 = bet1(v)/(q10*a01*(1.0+alp1(v)))
+    tau2 = bet2(v)/(q10*a02*(1.0+alp2(v)))
+    hinf = 1.0/(1.0+exp((v-thinf)/qinf))
+}
+
+FUNCTION alp1(v(mV)) {
+  alp1 = exp((v-vhalf1)*ct) 
+}
+
+FUNCTION bet1(v(mV)) {
+  bet1 = exp(gm1*(v-vhalf1)*ct)
+}
+
+FUNCTION alp2(v(mV)) {
+  alp2 = exp((v-vhalf2)*ct) 
+}
+
+FUNCTION bet2(v(mV)) {
+  bet2 = exp(gm2*(v-vhalf2)*ct) 
+}

cnmodel/mechanisms/iStim.mod

@@ -0,0 +1,48 @@
+COMMENT
+iStim
+
+This is a point current injection (like an electrode).
+Positive values of the amplitude depolarize the cell
+and in the presence of the extracellular mechanism there will be a change
+in vext since i is not a transmembrane current but a current injected
+directly to the inside of the cell.
+
+This is meant to be used with Vector Play...
+ENDCOMMENT
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+DEFINE NSTEP 5
+
+NEURON {
+    THREADSAFE
+    POINT_PROCESS iStim
+    RANGE  dur, delay, iMax
+    ELECTRODE_CURRENT i
+}
+UNITS {
+    (nA) = (nanoamp)
+}
+PARAMETER {
+    dur (ms)   <0,1e9>
+	delay (ms) <0,1e9>
+    iMax (nA)
+}
+ASSIGNED {
+i (nA)
+}
+
+INITIAL {
+    i = 0
+}
+
+BREAKPOINT {
+COMMENT
+if(t < delay || t > (delay+dur)) {
+        i = 0
+    }
+	if(t >= delay && t <= (delay+dur)) {
+		i = iMax
+	}
+ENDCOMMENT
+}

cnmodel/mechanisms/ihpkj.mod

@@ -0,0 +1,56 @@
+: Ih current
+: Created 8/6/02 - nwg
+
+NEURON {
+	THREADSAFE
+    SUFFIX hpkj
+	NONSPECIFIC_CURRENT i
+	RANGE gbar, gh, eh
+	GLOBAL ninf, ntau
+}
+
+UNITS {
+	(mA) = (milliamp)
+	(mV) = (millivolt)
+	(S) = (siemens)
+}
+
+PARAMETER {
+	v	 	(mV)
+	
+	gbar = .0001	(S/cm2)
+
+	eh = -30	(mV)
+}
+
+ASSIGNED {
+	gh (mho/cm2)
+    i (mA/cm2)
+	ninf
+	ntau (ms)
+}
+
+STATE {
+	n
+}
+
+INITIAL {
+	rates(v)
+	n = ninf
+}
+
+BREAKPOINT {
+	SOLVE states METHOD cnexp
+	gh = gbar * n
+    i = gh*(v - eh)
+}
+
+DERIVATIVE states {
+	rates(v)
+	n' = (ninf - n)/ntau
+}
+
+PROCEDURE rates(v (mV)) {
+	ninf = 1/(1+exp((v+90.1(mV))/9.9(mV)))
+	ntau = (1000) * (0.19 (s) + 0.72 (s)*exp(-((v-(-81.5(mV)))/11.9(mV))^2))
+}

cnmodel/mechanisms/ihpyr.mod

@@ -0,0 +1,134 @@
+TITLE ihpyr.mod   DCN pyramidal cell model H-current
+ 
+COMMENT
+
+This model is part a Dorsal Cochlear Nucleus Pyramidal point cell
+based on kinetic data from Kanold and Manis (1999) and Kanold's dissertation (1999)
+
+-- 15 Jan 1999 P. Manis
+
+Added export of start states for some variables to do perturbation tests
+These start values replace the "inf" values used in the initialization procedure
+Note that if the start variable is set to a value less than 0, 
+then the default initialization will be done. Typically I use a value of -1 for this flagging
+Note also that it is possible to set the initial values > 1 but this is meaningless in terms of
+the present equations. 
+-- 5 Feb 1999 P. Manis
+
+Added Patrick's version of ih as ihpyr
+Model is from Destexhe and Babloyantz 1993; Destexhe et al. 1993
+
+
+2/10/02. P. Manis.
+7/23/2014 P. Manis - separated from pyr.mod.
+
+ENDCOMMENT
+ 
+ 
+UNITS {
+ (mA) = (milliamp)
+ (mV) = (millivolt)
+}
+ 
+
+NEURON {
+ THREADSAFE
+ SUFFIX ihpyr
+ USEION na READ ena WRITE ina
+ USEION k READ ek WRITE ik
+ NONSPECIFIC_CURRENT i
+: USEION h READ eh WRITE ih VALENCE 1
+ RANGE eh
+:
+ RANGE gh, kh_m_inf, kh_n_inf, aih, gbar, ghvshift
+ RANGE kh_m_tau, kh_n_tau
+
+}
+ 
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+ 
+PARAMETER {
+ v (mV)
+ celsius (degC)
+ dt (ms)
+ ek (mV) : = -81.5 (mV)
+ ena (mV) : = 50.0 (mV)
+ gbar = 0.00025 (mho/cm2) <0,1e9>
+ ghvshift = 0 (mV)
+ eh (mV) : = -43.0(mV)
+}
+ 
+STATE {
+       khm khn
+}
+ 
+ASSIGNED {
+	gh (mho/cm2)
+	ina (mA/cm2)
+	ik (mA/cm2)
+	ih (mA/cm2)
+    i (mA/cm2)
+	kh_m_inf kh_n_inf
+	kh_m_tau kh_n_tau	
+	aih
+}
+
+BREAKPOINT {
+	SOLVE states METHOD cnexp
+	aih = khm*khn
+	gh = gbar*aih
+ 	ih = gh*(v - eh)
+    i = ih
+}
+
+UNITSOFF 
+ 
+INITIAL {
+	rates(v)
+	khm = kh_m_inf
+	khn = kh_n_inf
+}
+
+
+DERIVATIVE states {  
+	rates(v)
+	khm' = (kh_m_inf - khm) / kh_m_tau
+	khn' = (kh_n_inf - khn) / kh_n_tau
+}
+
+
+LOCAL q10
+
+PROCEDURE rates(v(mV)) {  :Computes rate and other constants at current v.
+	                      :Call once from HOC to initialize inf at resting v.
+	q10 = 3^((celsius - 22)/10 (degC))
+
+	:"kh" adaptation of Destexhe hyp-activated cation current by Patrick Kanold
+	kh_m_inf = kh_m(v) 
+	kh_n_inf = kh_n(v)
+	kh_m_tau = kh_mt(v)
+	kh_n_tau = kh_nt(v) 
+}
+ 
+: Make these as functions so we can view them from hoc, although this 
+: may slow things down a bit
+
+
+FUNCTION kh_m(x (mV)) {
+	kh_m = 1/(1+exp((x+68.9+ghvshift)/6.5))
+}
+
+FUNCTION kh_n(x (mV)) {
+	kh_n = 1/(1+exp((x+68.9+ghvshift)/6.5)) : same as kh_m, but for completeness, compute this
+}
+
+FUNCTION kh_mt(v (mV)) {
+	kh_mt = exp((v+183.6+ghvshift)/15.24)
+}
+
+FUNCTION kh_nt(v (mV)) {
+	kh_nt = exp((v+158.6+ghvshift)/11.2)/(1+exp((v+75+ghvshift)/5.5))
+}
+
+
+

cnmodel/mechanisms/ihpyr_adj.mod

@@ -0,0 +1,142 @@
+TITLE ihpyr_adj.mod   DCN pyramidal cell model H-current
+ 
+COMMENT
+
+This model is part a Dorsal Cochlear Nucleus Pyramidal point cell
+based on kinetic data from Kanold and Manis (1999) and Kanold's dissertation (1999)
+
+-- 15 Jan 1999 P. Manis
+
+Added export of start states for some variables to do perturbation tests
+These start values replace the "inf" values used in the initialization procedure
+Note that if the start variable is set to a value less than 0, 
+then the default initialization will be done. Typically I use a value of -1 for this flagging
+Note also that it is possible to set the initial values > 1 but this is meaningless in terms of
+the present equations. 
+-- 5 Feb 1999 P. Manis
+
+Added Patrick's version of ih as ihpyr
+Model is from Destexhe and Babloyantz 1993; Destexhe et al. 1993
+
+
+2/10/02. P. Manis.
+7/23/2014 P. Manis - separated from pyr.mod.
+
+7/23/2018 P. Manis - created "ihpyr_adj"
+ihpyr_adj has an adjustable q10 for fitting against experimental data
+
+ENDCOMMENT
+ 
+ 
+UNITS {
+ (mA) = (milliamp)
+ (mV) = (millivolt)
+}
+ 
+
+NEURON {
+ THREADSAFE
+ SUFFIX ihpyr_adj
+ USEION na READ ena WRITE ina
+ USEION k READ ek WRITE ik
+ NONSPECIFIC_CURRENT i
+: USEION h READ eh WRITE ih VALENCE 1
+ RANGE eh
+:
+ RANGE gh, kh_m_inf, kh_n_inf, aih, gbar, ghvshift
+ RANGE kh_m_tau, kh_n_tau
+ GLOBAL q10, q10f
+
+}
+ 
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+ 
+PARAMETER {
+ v (mV)
+ celsius (degC)
+ dt (ms)
+ ek (mV) : = -81.5 (mV)
+ ena (mV) : = 50.0 (mV)
+ gbar = 0.00025 (mho/cm2) <0,1e9>
+ ghvshift = 0 (mV)
+ eh (mV) : = -43.0(mV)
+ q10 = 3.0 (1)
+}
+ 
+STATE {
+       khm khn
+}
+ 
+ASSIGNED {
+	gh (mho/cm2)
+	ina (mA/cm2)
+	ik (mA/cm2)
+	ih (mA/cm2)
+    i (mA/cm2)
+	kh_m_inf kh_n_inf
+	kh_m_tau kh_n_tau	
+	aih
+    q10f (1)
+}
+
+BREAKPOINT {
+	SOLVE states METHOD cnexp
+	aih = khm*khn
+	gh = gbar*aih
+ 	ih = gh*(v - eh)
+    i = ih
+}
+
+UNITSOFF 
+ 
+INITIAL {
+	rates(v)
+	khm = kh_m_inf
+	khn = kh_n_inf
+}
+
+
+DERIVATIVE states {  
+	rates(v)
+	khm' = (kh_m_inf - khm) / kh_m_tau
+	khn' = (kh_n_inf - khn) / kh_n_tau
+}
+
+
+: LOCAL q10f
+
+PROCEDURE rates(v(mV)) {  :Computes rate and other constants at current v.
+	                      :Call once from HOC to initialize inf at resting v.
+	q10f = q10^((celsius - 22)/10 (degC))
+
+	:"kh" adaptation of Destexhe hyp-activated cation current by Patrick Kanold
+	: adding q10 does not shift activation curves
+    kh_m_inf = kh_m(v) 
+	kh_n_inf = kh_n(v)
+	: adding the q10 just changes the rate for the taus (1/(a+b))
+    kh_m_tau = kh_mt(v)/q10f
+	kh_n_tau = kh_nt(v)/q10f  
+}
+ 
+: Make these as functions so we can view them from hoc, although this 
+: may slow things down a bit
+
+
+FUNCTION kh_m(x (mV)) {
+	kh_m = 1/(1+exp((x+68.9+ghvshift)/6.5))
+}
+
+FUNCTION kh_n(x (mV)) {
+	kh_n = 1/(1+exp((x+68.9+ghvshift)/6.5)) : same as kh_m, but for completeness, compute this
+}
+
+FUNCTION kh_mt(v (mV)) {
+	kh_mt = exp((v+183.6+ghvshift)/15.24)
+}
+
+FUNCTION kh_nt(v (mV)) {
+	kh_nt = exp((v+158.6+ghvshift)/11.2)/(1+exp((v+75+ghvshift)/5.5))
+}
+
+
+

cnmodel/mechanisms/ihsgc_apical.mod

@@ -0,0 +1,155 @@
+TITLE ihsgc-apical.mod - Spiral Ganglion Cell Ih current for Apical Region
+
+COMMENT
+Ih for Spiral ganglion cells.
+Kinetcs are based on average fits to mouse SGCs,
+This model is for just the apical cell group.
+Data used to establish the kinetic parameters were collected by
+Qing Liu and Robin Davis (Rutgers).
+Data were taken at room temperature.
+Kinetic parameters were extracted by curve fitting for fast and
+slow components from activation and deactivation (using
+the program Ihfit4b.py).
+
+Implementation by Paul B. Manis, January-April, 2012.
+Revised December 2013, January 2014.
+        # of parameters in the fit were decreased (tau uses one v and scale factor).
+Parameters are shown in the tables in Liu et al., JARO 2014.
+
+March 13, 2014: Corrected version with boltzmax for slow component
+July 2014: made threadsafe, changed solver
+
+pmanis@med.unc.edu
+
+Note: vshift parameter is nominally 0. This parameter can
+shift the entire activation and rate curves, keeping them
+in register for each component of the conductance.
+
+ENDCOMMENT
+
+UNITS {
+	(mA) = (milliamp)
+	(mV) = (millivolt)
+	(nA) = (nanoamp)
+}
+
+NEURON {
+	THREADSAFE
+    SUFFIX ihsgcApical
+	NONSPECIFIC_CURRENT i
+	RANGE gbar, gh, ih, eh, vshift
+	RANGE vh, k, vhs, ks
+	RANGE rinf, rtau, sinf, stau
+}
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+PARAMETER {
+		v (mV)
+		celsius = 22 (degC)
+		dt (ms)
+		gbar = 0.00318 (mho/cm2) <0,1e9>
+        eh = -41 (mV)
+
+: Parameters from kinetic analysis
+: Format for NEURON MOD file: 
+
+: (Run on date =  2014-01-01 12:55:35.786524 )
+
+: lmfit, Constrained model t(v) = DC + 1/(a * exp((v+vh)/k1) + a*exp(-(v+vh)/k2))
+: A. Fast component (Fast trace):
+
+: Boltzmann:
+        vh = -101.831 (mV)
+        k = 12.431 (mV)
+        vshift = 0.0 (mV)
+        afast = 0.4225 : fraction that is fast.
+
+: Tau
+        taufac = 1.0 (1)
+        taumin = 0 (ms)
+        tausc1 = 0.00445778 (/ms) : (ms)
+        vtau1 = 87.0705 (mV)
+        kfac1 = 53.0338 (mV)
+        kfac2 = 21.5365 (mV)
+
+
+: B. Slow component (Cyan trace):
+: (Run on date =  2014-01-01 12:55:35.786883 )
+
+: Boltzmann:
+        svh1 = -86.762 (mV)
+        sk1 = 4.430 (mV) : double boltzmann
+        svh2 = -115.227 (mV)
+        sk2 = 9.675 (mV)
+        svshift = 0.0 (mV)
+        sba2 = 0.400557  : relative amplitude slow component 2 compared to slow 1 (slow2/(slow2+slow1))
+        aslow = 0.5775 : total slow
+		boltzmax = 0.5019571 : normalization factor 
+							 : (computed numerically in Sage to make double boltz max = 1.0)
+
+: stau
+        staufac = 1.0 (1)
+        staumin = 0 (ms)
+        stausc1 = 0.00093656 (/ms) : (ms)
+        svtau1 = 89.6097 (mV)
+        skfac1 = 25.392 (mV)
+        skfac2 = 26.4195 (mV)
+
+}
+
+STATE {
+		r
+		s
+}
+
+ASSIGNED {
+	gh (mho/cm2)
+	i (mA/cm2)
+	ih (mA/cm2)
+	rinf
+	rtau (ms)
+	sinf
+	stau (ms)
+    q10 ()
+}
+
+
+BREAKPOINT {
+	SOLVE states  METHOD cnexp
+	gh = gbar*(afast*(r^2)+aslow*s) : Balance between fast and slow determined by afast and aslow
+	ih = gh*(v - eh)
+	i = ih
+}
+
+INITIAL {
+	q10 = 3.0^((celsius - 22.0)/10.0 (degC)) : adjust for temperature...
+	rates(v)
+	r = rinf
+	s = sinf
+}
+
+DERIVATIVE states {
+	rates(v)
+	r' = (rinf - r)/rtau
+    s' = (sinf - s)/stau
+}
+
+LOCAL rt, st
+PROCEDURE rates(v (mV)) {  : Computes rate and activation at voltage = v.
+
+: fast component - standard HH-like kinetics.
+	rinf = 1.0 / (1+exp((v - vh + vshift) / k))^0.5
+	rt = tausc1*exp((v + vtau1 + vshift) / kfac1) + tausc1*exp(-(v + vtau1 + vshift) / kfac2)
+	rtau = (taumin + taufac/rt)
+
+: slow component
+: double boltzman activation function (decreasing conductance), unequal sharing. 
+	sinf = 1. / (1 + exp((v - svh1 + vshift) / sk1))
+	st =   1. / (1 + exp((v - svh2 + vshift) / sk2))
+	sinf = (1-sba2)*sinf - sba2*st
+	sinf = sinf/boltzmax : make sinf [0..1]
+	stau = staufac / (stausc1*exp((v + svtau1 + vshift) / skfac1) + stausc1*exp(-(v + svtau1 + vshift) / skfac2))
+	stau = (stau + staumin)
+}
+

cnmodel/mechanisms/ihsgc_basalmiddle.mod

@@ -0,0 +1,154 @@
+TITLE ihsgc-basalmiddle.mod - Spiral Ganglion Cell Ih current for basal and middle Regions
+
+COMMENT
+Ih for Spiral ganglion cells.
+Kinetcs are based on average fits to mouse SGCs,
+This model is for the basal and middle cell groups (averaged).
+Data used to establish the kinetic parameters were collected by
+Qing Liu and Robin Davis (Rutgers).
+Data were taken at room temperature.
+Kinetic parameters were extracted by curve fitting for fast and
+slow components from activation and deactivation (using
+the program Ihfit4b.py).
+
+Implementation by Paul B. Manis, January-April, 2012.
+Revised December 2013, January 2014.
+        # of parameters in the fit were decreased (tau uses one v and scale factor).
+Parameters are shown in the tables in Liu et al., JARO 2014.
+
+March 13, 2014: Corrected version with boltzmax for slow component
+July 2014: made threadsafe, changed solver
+
+pmanis@med.unc.edu
+
+Note: vshift parameter is nominally 0. This parameter can
+shift the entire activation and rate curves, keeping them
+in register for each component of the conductance.
+
+ENDCOMMENT
+
+UNITS {
+	(mA) = (milliamp)
+	(mV) = (millivolt)
+	(nA) = (nanoamp)
+}
+
+NEURON {
+	THREADSAFE
+    SUFFIX ihsgcBasalMiddle
+	NONSPECIFIC_CURRENT i
+	RANGE gbar, gh, ih, eh, vshift
+	RANGE vh, k, vhs, ks
+	RANGE rinf, rtau, sinf, stau
+}
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+PARAMETER {
+		v (mV)
+		celsius = 22 (degC)
+		dt (ms)
+		gbar = 0.00318 (mho/cm2) <0,1e9>
+        eh = -41 (mV)
+
+: Parameters from kinetic analysis
+: Formatted for NEURON MOD file
+: (Run on date =  2014-01-01 12:52:22.776598 )
+
+: lmfit, Constrained model t(v) = DC + 1/(a * exp((v+vh)/k1) + a*exp(-(v+vh)/k2))
+: A. Fast component (Fast trace):
+
+: Boltzmann:
+        vh = -105.298 (mV)
+        k = 12.359 (mV)
+        vshift = 0.0 (mV)
+        afast = 0.4471 : fraction that is fast.
+
+: Tau
+        taufac = 1.0 (1)
+        taumin = 0 (ms)
+        tausc1 = 0.00417518 (/ms)
+        vtau1 = 87.0836 (mV)
+        kfac1 = 28.1667 (mV)
+        kfac2 = 21.4809 (mV)
+
+
+: B. Slow component:
+: (Run on date =  2014-01-01 12:52:22.777259 )
+: Boltzmann:
+        svh1 = -91.860 (mV)
+        sk1 = 4.883 (mV) : double boltzmann
+        svh2 = -110.209 (mV)
+        sk2 = 3.927 (mV)
+        svshift = 0.0 (mV)
+        sba2 = 0.337216  : relative amplitude slow component 2 compared to slow 1 (slow2/(slow2+slow1))
+        aslow = 0.5529 : total slow
+		boltzmax = 0.5551729 : normalization factor 
+							 : (computed numerically in Sage to make double boltz max = 1.0)
+
+: stau
+        staufac = 1.0 (1)
+        staumin = 0 (ms)
+        stausc1 = 0.00104354 (/ms)
+        svtau1 = 105.816 (mV)
+        skfac1 = 40.0291 (mV)
+        skfac2 = 20.2273 (mV)
+
+}
+
+STATE {
+		r
+		s
+}
+
+ASSIGNED {
+	gh (mho/cm2)
+	i (mA/cm2)
+	ih (mA/cm2)
+	rinf
+	rtau (ms)
+	sinf
+	stau (ms)
+    q10 ()
+}
+
+
+BREAKPOINT {
+	SOLVE states  METHOD cnexp
+	gh = gbar*(afast*(r^2)+aslow*s) : Balance between fast and slow determined by afast and aslow
+	ih = gh*(v - eh)
+	i = ih
+}
+
+
+INITIAL {
+	q10 = 3.0^((celsius - 22.0)/10.0 (degC)) : adjust for temperature...
+	rates(v)
+	r = rinf
+	s = sinf
+}
+
+DERIVATIVE states {  : Updates state variables r and s
+	rates(v)		  : at the current voltage
+	r' = (rinf - r )/rtau
+	s' = (sinf - s)/stau
+}
+
+LOCAL rt, st
+PROCEDURE rates(v (mV)) {  : Computes rate and activation at voltage = v.
+
+: fast component - standard HH-like kinetics.
+	rinf = 1.0 / (1+exp((v - vh + vshift) / k))^0.5
+	rt = tausc1*exp((v + vtau1 + vshift) / kfac1) + tausc1*exp(-(v + vtau1 + vshift) / kfac2)
+	rtau = (taumin + taufac/rt)
+
+: slow component
+: double boltzman activation function (decreasing conductance), unequal sharing. 
+	sinf = 1. / (1 + exp((v - svh1 + vshift) / sk1))
+	st   = 1. / (1 + exp((v - svh2 + vshift) / sk2))
+	sinf = (1-sba2)*sinf - sba2*st
+	sinf = sinf/boltzmax : make sinf [0..1]
+	
+	stau = staufac / (stausc1*exp((v + svtau1 + vshift) / skfac1) + stausc1*exp(-(v + svtau1 + vshift) / skfac2))
+	stau = (stau + staumin)
+}

cnmodel/mechanisms/ihvcn.mod

@@ -0,0 +1,79 @@
+TITLE jsr.mod  VCN conductances
+
+COMMENT
+Ih for VCN neurons - average from several studies in auditory neurons
+
+Implementation by Paul B. Manis, April (JHU) and Sept, (UNC)1999.
+revised 2/28/04 pbm
+
+pmanis@med.unc.edu
+
+Modifed implementation; includes all temperature scaling, passes modlunit
+7/10/2014 pbm
+
+ENDCOMMENT
+
+UNITS {
+    (mA) = (milliamp)
+    (mV) = (millivolt)
+    (nA) = (nanoamp)
+}
+
+NEURON {
+    THREADSAFE
+    SUFFIX ihvcn
+    NONSPECIFIC_CURRENT i
+    RANGE gbar, gh, i, eh
+    GLOBAL rinf, rtau
+}
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+PARAMETER {
+    v (mV)
+    dt (ms)
+    gbar = 0.00318 (mho/cm2) <0,1e9>
+    q10tau = 3.0
+}
+
+STATE {
+    r
+}
+
+ASSIGNED {
+    celsius (degC)
+    gh (mho/cm2)
+    eh (mV)
+    i (mA/cm2)
+    rinf
+    rtau (ms)
+    q10 ()
+}
+
+BREAKPOINT {
+    SOLVE states METHOD cnexp
+
+    gh = gbar*r
+    i = gh*(v - eh)
+}
+
+INITIAL {
+    q10 = q10tau^((celsius - 22)/10 (degC)) : if you don't like room temp, it can be changed!
+    rates(v)
+    r = rinf
+}
+
+DERIVATIVE states {  :Computes state variables m, h, and n
+    rates(v)      :         at the current v and dt.
+    r' = (rinf - r)/rtau
+}
+
+PROCEDURE rates(v (mV)) {  :Computes rate and other constants at current v.
+              :Call once from HOC to initialize inf at resting v.
+
+    rinf = 1 / (1+exp((v + 76) / 7 (mV)))
+    rtau = (100000 (ms)/ (237*exp((v+60) / 12 (mV)) + 17*exp(-(v+60) / 14 (mV)))) + 25
+    rtau = rtau/q10
+
+}
+

cnmodel/mechanisms/inav11.mod

@@ -0,0 +1,188 @@
+:
+:  ichanWT2005.mod 
+:
+:   Alan Goldin Lab, University of California, Irvine
+:   Jay Lickfett - Last Modified: 6 July 2005
+:
+:  This file is the Nav1.1 wild-type channel model described in:
+:
+:		Barela et al. An Epilepsy Mutation in the Sodium Channel SCN1A That Decreases
+:	    Channel Excitability.  J. Neurosci. 26(10): p. 2714-2723 
+:
+:
+:   The model is derived from the one described in:
+:
+:    	Spampanato et al. (2004a) Increased Neuronal Firing in Computer Simulations 
+:		of Sodium Channel Mutations that Cause Generalized Epilepsy with Febrile Seizures Plus.
+:		Journal of Neurophysiology 91:2040-2050
+:
+:	and
+:
+:	 	Spampanato et al. (2004b) A Novel Epilepsy Mutation 
+:   	in the Sodium Channel SCN1A Identifies a Cytoplasmic Domain for 
+:		Beta Subunit Interaction. J. Neurosci. 24(44):10022-10034
+:
+ 
+: delayed rectifier removed (p.b.manis 2/22/2009)
+
+
+
+
+UNITS {
+    (mA) = (milliamp)
+    (mV) = (millivolt)
+    (uF) = (microfarad)
+    (molar) = (1/liter)
+    (nA) = (nanoamp)
+    (mM) = (millimolar)
+    (um) = (micron)
+    (S) = (siemens)
+    FARADAY = 96520 (coul)
+    R = 8.3134  (joule/degC)
+
+}
+
+ 
+NEURON {
+    THREADSAFE
+    SUFFIX nav11
+    USEION na READ ena WRITE ina VALENCE 1
+    RANGE gna
+    RANGE gbar
+    RANGE minf, mtau, hinf, htau, sinf, stau, inat, m, h, s
+	RANGE vsna : voltage shift parameter
+}
+
+ 
+INDEPENDENT {t FROM 0 TO 100 WITH 100 (ms)}
+
+ 
+PARAMETER {
+    vsna = 4.3 (mV)
+    celsius (degC)
+    dt (ms) 
+    ena (mV)
+    :enat = 50  (mV)
+    gbar = 0.1 (mho/cm2)
+    q10 = 3.0 (1)
+}
+
+
+ASSIGNED {
+      
+    v (mV) 
+    gna (mho/cm2)
+    ina (mA/cm2)
+    minf hinf sinf
+    mtau (ms) htau (ms) stau (ms)
+    mexp hexp sexp
+:	vsna (mV)
+} 
+
+
+STATE {
+    m h s
+}
+ 
+
+BREAKPOINT {
+    SOLVE states METHOD cnexp
+    gna = gbar*m*m*m*h*s
+    ina = gna*(v - ena)
+}
+
+ 
+UNITSOFF
+
+ 
+INITIAL {
+
+    trates(v)
+    
+    m = minf
+    h = hinf
+    s = sinf
+
+}
+
+
+DERIVATIVE states {        : Computes state variables m, h, s and n
+                            : at the current v and dt.        
+    rates(v)
+    m' = (minf - m)/mtau
+    h' = (hinf - h)/htau
+    s' = (sinf - s)/stau
+
+}
+ 
+
+LOCAL qt
+
+
+PROCEDURE rates(v (mV)) {   :Computes rate and other constants at current v.
+                            :Call once from HOC to initialize inf at resting v.
+
+    LOCAL  alpha, beta, sum
+    qt = q10^((celsius - 22)/10) : original recordings in Barela et al made at "room temperature"
+    
+    
+    : "m" sodium activation system
+    minf = f_minf(v)
+    mtau = f_mtau(v)/qt
+
+    : "h" sodium fast inactivation system
+    hinf = f_hinf(v)
+    htau = f_htau(v)/qt
+
+    : "s" sodium slow inactivation system
+    sinf = f_sinf(v)
+    stau = f_stau(v)/qt
+
+}
+ 
+
+PROCEDURE trates(v (mV)) {  :Build table with rate and other constants at current v.
+                            :Call once from HOC to initialize inf at resting v.
+    LOCAL tinc
+ 
+    TABLE minf, mexp, hinf, hexp, sinf, sexp, mtau, htau, stau
+       DEPEND dt, celsius FROM -100 TO 100 WITH 200
+                           
+    rates(v)    : not consistently executed from here if usetable_hh == 1
+                : so don't expect the tau values to be tracking along with
+                : the inf values in hoc
+
+    tinc = -dt  :  * q10 q10 is handled in rates, above
+    mexp = 1 - exp(tinc/mtau)
+    hexp = 1 - exp(tinc/htau)
+    sexp = 1 - exp(tinc/stau)
+}
+
+FUNCTION f_minf(v (mV)) {
+        f_minf = 1/(1+exp(-(v+27.4+vsna)*4.7*0.03937))
+
+        }
+FUNCTION f_mtau(v (mV)) {
+        f_mtau = 0.15
+        }
+
+FUNCTION f_hinf(v (mV)) {
+        f_hinf = 1/(1+exp((v+41.9+vsna)/6.7))
+    }
+
+FUNCTION f_htau(v (mV)) {
+        f_htau = 23.12*exp(-0.5*((v+77.58+vsna)/43.92)^2)
+        }
+
+
+FUNCTION f_sinf(v (mV)) {
+    f_sinf = 1/(1+exp((v+46.0+vsna)/6.6))
+        }
+
+FUNCTION f_stau(v (mV)) {
+        f_stau = 1000*140.4*exp(-0.5*((v+71.3+vsna)/30.9)^2)
+    }
+
+
+UNITSON
+

cnmodel/mechanisms/jsrnaf.mod

@@ -0,0 +1,136 @@
+TITLE jsrnaf.mod VCN Na conductance, fast model
+
+COMMENT
+gnaf is the modified form used in his
+1993 M.S. thesis (as in Rothman et al., J. Neurophysiol. 70:2562, 1993),
+with rapid recovery from inactivation for potentials below rest.
+
+Implementation by Paul B. Manis, April and Sept, 1999.
+ENDCOMMENT
+
+UNITS {
+	(mA) = (milliamp)
+	(mV) = (millivolt)
+	(nA) = (nanoamp)
+}
+
+? interface
+NEURON {
+THREADSAFE
+	SUFFIX jsrna
+	USEION na READ ena WRITE ina
+	RANGE gbar
+    RANGE gna, vsna
+	RANGE minf, hinf, mtau, htau
+}
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+PARAMETER {
+	v (mV)
+	dt (ms)
+    ena = 55 (mV)
+	gbar = 0.25 (mho/cm2)    <0,1e9>
+	vsna = 0 (mV)
+    q10 = 3.0 (1)
+}
+
+STATE {
+	m h 
+}
+
+ASSIGNED {
+	gna (mho/cm2) 
+    ina (mA/cm2)
+    minf hinf 
+    mtau (ms) htau (ms)
+    celsius (degC)
+}
+
+LOCAL mexp, hexp 
+
+? currents
+BREAKPOINT {
+	SOLVE states METHOD cnexp
+    gna = gbar*(m^3)*h
+    ina = gna*(v - ena)
+}
+
+UNITSOFF
+
+INITIAL {
+    trates(v)
+    m = minf
+    h = hinf
+}
+
+DERIVATIVE states {  :Computes state variables m, h, and n
+	trates(v)      :             at the current v and dt.
+	m' = (minf - m)/mtau
+    h' = (hinf - h)/htau
+}
+
+LOCAL qt
+
+PROCEDURE rates(v (mV)) {  :Computes rate and other constants at current v.
+                      :Call once from HOC to initialize inf at resting v.
+LOCAL  alpha, beta, sum
+
+	qt = q10^((celsius - 22)/10 (degC)) : R&M'03 used 3
+
+: Note qt temperature here cancels in minf (a/(a+b))
+:"m" sodium activation system - JSR
+        alpha = -0.36*qt*vtrap((v+49),-3)
+        beta =  0.4*qt*vtrap((v+58),20)
+        sum = alpha + beta
+		mtau = 1/sum
+        minf = alpha/sum
+
+:"h" sodium inactivation system - JSR
+        alpha = 2.4*qt/(1+exp((v+68-vsna)/3 (mV))) + 0.8*qt/(1+exp(v+61.3-vsna))
+        beta = 3.6*qt/(1+exp(-(v+21-vsna)/10 (mV)))
+        sum = alpha + beta
+		htau = 1/sum
+        hinf = alpha/sum
+
+
+
+
+: jsr modified sodium channel - defined in terms of alpha and beta this time
+:    am = (0.36*q10*(v+49))/(1-exp(-((v+49)/3)))
+:    am = -(0.36*q10*vtrap(-(v+49),3))
+:    bm = -(0.40*q10*(v+58))/(1-exp((v+58)/20))
+:    bm = (0.40*q10*vtrap((v+58),20))
+:    ah = ((2.4*q10)/(1+exp((v+68)/3))) + (0.8*qten/(1+exp(v+61.3)))
+:    bh = (3.6*q10)/(1+exp(-(v+21)/10))
+
+:    minf = am/(am+bm)
+:    hinf = ah/(ah+bh)
+    
+:	mtau = 1/(am+bm)
+:	htau = 1/(ah+bh)
+
+}
+
+PROCEDURE trates(v (mV)) {  :Computes rate and other constants at current v.
+                      :Call once from HOC to initialize inf at resting v.
+	LOCAL tinc
+
+    rates(v)    : not consistently executed from here if usetable_hh == 1
+        : so don't expect the tau values to be tracking along with
+        : the inf values in hoc
+
+	tinc = -dt : * q10 # handled in rates now
+	mexp = 1 - exp(tinc/mtau)
+	hexp = 1 - exp(tinc/htau)
+}
+
+FUNCTION vtrap(x,y) {  :Traps for 0 in denominator of rate eqns.
+	if (fabs(x/y) < 1e-6) {
+		vtrap = y*(1 - x/y/2)
+	}else{
+		vtrap = x/(exp(x/y) - 1)
+	}
+}
+
+

cnmodel/mechanisms/ka.mod

@@ -0,0 +1,104 @@
+TITLE klt.mod  The low threshold conductance of cochlear nucleus neurons
+
+COMMENT
+
+NEURON implementation of Jason Rothman's measurements of VCN conductances.
+
+This file implements the transient potassium current found in ventral cochlear
+nucleus "Type I" cells, which are largely "stellate" or "multipolar" cells  (Manis and
+Marx, 1991; Rothman and Manis, 2003a,b; Manis et al, 1996). The current is likely
+ mediated by Kv4.2 potassium channel subunits, but this has not been directly
+demonstrated. The specific implementation is described in Rothman and Manis, J.
+Neurophysiol. 2003, in the appendix. Measurements were made from isolated 
+neurons from adult guinea pig, under reasonably stringent voltage clamp conditions.
+ The measured current is sensitive to 4-aminopyridine. 
+Original implementation by Paul B. Manis, April (JHU) and Sept, (UNC)1999.
+
+File split implementaiton, April 1, 2004.
+
+Contact: pmanis@med.unc.edu
+
+ENDCOMMENT
+
+UNITS {
+        (mA) = (milliamp)
+        (mV) = (millivolt)
+        (nA) = (nanoamp)
+}
+
+NEURON {
+        THREADSAFE
+        SUFFIX ka
+        USEION k READ ek WRITE ik
+        RANGE gbar, gka, ik
+        GLOBAL ainf, binf, cinf, atau, btau, ctau
+}
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+PARAMETER {
+        v (mV)
+        dt (ms)
+        gbar = 0.00477 (mho/cm2) <0,1e9>
+        q10tau = 3.0
+        q10g = 2.0
+}
+
+STATE {
+        a b c
+}
+
+ASSIGNED {
+    celsius  (degC)  : model is defined on measurements made at room temp in Baltimore
+    ik (mA/cm2) 
+    ek (mV)
+    gka (mho/cm2)
+    ainf binf cinf
+    atau (ms) btau (ms) ctau (ms)
+    qg ()  : computed q10 for gnabar based on q10g
+    q10 ()
+}
+
+LOCAL aexp, bexp, cexp
+
+BREAKPOINT {
+    SOLVE states METHOD cnexp
+    
+    gka = gbar*(a^4)*b*c
+    ik = gka*(v - ek)
+
+}
+
+
+INITIAL {
+    qg = q10g^((celsius-22)/10 (degC))
+    q10 = q10tau^((celsius - 22)/10 (degC)) : if you don't like room temp, it can be changed!
+    rates(v)
+    a = ainf
+    b = binf
+    c = cinf
+}
+
+DERIVATIVE states {  :Computes state variables m, h, and n
+    rates(v)      :             at the current v and dt.
+    a' = (ainf - a)/atau
+    b' = (binf - b)/btau
+    c' = (cinf - c)/ctau
+}
+
+
+PROCEDURE rates(v (mV)) {  :Computes rate and other constants at current v.
+                      :Call once from HOC to initialize inf at resting v.
+
+    ainf = (1 / (1 + exp(-1*(v + 31) / 6 (mV))))^0.25
+    binf = 1 / (1 + exp((v + 66) / 7 (mV)))^0.5
+    cinf = 1 / (1 + exp((v + 66) / 7 (mV)))^0.5
+
+    atau =  (100 (ms)/ (7*exp((v+60) / 14 (mV)) + 29*exp(-(v+60) / 24 (mV)))) + 0.1
+    atau = atau/q10
+    btau =  (1000 (ms) / (14*exp((v+60) / 27 (mV)) + 29*exp(-(v+60) / 24 (mV)))) + 1
+    btau = btau/q10
+    ctau = (90 (ms)/ (1 + exp((-66-v) / 17 (mV)))) + 10
+    ctau = ctau/q10
+}
+

cnmodel/mechanisms/kcnq.mod

@@ -0,0 +1,72 @@
+TITLE KCNQ potassium channel for GPe neuron
+
+COMMENT
+ modeled by Gunay et al., 2008
+ implemented in NEURON by Kitano, 2011
+Threadsafe and unit checking, P.B. Manis, 2014
+
+ ENDCOMMENT
+
+UNITS {
+ (mV) = (millivolt)
+ (mA) = (milliamp)
+}
+
+NEURON {
+ THREADSAFE
+ SUFFIX kcnq
+ USEION k READ ek WRITE ik
+ RANGE gbar, gk, iKCNQ
+}
+
+PARAMETER {
+    v (mV)
+    dt (ms)
+    gbar  = 0.001 (mho/cm2)
+    iKCNQ  = 0.0 (mA/cm2)
+    ek (mV)
+
+    theta_m = -61.0 (mV)
+    k_m = 19.5 (mV)
+    tau_m0 = 6.7 (ms)
+    tau_m1 = 100.0 (ms)
+    phi_m = -61.0 (mV)
+    sigma_m0 = 35.0 (mV)
+    sigma_m1 = -25.0 (mV)
+}
+
+STATE {
+    m
+}
+
+ASSIGNED {
+    ik (mA/cm2)
+    gk (mho/cm2)
+    minf
+    taum (ms)
+}
+
+BREAKPOINT {
+    SOLVE states METHOD cnexp
+    gk  = gbar*m*m*m*m
+    ik = gk * (v-ek)
+    iKCNQ = ik
+}
+
+
+INITIAL {
+    settables(v)
+    m = minf
+}
+
+DERIVATIVE states {
+    settables(v)
+    m' = (minf - m)/taum
+}
+
+PROCEDURE settables(v (mV)) {
+    TABLE minf, taum FROM -100 TO 100 WITH 400
+
+	minf = 1.0 / (1.0 + exp((theta_m - v)/k_m))
+	taum = tau_m0 + (tau_m1 - tau_m0)/(exp((phi_m - v)/sigma_m0) + exp((phi_m - v)/sigma_m1))
+}

cnmodel/mechanisms/kdpyr.mod

@@ -0,0 +1,92 @@
+TITLE kdpyr.mod   DCN pyramidal cell model, delayed rectifier  
+    
+COMMENT
+
+This is part of a model implements a Dorsal Cochlear Nucleus Pyramidal point cell
+based on kinetic data from Kanold and Manis (1999) and Kanold's dissertation (1999)
+
+-- 15 Jan 1999 P. Manis
+
+Added export of start states for some variables to do perturbation tests
+These start values replace the "inf" values used in the initialization procedure
+Note that if the start variable is set to a value less than 0, 
+then the default initialization will be done. Typically I use a value of -1 for this flagging
+Note also that it is possible to set the initial values > 1 but this is meaningless in terms of
+the present equations. 
+-- 5 Feb 1999 P. Manis
+
+ENDCOMMENT
+
+UNITS {
+       (mA) = (milliamp)
+       (mV) = (millivolt)
+}
+
+NEURON {
+    THREADSAFE
+    SUFFIX kdpyr
+    USEION k READ ek WRITE ik
+    RANGE gbar, gk		: delayed rectifier
+    RANGE ntau: time constants delayed rectifier
+    RANGE kd_avh
+    
+}
+    
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+    
+PARAMETER {
+    v (mV)
+    celsius (degC)
+    dt (ms)
+    ek (mV) : = -81.5 (mV)
+    gbar = 0.006667 (mho/cm2)	<0,1e9>
+    ntau = 0.5 (ms) <0.1,100>
+    kd_avh = -40 (mV)
+    }
+    
+STATE {
+            n
+}
+    
+ASSIGNED {
+	gk (mho/cm2)
+	ik (mA/cm2)
+	ninf
+}
+
+LOCAL nexp
+    
+BREAKPOINT {
+	SOLVE states METHOD cnexp
+	gk = gbar*n*n
+	ik = gk*(v - ek)
+}
+    
+INITIAL {
+	rates(v)
+	n = ninf
+}
+
+DERIVATIVE states {  
+	rates(v)
+	n' = (ninf - n) / ntau
+}
+    
+LOCAL q10
+
+PROCEDURE rates(v(mV)) {  :Computes rate and other constants at current v.
+	                      :Call once from HOC to initialize inf at resting v.
+	LOCAL  alpha, beta, sum
+	TABLE ninf, ntau DEPEND celsius FROM -200 TO 100 WITH 400
+
+	q10 = 3^((celsius - 22)/10 (degC))
+	: "n" potassium activation system
+       ninf = kd_m(v)
+}
+    
+: Make these as functions so we can view them from hoc, although this 
+: may slow things down a bit
+FUNCTION kd_m(x (mV)) { : potassium activation
+	kd_m = 1/(1+exp(-(x-kd_avh)/(3 (mV))))		: flat time constants
+}
+

cnmodel/mechanisms/kht.mod

@@ -0,0 +1,111 @@
+TITLE kht.mod  The high threshold conductance of cochlear nucleus neurons
+
+COMMENT
+
+NEURON implementation of Jason Rothman's measurements of VCN conductances.
+
+This file implements the high threshold potassium current found in several brainstem
+ nuclei of the auditory system, including the spherical and globular bushy cells
+  (Manis and Marx, 1991; Rothman and Manis, 2003a,b) and multipolar (stellate) 
+  cells of the ventral cochlear nucleus, principal cells of the medial 
+  nucleus of the trapzoid body (Brew and Forsythe, 1995, Wang and Kaczmarek, 
+  1997) and neurons of the medial superior olive. The current is likely mediated by 
+  Kv3.1  potassium channel subunits. The specific 
+  implementation is described in Rothman and Manis, J. Neurophysiol. 2003, in the 
+  appendix. Measurements were made from isolated neurons from adult guinea pig, 
+  under reasonably stringent voltage clamp conditions. The measured current is 
+  sensitive to 4-aminopyridine and TEA, but is spared by mamba snake toxi
+  dendrotoxin I.
+
+
+Similar conductrances are found in the homologous neurons of the avian auditory 
+system (Reyes and Rubel; Zhang and Trussell; Rathouz and Trussell), and the 
+conductance described here, in the absence of more detailed kinetic measurements
+, is probably suitable for use in modeling that system.
+
+
+Original implementation by Paul B. Manis, April (JHU) and Sept, (UNC)1999.
+
+File split implementation, February 28, 2004.
+
+Contact: pmanis@med.unc.edu
+
+ENDCOMMENT
+
+UNITS {
+        (mA) = (milliamp)
+        (mV) = (millivolt)
+        (nA) = (nanoamp)
+}
+
+NEURON {
+        THREADSAFE
+        SUFFIX kht
+        USEION k READ ek WRITE ik
+        RANGE gbar, gkht, ik, q10g
+        GLOBAL ninf, pinf, ntau, ptau
+}
+
+:INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+ASSIGNED {
+    celsius (degC)  : model is defined on measurements made at room temp in Baltimore: 22 degC
+    ik (mA/cm2)
+    ek (mV)
+    gkht (mho/cm2)
+    pinf ninf
+    ptau (ms)
+    ntau (ms)
+    qg ()  : computed q10 for gnabar based on q10g
+    q10 ()
+}
+    
+PARAMETER {
+        v (mV)
+        dt (ms)
+        gbar = 0.01592 (mho/cm2) <0,1e9>
+        nf = 0.85 <0,1> :proportion of n vs p kinetics
+        q10tau = 3.0
+        q10g = 2.0
+}
+
+STATE {
+        n p
+}
+
+LOCAL nexp, pexp
+
+BREAKPOINT {
+    SOLVE states METHOD cnexp
+    
+    gkht = qg*gbar*(nf*(n^2) + (1-nf)*p)
+    ik = gkht*(v - ek)
+}
+
+INITIAL {
+    qg = q10g^((celsius-22)/10 (degC))
+    q10 = q10tau^((celsius - 22)/10 (degC))
+    rates(v)
+    p = pinf
+    n = ninf
+}
+
+DERIVATIVE states {  :Computes state variables m, h, and n
+    rates(v)      :             at the current v and dt.
+    n' = (ninf - n)/ntau
+    p' = (pinf - p)/ptau
+}
+
+PROCEDURE rates(v (mV)) {  :Computes rate and other constants at current v.
+                      :Call once from HOC to initialize inf at resting v.
+
+    ninf =   (1 + exp(-(v + 15) / 5 (mV)))^-0.5
+    pinf =  1 / (1 + exp(-(v + 23) / 6 (mV)))
+
+    ntau =  (100 (ms)/ (11*exp((v+60) / 24 (mV)) + 21*exp(-(v+60) / 23 (mV)))) + 0.7
+    ntau = ntau/q10
+    ptau = (100 (ms)/ (4*exp((v+60) / 32 (mV)) + 5*exp(-(v+60) / 22 (mV)))) + 5
+    ptau = ptau/q10
+    
+}
+
+

cnmodel/mechanisms/kif.mod

@@ -0,0 +1,128 @@
+TITLE kif.mod   DCN pyramidal cell model fast transient current
+    
+COMMENT
+    
+This model implements a fast transient potassium current from 
+Dorsal Cochlear Nucleus Pyramidal cells
+based on kinetic data from Kanold and Manis (1999) and Kanold's dissertation (1999)
+    -- 15 Jan 1999 P. Manis
+    2/10/02. P. Manis.
+            Pulled from pyr.mod 7/24/2014
+ENDCOMMENT
+       
+       
+UNITS {
+    (mA) = (milliamp)
+    (mV) = (millivolt)
+}
+    
+    NEURON {
+    SUFFIX kif
+    USEION k READ ek WRITE ik
+     
+    RANGE gkif, kif_a_inf, kif_i_inf	: fast inactivating potassium current
+    RANGE akif, gbar
+    RANGE kif_a_tau, kif_i_tau	
+    RANGE kif_a_start, kif_i_start
+    RANGE kif_ivh, kif_avh, kif_hivh
+}
+    
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+    
+PARAMETER {
+    v (mV)
+    celsius (degC)
+    dt (ms)
+    ek (mV) : = -81.5 (mV)
+    ena (mV) : = 50.0 (mV)
+    gbar = 0.0125 (mho/cm2) <0,1e9>
+    
+    kif_ivh = -89.6 (mV)
+    kif_avh = -57.0 (mV)
+    kif_hivh = -87.0 (mV)
+    kif_a_start = -1 (1)
+    kif_i_start = -1 (1)
+    }
+    
+STATE {
+           kifa kifi
+}
+    
+ASSIGNED {
+    gkif (mho/cm2)
+    ik (mA/cm2)
+    kif_a_inf (1)
+    kif_i_inf  (1)
+    kif_a_tau (ms)
+    kif_i_tau (ms)
+    akif ()
+    q10 ()
+}
+
+
+BREAKPOINT {
+    SOLVE states METHOD cnexp
+    akif = kifa*kifa*kifa*kifa*kifi
+    gkif = gbar*akif
+    ik = gkif*(v - ek)
+}
+
+INITIAL {
+    q10 = 3^((celsius - 22)/10 (degC))
+    rates(v)
+    if(kif_a_start < 0) {		: if xx_(i/a)_start is > 0, then perturbation is done at onset of computations.
+    	kifa = kif_a_inf
+    } else {
+    	kifa = kif_a_start
+    }
+    if(kif_i_start < 0) {
+    	kifi = kif_i_inf
+    } else {
+    	kifi = kif_i_start
+    }
+}
+    
+DERIVATIVE states {
+    rates(v)
+    kifa' = (kif_a_inf - kifa) / kif_a_tau
+    kifi' = (kif_i_inf - kifi) / kif_i_tau
+}
+    
+
+PROCEDURE rates(v(mV)) {  :Computes rate and other constants at current v.
+                          :Call once from HOC to initialize inf at resting v.
+:    LOCAL  alpha, beta, sum
+:    TABLE kif_a_inf, kif_a_tau, kif_i_inf, kif_i_tau DEPEND celsius, kif_avh, kif_ivh FROM -200 TO 100 WITH 400
+    : "kif" fast inactivation potassium channel - activation and inactivation
+    kif_a_inf = kif_m(v)
+    kif_i_inf = kif_h(v)
+    kif_a_tau = kif_mt(v)
+    kif_i_tau = kif_ht(v)
+    }
+    
+: Make these as functions so we can view them from hoc, although this 
+: may slow things down a bit
+    
+FUNCTION kif_m(v ) { : ikif activation
+    kif_m = 1.0/(1+exp(-(v-kif_avh)/25.8 (mV)))
+}	
+
+FUNCTION kif_h(v (mV)) { : ikif inactivation
+    kif_h = 1.0/(1+exp((v-kif_ivh)/6.7 (mV)))
+}
+
+FUNCTION kif_mt(v (mV)) (ms) { : ikif activation tau
+    LOCAL x
+    x = 0.15 * exp((v-kif_avh)/10 (mV)) + 0.3 *exp(-(v-kif_avh)/10 (mV))
+    x = 0.5 + (1.0 /x)
+    kif_mt = (x  * 1.0 (ms))/q10
+}
+
+FUNCTION kif_ht(v (mV)) (ms) { : ikif inactivation tau
+    LOCAL x
+    x = 0.015 * exp((v-kif_hivh)/20 (mV))+0.03*exp(-(v-kif_hivh)/20 (mV))
+    x = 10 + (1./x)
+    kif_ht = (x * 1.0 (ms)) /q10
+}
+
+

cnmodel/mechanisms/kir.mod

@@ -0,0 +1,108 @@
+TITLE KIR channel
+COMMENT
+Reference: Steephen JE, Manchanda R (2009) Differences in biophysical
+properties of nucleus accumbens medium spiny neurons emerging from
+inactivation of inward rectifying potassium currents. J Comput Neurosci [PubMed] 
+
+Found on ModelDB, 1/21/2013 PBManis
+
+
+ENDCOMMENT
+
+NEURON {
+	SUFFIX KIR
+	USEION  k READ ek WRITE ik
+	RANGE g, ik, gbar
+	GLOBAL minf, mtau
+}
+
+UNITS {
+	(mA) = (milliamp)
+	(uA) = (microamp)
+	(mV) = (millivolt)
+	(mS) = (millimho)
+}
+
+PARAMETER {
+	celsius (degC)
+	ek			(mV)
+	gbar = 1.4e-4	(mho/cm2)	<0,1e9>
+	m_vh = -82	(mV)	: half activation
+	m_ve = 13		(mV)	: slope
+}
+
+ASSIGNED {
+	v	(mV)
+	g	(mho/cm2)
+	ik	(mA/cm2)
+	minf	(1)
+	mtau	(ms)
+	qt (1)
+}
+
+STATE {
+	m
+}
+
+BREAKPOINT {
+	SOLVE states METHOD cnexp
+	g = gbar*m
+	ik = g*(v - ek)
+}
+
+INITIAL {
+	qt = 3^((celsius-35)/10)
+	rates(v)
+	m = minf
+}
+
+DERIVATIVE states { 
+	rates(v)
+	m' = (minf-m)/mtau
+}
+
+FUNCTION_TABLE tabmtau(v(mV)) (ms)
+
+: rates() computes rate and other constants at present v
+: call once from hoc to initialize inf at resting v
+
+PROCEDURE rates(v(mV)) {
+:	mtau = tabmtau(v)
+	mtau = tabmtau(v)/qt
+	minf = 1/(1 + exp((v - m_vh)/m_ve))
+}
+ 
+COMMENT
+/* TABLES
+The tables here are built as vecs in hoc, and then loaded into the mod file arrays before use...
+
+ * 
+ * Steephen, J. E., & Manchanda, R. (2009). Differences in biophysical properties of nucleus accumbens medium spiny neurons emerging from inactivation of inward rectifying potassium currents. J Comput Neurosci, 
+ * doi:10.1007/s10827-009-0161-7
+ */
+ 
+//KIR
+objref vecmtau_KIR, vecv_KIR
+vecmtau_KIR = new Vector()
+vecv_KIR = new Vector()
+vecv_KIR.indgen(-120, 0, 10)
+vecmtau_KIR.append(7.465, 7.465, 7.465, 8, 9.435, 10.755, 12.12, 13.795, 15.385, 14.285, 11.765, 8.89, 8) // At 35 deg C
+table_tabmtau_KIR(&vecmtau_KIR.x[0], vecv_KIR.size, &vecv_KIR.x[0]) 
+
+//inKIR
+objref vecv_inKIR, vechinf_inKIR, vechtau_inKIR, vecv_tau_inKIR
+vecv_tau_inKIR = new Vector()
+vechtau_inKIR= new Vector()
+vecv_tau_inKIR.append(-120,-90, -50)
+vechtau_inKIR.append(7.767, 15, 25.333) // At 35 deg C
+table_tabhtau_inKIR(&vechtau_inKIR.x[0],vecv_tau_inKIR.size, &vecv_tau_inKIR.x[0])
+
+vecv_inKIR = new Vector()
+vechinf_inKIR = new Vector()
+vecv_inKIR.append(-120,-90, -50)
+vechinf_inKIR.append(0, 0.13, 1)
+table_tabhinf_inKIR(&vechinf_inKIR.x[0], vecv_inKIR.size, &vecv_inKIR.x[0])
+table_tabmtau_inKIR(&vecmtau_KIR.x[0], vecv_KIR.size, &vecv_KIR.x[0]) 
+
+ENDCOMMENT
+

cnmodel/mechanisms/kis.mod

@@ -0,0 +1,123 @@
+TITLE kis.mod   DCN pyramidal cell model Slow transient K current
+
+COMMENT
+
+
+This model implements the slow transient potassium current from
+Dorsal Cochlear Nucleus Pyramidal cells
+based on kinetic data from Kanold and Manis (1999) and Kanold's dissertation (1999)
+
+-- 15 Jan 1999 P. Manis
+
+2/10/02, 7/24/2014. P. Manis.
+
+ENDCOMMENT
+
+
+UNITS {
+    (mA) = (milliamp)
+    (mV) = (millivolt)
+}
+
+
+NEURON {
+    THREADSAFE
+    SUFFIX kis
+    USEION k READ ek WRITE ik
+    RANGE gkis, kis_a_inf, kis_i_inf	: fast inactivating potassium current
+    RANGE akis, gbar
+    RANGE kis_a_tau, kis_i_tau	
+    RANGE kis_a_start, kis_i_start
+}
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+PARAMETER {
+    v (mV)
+    celsius (degC)
+    dt (ms)
+    ek (mV) : = -81.5 (mV)
+    ena (mV) : = 50.0 (mV)
+    gbar = 0.0033333  (mho/cm2) <0,1e9>
+
+    kis_ivh = -40.9 (mV)
+    kis_avh = -38.4 (mV)
+    kis_a_start = -1
+    kis_i_start = -1
+}
+
+STATE {
+    kisa kisi
+}
+
+ASSIGNED {
+	gkis (mho/cm2)
+	ik (mA/cm2)
+	kis_a_inf kis_i_inf
+	kis_a_tau (ms)
+    kis_i_tau (ms)
+	akis
+    q10 ()
+}
+
+BREAKPOINT {
+	SOLVE states METHOD cnexp
+	akis = kisa*kisa*kisa*kisa*kisi
+	gkis = gbar*akis
+	ik = gkis*(v - ek)
+}
+
+INITIAL {
+	q10 = 3^((celsius - 22)/10 (degC))
+	rates(v)
+	if(kis_a_start < 0) {		: if xx_(i/a)_start is > 0, then perturbation is done at onset of computations.
+		kisa = kis_a_inf
+	} else {
+		kisa = kis_a_start
+	}
+	if(kis_i_start < 0) {
+		kisi = kis_i_inf
+	} else {
+		kisi = kis_i_start
+	}
+}
+
+
+DERIVATIVE states {
+	rates(v)
+	kisa' = (kis_a_inf - kisa) / kis_a_tau
+	kisi' = (kis_i_inf - kisi) / kis_i_tau
+}
+
+PROCEDURE rates(v (mV)) {  :Computes rate and other constants at current v.
+	                      :Call once from HOC to initialize inf at resting v.
+
+	: "kis" fast inactivation potassium channel - activation and inactivation
+	kis_a_inf = kis_m(v)
+	kis_i_inf = kis_h(v)
+	kis_a_tau = kis_mt(v)
+	kis_i_tau = kis_ht(v)
+}
+
+: Make these as functions so we can view them from hoc, although this
+: may slow things down a bit
+
+FUNCTION kis_m(v (mV)) { : ikis activation
+	kis_m = 1/(1+exp(-(v-kis_avh)/23.7 (mV)))
+}
+
+FUNCTION kis_h(v (mV)) { : ikis inactivation
+	kis_h = 1/(1+exp((v-kis_ivh)/9 (mV)))
+}
+
+FUNCTION kis_mt(v (mV)) (ms) { : ikis activation tau
+	LOCAL x
+    x = 0.15*exp((v-kis_avh)/10 (mV)) + 0.3*exp(-(v-kis_avh)/10 (mV))
+    x = 0.5 + (1.0 /x)
+    kis_mt = (x  * 1.0 (ms))/q10
+}
+
+FUNCTION kis_ht(v (mV)) (ms) { : ikis inactivation tau
+	kis_ht = 200 (ms)
+}
+

cnmodel/mechanisms/klt.mod

@@ -0,0 +1,109 @@
+TITLE klt.mod  The low threshold conductance of cochlear nucleus neurons
+
+COMMENT
+
+NEURON implementation of Jason Rothman's measurements of VCN conductances.
+
+This file implements the low threshold potassium current found in several brainstem
+ nuclei of the auditory system, including the spherical and globular bushy cells
+  (Manis and Marx, 1991; Rothman and Manis, 2003a,b) and octopus cells (Bal and
+  Oertel, 2000) of the ventral cochlear nucleus, principal cells of the medial 
+  nucleus of the trapzoid body (Brew and Forsythe, 1995, Wang and Kaczmarek, 
+  1997) and neurons of the medial superior olive. The current is likely mediated by 
+  heteromultimers of Kv1.1 and Kv1.2 potassium channel subunits. The specific 
+  implementation is described in Rothman and Manis, J. Neurophysiol. 2003, in the 
+  appendix. Measurements were made from isolated neurons from adult guinea pig, 
+  under reasonably stringent voltage clamp conditions. The measured current is 
+  sensitive to the mamba snake toxin dendrotoxin-I.
+
+
+Similar conductrances are found in the homologous neurons of the avian auditory 
+system (Reyes and Rubel; Zhang and Trussell; Rathouz and Trussell), and the 
+conductance described here, in the absence of more detailed kinetic measurements
+, is probably suitable for use in modeling that system.
+
+
+Original implementation by Paul B. Manis, April (JHU) and Sept, (UNC)1999.
+
+File split implementation, February 28, 2004.
+
+Contact: pmanis@med.unc.edu
+
+ENDCOMMENT
+
+UNITS {
+    (mA) = (milliamp)
+    (mV) = (millivolt)
+    (nA) = (nanoamp)
+}
+
+NEURON {
+    THREADSAFE
+    SUFFIX klt
+    USEION k READ ek WRITE ik
+    RANGE gbar, gklt, ik, q10g
+    GLOBAL winf, zinf, wtau, ztau
+}
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+PARAMETER {
+    v (mV)
+    dt (ms)
+    gbar = 0.01592 (mho/cm2) <0,1e9>
+    zss = 0.5   <0,1>   : steady state inactivation of glt
+    q10tau = 3.0
+    q10g = 2.0
+}
+
+STATE {
+    w z
+}
+
+ASSIGNED {
+    celsius (degC)  : model is defined on measurements made at room temp in Baltimore
+    ik (mA/cm2) 
+    ek (mV)
+    gklt (mho/cm2)
+    winf zinf
+    wtau (ms) ztau (ms)
+    qg ()  : computed q10 for gnabar based on q10g
+    q10 ()
+}
+
+LOCAL wexp, zexp
+
+BREAKPOINT {
+    SOLVE states METHOD cnexp
+    
+    gklt = qg*gbar*(w^4)*z
+    ik = gklt*(v - ek)
+}
+
+INITIAL {
+    qg = q10g^((celsius-22)/10 (degC))
+    q10 = q10tau^((celsius - 22)/10 (degC)) : if you don't like room temp, it can be changed!
+    rates(v)
+    w = winf
+    z = zinf
+}
+
+DERIVATIVE states {  :Computes state variables m, h, and n
+    rates(v)      :         at the current v and dt.
+    w' = (winf - w)/wtau
+    z' = (zinf - z)/ztau
+}
+
+
+PROCEDURE rates(v (mV)) {  :Computes rate and other constants at current v.
+              :Call once from HOC to initialize inf at resting v.
+
+    winf = (1 / (1 + exp(-(v + 48) / 6 (mV))))^0.25
+    zinf = zss + ((1-zss) / (1 + exp((v + 71) / 10 (mV))))
+
+    wtau =  (100 (ms)/ (6*exp((v+60) / 6 (mV)) + 16*exp(-(v+60) / 45 (mV)))) + 1.5
+    wtau = wtau/q10
+    ztau =  (1000 (ms)/ (exp((v+60) / 20 (mV)) + exp(-(v+60) / 8 (mV)))) + 50
+    ztau = ztau/q10
+}
+

cnmodel/mechanisms/kpkj.mod

@@ -0,0 +1,94 @@
+: HH TEA-sensitive Purkinje potassium current
+: Created 8/5/02 - nwg
+
+NEURON {
+	THREADSAFE
+    SUFFIX kpkj
+	USEION k READ ek WRITE ik
+	RANGE gbar, ik, gk
+	GLOBAL minf, hinf, mtau, htau
+}
+
+UNITS {
+	(mV) = (millivolt)
+	(mA) = (milliamp)
+}
+
+PARAMETER {
+	v (mV)
+
+	gbar = .004	(mho/cm2)
+
+	mivh = -24	(mV)
+	mik = 15.4	(mV)
+	mty0 = .00012851 (s)	
+	mtvh1 = 100.7	(mV)
+	mtk1 = 12.9	(mV)
+	mtvh2 = -56.0	(mV)
+	mtk2 = -23.1	(mV)
+	
+	hiy0 = .31	
+	hiA = .78
+	hivh = -5.802	(mV)
+	hik = 11.2	(mV)
+
+	ek (mV)
+}
+
+ASSIGNED {
+	gk    (mho/cm2)
+    ik		(mA/cm2)
+	minf
+	mtau		(ms)
+	hinf
+	htau		(ms)
+}
+
+STATE {
+	m
+	h
+}
+
+INITIAL {
+	rates(v)
+	m = minf
+	h = hinf
+}
+
+BREAKPOINT {
+	SOLVE states METHOD cnexp
+	gk = gbar * m^3 * h
+    ik =  gk * (v - ek)
+}
+
+DERIVATIVE states {
+	rates(v)
+	m' = (minf - m) / mtau
+	h' = (hinf - h) / htau
+}
+
+PROCEDURE rates( Vm (mV)) {
+	LOCAL v
+	v = Vm + 11 (mV)	: Account for Junction Potential
+	minf = 1/(1+exp(-(v-mivh)/mik))
+	mtau =  mtau_func(v)
+	hinf = hiy0 + hiA/(1+exp((v-hivh)/hik))
+	htau = 1000 * htau_func(v)
+}
+
+FUNCTION mtau_func (v (mV)) (ms) {
+	if (v < -35 (mV)) {
+		mtau_func = (1000)*(3.4225e-5+.00498*exp(-v/-28.29 (mV)))*3 (s)
+	} else {
+		mtau_func = (1000)*(mty0 + 1(s)/(exp((v+mtvh1)/mtk1)+exp((v+mtvh2)/mtk2)))
+	}
+}
+
+FUNCTION htau_func(Vm (mV)) (ms) {
+	if ( Vm > 0) {
+		htau_func = (1000)*(0.0012 (s) + 0.0023(s)*exp(-0.141 *Vm / 1 (mV)))
+	} else {
+		htau_func = (1000)*(1.2202e-05(s) + .012(s) * exp(-((Vm-(-56.3 (mV)))/49.6 (mV))^2))
+	}
+}
+	

cnmodel/mechanisms/kpkj2.mod

@@ -0,0 +1,67 @@
+: HH Low TEA-sensitive Purkinje potassium current
+: Created 8/7/02 - nwg
+
+NEURON {
+	THREADSAFE
+    SUFFIX kpkj2
+	USEION k READ ek WRITE ik
+	RANGE gbar, ik, gk
+	GLOBAL ninf, ntau
+}
+
+UNITS {
+	(mV) = (millivolt)
+	(mA) = (milliamp)
+}
+
+PARAMETER {
+	v		(mV)
+	gbar = .002	(mho/cm2)
+	
+	nivh = -24	(mV)
+	nik = 20.4 (mV)
+	
+	ek (mV)
+}
+
+ASSIGNED {
+	gk (mho/cm2)
+    ik (mA/cm2)
+	ninf (1)
+	ntau		(ms)
+}
+
+STATE {
+	n
+}
+
+INITIAL {
+	rates(v)
+	n = ninf
+}
+
+BREAKPOINT {
+	SOLVE states METHOD cnexp
+	gk = gbar * n^4
+    ik = gk * (v - ek)
+}
+
+DERIVATIVE states {
+	rates(v)
+	n' = (ninf - n) / ntau
+}
+
+PROCEDURE rates(Vm (mV)) {
+	LOCAL v
+	v = Vm + 11	: Account for Junction Potential
+	ninf = 1/(1+exp(-(v-nivh)/nik))
+	ntau = 1000 * ntau_func(v)
+}
+
+FUNCTION ntau_func(v (mV)) (ms) {
+	if (v < -20) {
+		ntau_func = 0.000688 (ms) + 1 (ms)/(exp((v+64.2 (mV))/6.5 (mV))+exp((v-141.5 (mV))/-34.8 (mV)))
+	} else {
+		ntau_func = 0.00016 (ms) + 0.0008 (ms) *exp(-0.0267 * v /(1 (mV)))
+	}
+}

cnmodel/mechanisms/kpkjslow.mod

@@ -0,0 +1,63 @@
+: HH Slow TEA-insensitive Purkinje potassium current
+: Created 8/7/02 - nwg
+
+NEURON {
+	THREADSAFE
+    SUFFIX kpkjslow
+	USEION k READ ek WRITE ik
+	RANGE gbar, ik, gk
+	GLOBAL ninf, ntau
+}
+
+UNITS {
+	(mV) = (millivolt)
+	(mA) = (milliamp)
+}
+
+PARAMETER {
+	v		(mV)
+	gbar = 0.004	(mho/cm2)
+	
+	nivh = -16.5	(mV)
+	nik = 18.4 (mV)
+	
+	ek (mV)
+}
+
+ASSIGNED {
+    gk (mho/cm2)
+	ik (mA/cm2)
+	ninf
+	ntau		(ms)
+}
+
+STATE {
+	n
+}
+
+INITIAL {
+	rates(v)
+	n = ninf
+}
+
+BREAKPOINT {
+	SOLVE states METHOD cnexp
+	gk = gbar * n^4
+    ik = gk * (v - ek)
+}
+
+DERIVATIVE states {
+	rates(v)
+	n' = (ninf - n) / ntau
+}
+
+PROCEDURE rates(Vm (mV)) {
+	LOCAL v
+	v = Vm + 11	: Account for Junction Potential
+	ninf = 1/(1+exp(-(v-nivh)/nik))
+	ntau = 1000 * ntau_func(v)
+}
+
+FUNCTION ntau_func(v (mV)) (ms){
+	ntau_func = 0.000796 (ms) + 1 (ms)/(exp((v+73.2 (mV))/11.7 (mV))+exp((v-306.7 (mV))/-74.2(mV)))
+}

cnmodel/mechanisms/kpksk.mod

@@ -0,0 +1,111 @@
+TITLE Slow Ca-dependent potassium current
+:
+:   Ca++ dependent K+ current IC responsible for slow AHP
+:   Differential equations
+:
+:   Model based on a first order kinetic scheme
+:
+:      <closed> + n cai <-> <open>	(alpha,beta)
+:
+:   Following this model, the activation fct will be half-activated at 
+:   a concentration of Cai = (beta/alpha)^(1/n) = cac (parameter)
+:
+:   The mod file is here written for the case n=2 (2 binding sites)
+:   ---------------------------------------------
+:
+:   This current models the "slow" IK[Ca] (IAHP): 
+:      - potassium current
+:      - activated by intracellular calcium
+:      - NOT voltage dependent
+:
+:   A minimal value for the time constant has been added
+:
+:   Ref: Destexhe et al., J. Neurophysiology 72: 803-818, 1994.
+:
+:   Modifications by Arthur Houweling for use in MyFirstNEURON
+
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+NEURON {
+	THREADSAFE
+    SUFFIX kpksk
+	USEION k READ ek WRITE ik
+	USEION ca READ cai
+    RANGE  m_inf, tau_m, gbar, gk
+	GLOBAL beta, cac
+	RANGE ik
+}
+
+
+UNITS {
+	(mA) = (milliamp)
+	(mV) = (millivolt)
+	(molar) = (1/liter)
+	(mM) = (millimolar)
+}
+
+
+PARAMETER {
+	v		(mV)
+	celsius		(degC)
+    dt      (ms)
+	ek		(mV)
+	cai		(mM)	
+	gbar	= .01	(mho/cm2)
+	beta	= 0.002	(1/ms)		: backward rate constant
+	cac	= 0.010	(mM)		: middle point of activation fct
+	taumin	= 0.1	(ms)		: minimal value of the time cst
+}
+
+
+STATE {
+	m
+}
+
+ASSIGNED {
+	ik	(mA/cm2)
+    gk  (mho/cm2)
+	m_inf
+	tau_m	(ms)
+	tadj ()
+}
+
+BREAKPOINT { 
+	SOLVE states :METHOD euler
+	gk = gbar * m*m
+    ik =  gk * (v - ek)
+}
+
+:DERIVATIVE states {
+:       evaluate_fct(v,cai)
+:
+:       m'= (m_inf-m) / tau_m 
+:}
+  
+PROCEDURE states() {
+        evaluate_fct(v,cai)
+
+        m= m + (1-exp(-dt/tau_m))*(m_inf-m)
+}
+
+INITIAL {
+:
+:  activation kinetics are assumed to be at 22 deg. C
+:  Q10 is assumed to be 3
+:
+	tadj = 3 ^ ((celsius-22.0)/10 (degC))
+
+	evaluate_fct(v,cai)
+	m = m_inf
+}
+
+PROCEDURE evaluate_fct(v(mV),cai(mM)) {  LOCAL car
+
+	car = (cai/cac)^2
+
+	m_inf = car / ( 1 + car )
+	tau_m = 1 / beta / (1 + car) / tadj
+
+    if(tau_m < taumin) { tau_m = taumin } 	: min value of time cst
+}

cnmodel/mechanisms/leak.mod

@@ -0,0 +1,32 @@
+TITLE passive  (leak) membrane channel
+
+UNITS {
+	(mV) = (millivolt)
+	(mA) = (milliamp)
+}
+
+NEURON {
+	THREADSAFE
+	SUFFIX leak
+	NONSPECIFIC_CURRENT i
+	RANGE gbar, erev, i
+}
+
+PARAMETER {
+	v (mV)
+	gbar = 0.001	(mho/cm2)
+	erev = -65	(mV)
+}
+
+ASSIGNED { 
+    i	(mA/cm2)
+}
+
+INITIAL {
+    
+}
+BREAKPOINT {
+	i = gbar*(v - erev)
+}
+
+

cnmodel/mechanisms/multisite.mod

@@ -0,0 +1,379 @@
+TITLE Multisite synapse
+
+COMMENT
+-----------------------------------------------------------------------------
+Multi-site synapse with independent release sites. Each site operates independently
+and releases a vesicle upon presynaptic depolarization with a probability 
+determined by the history of activity, using the Dittman and Regehr (1998, 2000)
+model.
+
+Revised from coh2.mod, coh3.mod, and coh4.mod.
+The Dittman and Regeher (1998, 2000) release model with
+facilitation closely fits  Auditory Nerve data from mouse over
+a wide range of frequencies.
+The model DOES NOT include the postsynaptic receptors or desensitization, since
+these should be treated separately (couple XMTR to an AMPA receptor model,
+such as the Trussell-Raman model)
+
+Range variables:
+nZones: is the number of active zones simulated in this calyx model. Each zone
+        can be connected to a separate PSD.
+F (0.4): The base release probability
+k0 (1/1.75): /s, baseline recovery rate from depletion (slow rate)
+kmax (1/0.025): /s, maximal recovery rate from depletion (fast rate)
+td (0.05) : time constant for fast calcium-dependent recovery, sec
+kd (0.7) : affinity of fast recovery process for calcium sensor
+kf (0.5) : affinity of facilitation process
+tf (0.01) : rate of facilitation process (slow) seconds
+dD (0.02): calcium that drives recovery (ca influx per AP)
+dF (0.02): calcium that drives facilitation
+
+Added latency and variable delay (latstd, latency standard deviation in msec)
+around the mean spike time. 4/5/2011 pbm.
+
+Version 4 uses a log-normal distribution to determine release latencies. 
+The calculation is built-in instead of being passed through an array. 
+The lognormal distribution describes the individual vesicle release time
+course at this synapse as measured by Isaacson and Walmsley, 1996. Note that
+they used a gamma distribution in some plots, but the lognormal distribution 
+seems to fit their published data at least as well. 
+The parameters of the distribution, as well as the release latency,
+are controlled by an exponential function whose parameters are initialized at
+run time. 
+10/19/2011 Paul B. Manis, UNC Chapel Hill
+
+ENDCOMMENT
+
+DEFINE MAX_ZONES 1000  : maximum number of zones in this model
+DEFINE EVENT_N 10000   : number of entries in the Event Distribution (e.g., as sampled)
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+NEURON {
+    THREADSAFE
+    POINT_PROCESS MultiSiteSynapse
+    RANGE F, k0, kmax, taud, kd, tauf, kf
+    RANGE nZones, multisite, rseed, latency, latstd, debug
+    RANGE dD, dF, XMTR, glu, CaDi, CaFi
+    RANGE Fn, Dn
+    RANGE TTotal
+    RANGE nRequests, nReleases
+    RANGE Identifier : just a number so we can report which instance is active
+    RANGE tau_g, amp_g
+    : Distributions for stochastic release and testing (Sept, Oct, 2011):
+    RANGE EventLatencies, EventTime : returns the first EVENT_N latencies and absolute times at which they were used
+    RANGE ev_index : count in the EventLatencies (in case we are "short")
+    : parameters for latency shift during repetitive stimulation (Oct 19, 2011)
+    RANGE Dep_Flag : Depression flag (0 to remove depression; 1 to allow DKR control of facilitation and depression)
+    RANGE Lat_Flag, Lat_t0, Lat_A0, Lat_tau : Lat_Flag  = 0 means fixed latency (set by "latency" above)
+                                            : otherwise, latency = latency for t < Lat_t0
+                                            :            latency = latency + Lat_A0*(1-exp(-(t-Lat_t0)/Lat_tau))
+    : parameters for lognorm distribution shift during repetitive stimulation (Oct 19, 2011)
+    RANGE LN_Flag, LN_t0, LN_A0, LN_tau : LN_Flag  = 0 means fixed sigma as well
+                                            : otherwise, sigma = latstd for t < LN_t0
+                                            :            sigma = latstd + LN_A0*(1-exp(-(t-LN_t0)/LN_tau))
+
+    : externally assigned pointers to RNG functions
+    POINTER uniform_rng  : for deciding the number of active synapses when multisite==0
+}
+
+UNITS {
+    (mA) = (milliamp)
+    (mV) = (millivolt)
+    (mM) = (milli/liter)
+    (uM) = (micro/liter)
+}
+
+PARAMETER {
+    dt      (ms)
+    amp_g = 1.0 (mM)          : amplitude of transmitter pulse
+    tau_g = 0.5    (ms)    : duration of transmitter pulse
+    dD = 0.02 (1)       : calcium influx driving recovery per AP
+    dF = 0.02 (1)       : calcium influx driving facilitation per AP
+    F  = 0.5 (1)        : basal facilitation
+    k0 = 0.0005714(/ms)       : slow recovery from depletion (1.0/1.75)
+    kmax = 0.040 (/ms)      : fast recovery from depletion (1/0.025)
+    taud = 50.0 (ms)        : time constant for fast calcium dependent recovery
+    kd = 0.7 (1)       : affinity of fast recovery process for calcium sensor
+    tauf = 10.0 (ms)        : rate of slow facilitation process
+    kf = 0.5 (1)       : affinity of slow facilitation process
+    : taus = 1 (ms)    : defined by DKR but not used here 
+    : ks = 0.5 (1)     
+    : glu = 1 (mM)
+    rseed (1)        : random number generator seed (for SCOP module)
+    latency = 0.0 (ms)
+    latstd = 0.0 (ms)
+    
+    : Time course of latency shift in release during repetitive stimulation
+    Lat_Flag = 0 (1) : 0 means fixed latency, 1 means lognormal distribution
+    Lat_t0 = 0.0 (ms) : minimum time since simulation start before changes in latency are calculated
+    Lat_A0 = 0.0 (ms) : size of latency shift from t0 to infinity
+    Lat_tau = 100.0 (ms) : rate of change of latency shift (from fit of a+b(1-exp(-t/tau)))
+    : Statistical control of log-normal release shape over time during repetitive stimulation
+    LN_Flag = 0 (1) : 0 means fixed values for all time
+    LN_t0 = 0.0 (ms) : : minimum time since simulation start before changes in distribution are calculated
+    LN_A0 = 0.0 (ms) : size of change in sigma from t0 to infinity
+    LN_tau = 100.0 (ms) : rate of change of sigma over time (from fit of a+b*(1-exp(-t/tau)))
+    
+    : control flags     - if debug is 1, show all, if 2, just "some"
+    debug = 0
+    Identifier = 0
+    Dep_Flag = 1 (1) : 1 means use depression calculations; 0 means always set release probability to F
+}
+
+ASSIGNED {
+    : Externally set assignments
+    nZones (1)    : number of zones in the model
+    multisite (1)  : whether zones are modeled individually (1) or as a single, variable-amplitude zone (0)
+    nRequests (1) 
+    nReleases (1)
+    EventLatencies[EVENT_N] (0)
+    EventTime[EVENT_N] (0)
+    tRelease[MAX_ZONES] (ms)    : time of last release
+
+    : Internal calculated variables
+    Fn (1)
+    Dn (1)
+    CaDn (1)
+    CaFn (1)
+    CaDi (1)
+    CaFi (1)
+    eta (1)
+    tSpike (ms)            : time of last spike
+    tstep(ms)
+    TTotal(0)
+    tspike (ms)
+    latzone (ms)
+    vesicleLatency (ms)
+    sigma (ms)
+    gindex (0)
+    ev_index (0)
+    scrand (0)    
+    
+    uniform_rng
+}
+
+: Function prototypes needed to assign RNG function pointers
+VERBATIM
+double nrn_random_pick(void* r);
+void* nrn_random_arg(int argpos);
+ENDVERBATIM
+
+: Return a pick from uniform distribution.
+: (distribution parameters are set externally)
+FUNCTION rand_uniform() {
+VERBATIM
+    _lrand_uniform = nrn_random_pick(_p_uniform_rng);
+ENDVERBATIM
+}
+
+: Function to allow RNG to be externally set
+PROCEDURE setUniformRNG() {
+VERBATIM
+ {
+    void** pv = (void**)(&_p_uniform_rng);
+    *pv = nrn_random_arg(1);
+ }
+ENDVERBATIM
+}
+
+
+STATE {
+    XMTR[MAX_ZONES]       (mM)       : per-zone neurotransmitter concentration
+    N_ACTIVE[MAX_ZONES]       (1)    : number of zones actively releasing
+}
+
+INITIAL {
+:    VERBATIM
+:        fprintf(stdout, "MultiSiteSynapse: Calyx #%d Initialized with Random Seed: %d\n", (int)Identifier, (int)rseed);
+:    ENDVERBATIM
+
+    TTotal = 0
+    nRequests = 0
+    nReleases = 0
+    set_seed(rseed)
+    tSpike = -1e9
+    latzone = 0.0
+    sigma = 0.0
+    vesicleLatency = 0.0
+    gindex = 0
+    ev_index = 0
+    scrand = 0.0
+    CaDi = 1.0
+    CaFi = 0.0
+    CaDn = 1.0
+    CaFn = 0.0
+    Fn = F
+    Dn = 1.0
+    FROM i = 0 TO (nZones-1) {
+        XMTR[i] = 0
+        N_ACTIVE[i] = 1
+        tRelease[i] = tSpike
+    }
+    update_dkr(t-tSpike)
+}
+
+BREAKPOINT {
+    SOLVE release
+}
+
+LOCAL tz, n_relzones, amp
+PROCEDURE release() {
+    : Once released, the transmitter packet has a defined smooth time course in the "cleft"
+    : represented by the product of rising and falling exponentials.
+    : update glutamate in cleft
+    if (multisite == 1) {
+        : Update glutamate waveform for each active release zone
+        n_relzones = nZones
+    }
+    else {
+        : Update aggregate glutamate waveform for only the first release zone
+        n_relzones = 1
+    }
+    
+    FROM i = 0 TO (nZones-1) { : for each zone in the synapse
+        if (t >= tRelease[i] && t < tRelease[i] + 5.0 * tau_g) {
+            tz = t - tRelease[i] : time since onset of release
+            : calculate glutamate waveform (Xie & Manis 2013 Supplementary Eq. 1)
+            XMTR[i] = amp_g * (1.0-exp(-tz/(tau_g/3.0))) * exp(-(tz-(tau_g/3.0))/tau_g) 
+        }
+        else {
+            XMTR[i] = 0
+        }
+    }
+}
+
+
+PROCEDURE update_dkr(tstep (ms)) {
+    : update the facilitation and depletion variables
+    : from the Dittman-Regehr model. 
+    : Updates are done with each new presynaptic AP event.
+    if(tstep > 0.0) {
+        CaDi = CaDi + dD
+        CaFi = CaFi + dF
+        CaDn = CaDi * exp (-tstep/taud)
+        CaFn = CaFi * exp (-tstep/tauf)
+        eta = (kd/CaDi + 1.0)/(kd/CaDi + exp(-tstep/taud))
+        eta = eta^(-(kmax-k0)*taud)
+        Dn = 1.0-(1.0-(1.0-Fn)*Dn)*exp(-k0*tstep)*eta
+        Fn = F + (1.0-F)/(1.0+kf/CaFn)
+        CaDi = CaDn
+        CaFi = CaFn
+    }
+    if (Dep_Flag == 0) { : no depression
+        Dn = 1.0  : set to 1
+        Fn = F   : set to initial value to set release probability constant
+    }
+:    VERBATIM
+:        if (debug >= 2 ){
+:            fprintf(stdout, "update start t = %f ts=%f: F=%7.2f CaDi = %g CaFi = %g\n", \
+:            t, tstep, F, CaDi, CaFi);
+:            fprintf(stdout, "    vars:  taud=%g: tauf=%g kd = %g kmax= %g\n", taud, tauf, kd, kmax);
+:            fprintf(stdout, "    CaDi = %g CaFi = %g\n", CaDi, CaFi);
+:            fprintf(stdout, "    CaDn = %g CaFn = %g\n", CaDn, CaFn);
+:            fprintf(stdout, "    eta: %g\n", eta);
+:            fprintf(stdout, "    Fn=%7.2f Dn: %7.2f  CaDi = %g CaFi = %g,\n", \
+:            Fn, Dn, CaDi, CaFi);
+:        }
+:    ENDVERBATIM
+}
+
+
+NET_RECEIVE(weight) {
+    : A spike has been received; process synaptic release
+    
+    : First, update DKR state to determine new release probability
+    update_dkr(t - tSpike)
+    tSpike = t      : save the time of spike
+    
+    TTotal = 0 : reset total transmitter from this calyx for each release
+    nRequests = nRequests + 1 : count the number of inputs that we received
+    
+    : Next, process vesicle release using new release probability
+    if (multisite == 1) {
+        release_multisite()
+    }
+    else {
+        release_singlesite()
+    }
+}
+
+
+PROCEDURE release_multisite() {
+    : Vesicle release procedure for multi-site terminal.
+    : Loops over multiple zones using release probability Fn*Dn to decide whether
+    : each site will release, and selecting an appropriate release latency. 
+    
+    : The synapse can release one vesicle per AP per zone, with a probability 0<p<1.
+    : The probability, p, is defined by the time evolution of a Dittman-Regher model
+    : of release, whose parameters are set during initialization.
+    : The vesicle can be released over a variable time interval defined by a lognormal
+    : distribution, plus a fixed latency.
+    
+    FROM i = 0 TO (nZones-1) { : for each zone in the synapse
+        if(tRelease[i] < t) {
+            scrand = scop_random()
+            : look to make release if we have not already (single vesicle per zone per spike)
+            : check for release and release probability - assume infinite supply of vesicles
+            if (scrand  < Fn*Dn) { 
+                nReleases = nReleases + 1 : count number of releases since inception
+                TTotal = TTotal + 1     : count total releases this trial.
+
+:               Compute the median latency for this vesicle.
+                if (Lat_Flag == 0 || t < Lat_t0) {
+                    vesicleLatency = latency : use a fixed value
+                }
+                else {
+                    vesicleLatency = latency + Lat_A0*(1-exp(-(t-Lat_t0)/Lat_tau)) : latency rises during train
+                }
+:               Now compute distribution around that latency
+:               if LN_Flag is 1, we adjust the sigma values over time, otherwise we just use a fixed value.
+:               The following math applies:
+:               lognormal dist = exp(X), where X = gaussian(mu, sigma).
+:               The median of the lognormal dist. is e^mu; Note that if mu is 0, then the median is 1.0
+:               The mode of the lognormal dist is e^(u-sigma^2). 
+:               Note that normrand is a SCoP function, see http://cns.iaf.cnrs-gif.fr/files/scopman.html
+                if (LN_Flag == 0 || t < LN_t0) { : use fixed sigma in lognormal distribution for all time.
+                    if (latstd > 0.0) {
+                        latzone = normrand(0.0, latstd) : set a latency for the zone with one draw from the distribution
+                        latzone = exp(latzone) - 1.0 + vesicleLatency : the latency should not be too short.... 
+                    }
+                    else {
+                        latzone = vesicleLatency : fixed value
+                    }
+                }
+                else {
+                    sigma = latstd + LN_A0*(1-exp(-(t-LN_t0)/LN_tau)) : time-dependent std shift
+                    latzone = normrand(0.0, sigma)
+                    latzone = exp(latzone)-1.0 + vesicleLatency
+                }
+                if (latzone < 0.0) { : this is to be safe... must have causality.
+                    latzone = 0.0
+                }
+                if (ev_index < EVENT_N) { : save event distribution list for verification
+                    EventLatencies[ev_index] = latzone
+                    EventTime[ev_index] = t
+                    ev_index = ev_index + 1
+                }
+                
+                : release time for this event
+                tRelease[i] = t + latzone
+            }
+        }
+    }
+}
+
+
+PROCEDURE release_singlesite() {
+    LOCAL pr
+    tRelease[0] = t
+    pr = Fn * Dn
+    FROM i = 0 TO (nZones-1) {
+        if (rand_uniform() < pr) { 
+            TTotal = TTotal + 1     : count total releases this trial.
+        }
+    }
+    : Tell PSD to multiply its final current by the number of active zones
+    N_ACTIVE[0] = TTotal
+    printf("Release: %f\n", TTotal)
+}

cnmodel/mechanisms/na.mod

@@ -0,0 +1,100 @@
+TITLE na.mod  A sodium channel for cochlear nucleus neurons
+
+COMMENT
+
+NEURON implementation of Jason Rothman's measurements of VCN conductances.
+
+This file implements the average brain sodium current used in the Rothman model.
+In the absence of direct measurements in the VCN, this is a fair assumption.
+The model differs from the one used in Rothman et al, (1993) in that the steep
+voltage dependence of recovery from inactivation in that model is missing. This
+may affect the refractory period. To use the other model, use najsr.mod instead.
+
+Original implementation by Paul B. Manis, April (JHU) and Sept, (UNC)1999.
+
+File split implementaiton, April 1, 2004.
+
+Contact: pmanis@med.unc.edu
+
+Modifed implementation; includes all temperature scaling, passes modlunit
+7/10/2014 pbm
+
+ENDCOMMENT
+
+UNITS {
+        (mA) = (milliamp)
+        (mV) = (millivolt)
+        (nA) = (nanoamp)
+}
+
+NEURON {
+        THREADSAFE
+        SUFFIX na
+        USEION na READ ena WRITE ina
+        RANGE gbar, gna, ina
+        GLOBAL hinf, minf, htau, mtau
+}
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+PARAMETER {
+        v (mV)
+        dt (ms)
+        ena (mV)
+        gbar =  0.07958 (mho/cm2) <0,1e9>
+        q10tau = 3.0
+        q10g = 2.0
+
+}
+
+STATE {
+        m h
+}
+
+ASSIGNED {
+    celsius (degC)  : model is defined on measurements made at room temp in Baltimore
+    ina (mA/cm2) 
+    gna (mho/cm2)
+    minf hinf
+    mtau (ms) htau (ms)
+    qg ()  : computed q10 for gnabar based on q10g
+    q10 ()
+    
+}
+
+LOCAL mexp, hexp
+
+BREAKPOINT {
+    SOLVE states METHOD cnexp
+
+    gna = qg*gbar*(m^3)*h
+    ina = gna*(v - ena)
+}
+
+INITIAL {
+    qg = q10g^((celsius-22)/10 (degC))
+    q10 = q10tau^((celsius - 22)/10 (degC)) : if you don't like room temp, it can be changed!
+    rates(v)
+    m = minf
+    h = hinf
+}
+
+DERIVATIVE states {  :Computes state variables m, h, and n
+    rates(v)      :             at the current v and dt.
+    m' = (minf - m)/mtau
+    h' = (hinf - h)/htau
+}
+
+PROCEDURE rates(v (mV)) {  :Computes rate and other constants at current v.
+                      :Call once from HOC to initialize inf at resting v.
+
+: average sodium channel
+    minf = 1 / (1+exp(-(v + 38) / 7 (mV)))
+    hinf = 1 / (1+exp((v + 65) / 6 (mV)))
+
+    mtau =  (10 (ms)/ (5*exp((v+60) / 18 (mV)) + 36*exp(-(v+60) / 25 (mV)))) + 0.04
+    mtau = mtau/q10
+    htau =  (100 (ms)/ (7*exp((v+60) / 11 (mV)) + 10*exp(-(v+60) / 25 (mV)))) + 0.6
+    htau = htau/q10
+}
+

cnmodel/mechanisms/nacn.mod

@@ -0,0 +1,103 @@
+TITLE nacn.mod  A sodium conductance for a ventral cochlear nucleus neuron model
+
+COMMENT
+
+NEURON implementation of Jason Rothman's measurements of VCN conductances.
+
+This file implements the average brain sodium current used in the Rothman model.
+In the absence of direct measurements in the VCN, this is a fair assumption.
+The model differs from the one used in Rothman et al, (1993) in that the steep
+voltage dependence of recovery from inactivation in that model is missing. This
+may affect the refractory period. To use the other model, use najsr.mod instead.
+
+Original implementation by Paul B. Manis, April (JHU) and Sept, (UNC)1999.
+
+File split implementaiton, April 1, 2004.
+
+ Does not pass modlunit.
+Should work at 22C and scales by Rothman and Manis, 2003c for temperature
+
+Contact: pmanis@med.unc.edu
+
+ENDCOMMENT
+
+UNITS {
+        (mA) = (milliamp)
+        (mV) = (millivolt)
+        (nA) = (nanoamp)
+}
+
+NEURON {
+        THREADSAFE
+        SUFFIX nacn
+        USEION na READ ena WRITE ina
+        RANGE gbar, gna, ina
+        GLOBAL hinf, minf, htau, mtau
+}
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+PARAMETER {
+        v (mV)
+        celsius (degC) : 22 (degC) model is defined on measurements made at room temp in Baltimore
+        dt (ms)
+        ena (mV)
+        gbar =  0.07958 (mho/cm2) <0,1e9>
+        q10tau = 3.0 : q10 for rates
+}
+
+STATE {
+        m h
+}
+
+ASSIGNED {
+    ina (mA/cm2) 
+    gna (mho/cm2)
+    minf hinf
+    mtau (ms) htau (ms)
+    q10 ()
+    
+    }
+
+LOCAL mexp, hexp
+
+BREAKPOINT {
+	SOLVE states METHOD cnexp
+    gna = gbar*(m^3)*h
+    ina = gna*(v - ena)
+}
+
+UNITSOFF
+
+INITIAL {
+    rates(v)
+    m = minf
+    h = hinf
+}
+
+DERIVATIVE states {  :Computes state variables m, h, and n
+	rates(v)      :             at the current v and dt.
+	m' = (minf - m)/mtau : m = m + mexp*(minf-m)
+	h' = (hinf - h)/htau : h = h + hexp*(hinf-h)
+
+}
+
+LOCAL qt
+
+PROCEDURE rates(v) {  :Computes rate and other constants at current v.
+                      :Call once from HOC to initialize inf at resting v.
+
+	q10 = q10tau^((celsius - 22)/10) : if you don't like room temp, it can be changed!
+
+: average sodium channel
+    minf = 1 / (1+exp(-(v + 38) / 7))
+    hinf = 1 / (1+exp((v + 65) / 6))
+
+    mtau =  (10 / (5*exp((v+60) / 18) + 36*exp(-(v+60) / 25))) + 0.04
+    mtau = mtau/q10
+    htau =  (100 / (7*exp((v+60) / 11) + 10*exp(-(v+60) / 25))) + 0.6
+    htau = htau/q10
+}
+
+
+UNITSON

cnmodel/mechanisms/nacncoop.mod

@@ -0,0 +1,138 @@
+TITLE nacn.mod  A sodium conductance for a ventral cochlear nucleus neuron model
+
+COMMENT
+
+NEURON implementation of Jason Rothman's measurements of VCN conductances.
+
+This file implements a modified version of the average brain sodium current
+    used in the Rothman and Manis 2003 models.
+    
+The model differs from the one used in Rothman et al, (1993) in that the steep
+voltage dependence of recovery from inactivation in that model is missing. This
+may affect the refractory period. To use the other model, use jsrnaf.mod instead.
+
+Original implementation by Paul B. Manis, April 1999 (JHU) and Sept 1999 (UNC-CH).
+
+File split implementation, April 1, 2004.
+
+
+Version nacncoop implements a cooperative sodium channel model built on the kinetics
+of the original nacn model (R&M2003c). The motivation is to make a sodium channel with
+faster activation kinetics, by introducing cooperativity between a subset of channels.
+The model is based on concepts and implementation similar to Oz et al. 
+J.Comp. Neurosci. 39: 63, 2015, and Huang et al., PloSOne 7:e37729, 2012.
+The cooperative channels are modeled with the same kinetics as the non-cooperative
+channels, but are treated as a separate subset (fraction: p). The cooperativity is
+introduced by shifting the voltage "seen" by the channels by KJ*m^3*h, which moves
+the channels to a faster regime (essentially, they experience a depolarized membrane
+potential that depends on their current gating state, relative to the main population
+of channels).
+
+A subpopulation of Na channels (p [0..1]) experiences a small voltage-dependent shift
+in the gating kinetics. The shift is determined by KJ
+
+This version does not have all the temperature scaling. Does not pass modlunit.
+Should work at 22C, appears to work at other temperatures ok.
+
+Contact: pmanis@med.unc.edu
+
+ENDCOMMENT
+
+UNITS {
+        (mA) = (milliamp)
+        (mV) = (millivolt)
+        (nA) = (nanoamp)
+}
+
+NEURON {
+        THREADSAFE
+        SUFFIX nacncoop
+        USEION na READ ena WRITE ina
+        RANGE gbar, gna, ina, p, KJ
+    	RANGE vsna : voltage shift parameter
+        GLOBAL hinf, minf, htau, mtau, hinf2, minf2, htau2, mtau2
+}
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+PARAMETER {
+        v (mV)
+        celsius (degC) : 22 (degC) model is defined at room temp in Baltimore
+        dt (ms)
+        ena (mV)
+        gbar =  0.07958 (mho/cm2) <0,1e9>
+        q10 = 3.0 : q10 for rates
+        p  = 0.0 (): fraction of cooperative channels (0-1)
+        KJ = 0 (mV) : coupling strength between cooperative channels (0-1000mV is usable range)
+                      : setting either KJ = 0 or p = 0 will remove cooperativity.
+        vsna = 0 (mV)
+}
+
+STATE {
+        m h m2 h2
+}
+
+ASSIGNED {
+    ina (mA/cm2) 
+    gna (mho/cm2)
+    vNa (mV)  : shifted V for cooperative behavior
+    minf hinf minf2 hinf2
+    mtau (ms) htau (ms) mtau2 (ms) htau2 (ms)
+
+    }
+
+LOCAL mexp, hexp, mexp2, hexp2
+
+BREAKPOINT {
+    SOLVE states METHOD cnexp
+    
+    gna = gbar*(p*(m2^3*h2) + (1.-p)*(m^3)*h)
+    ina = gna*(v - ena)
+}
+
+UNITSOFF
+
+INITIAL {
+    rates(v)
+    m = minf
+    h = hinf
+    m2 = minf2
+    h2 = hinf2
+    vNa = v + vsna + KJ*m^3*h
+}
+
+DERIVATIVE states {  :Computes state variables m, h, and n
+    rates(v)      :             at the current v and dt.
+    m' = (minf - m)/mtau 
+    h' = (hinf - h)/htau
+    m2' = (minf2 - m2)/mtau2
+    h2' = (hinf2 - h2)/htau2
+    vNa = v + vsna + KJ*m^3*h  : note addition of vsna shift here so that we do not add it in rates
+}
+
+LOCAL qt
+
+PROCEDURE rates(v) {  :Computes rate and other constants at current v.
+                      :Call once from HOC to initialize inf at resting v.
+
+    qt = q10^((celsius - 22)/10) : if you don't like room temp, it can be changed!
+
+: average sodium channel
+    minf = 1 / (1+exp(-(v + 38 + vsna) / 7))
+    hinf = 1 / (1+exp((v + 65 + vsna) / 6))
+    mtau =  (10 / (5*exp((v + 60 + vsna) / 18) + 36*exp(-(v + 60+vsna) / 25))) + 0.04
+    mtau = mtau/qt
+    htau =  (100 / (7*exp((v + 60 + vsna) / 11) + 10*exp(-(v + 60 + vsna) / 25))) + 0.6
+    htau = htau/qt
+
+: cooperative group of channels
+    minf2 = 1 / (1+exp(-(vNa + 38) / 7))
+    hinf2 = 1 / (1+exp((vNa + 65) / 6))
+    mtau2 =  (10 / (5*exp((vNa+60) / 18) + 36*exp(-(vNa+60) / 25))) + 0.04
+    mtau2 = mtau2/qt
+    htau2 =  (100 / (7*exp((vNa+60) / 11) + 10*exp(-(vNa+60) / 25))) + 0.6
+    htau2 = htau2/qt
+
+}
+
+UNITSON

cnmodel/mechanisms/nap.mod

@@ -0,0 +1,130 @@
+TITLE nap.mod   Persistent sodium conductance
+ 
+COMMENT
+
+
+Persistent sodium current from deSchutter and Bower, J. Neurophys.
+71:375, 1994.
+
+
+2/10/02, 10/10/2014. P. Manis.
+
+ENDCOMMENT
+ 
+ 
+UNITS {
+ (mA) = (milliamp)
+ (mV) = (millivolt)
+}
+ 
+
+NEURON {
+ THREADSAFE
+ SUFFIX nap
+ USEION na READ ena WRITE ina
+ 
+ RANGE nap_inf, nap_tau, napi_inf, napi_tau
+ RANGE gbar, gnap
+ RANGE nap_A, nap_B, nap_C, nap_D, nap_E, nap_F, nap_G, nap_H
+ 
+}
+ 
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+ 
+PARAMETER {
+ v (mV)
+ celsius (degC)
+ dt (ms)
+ ena (mV) : = 50.0 (mV)
+ gbar = 0.00001 (mho/cm2) <0,1e9>
+ q10tau = 3.0
+ q10g = 2.0
+ 
+ nap_shift = 0 (mV)
+ 
+ nap_A = 200  (/ms): parameters from Bowers et al.
+ nap_B = 1
+ nap_C = -18 (mV)
+ nap_D = -16 (mV)
+ nap_E = 25 (/ms)
+ nap_F = 1
+ nap_G = 58 (mV)
+ nap_H = 8 (mV)
+ }
+ 
+STATE {
+        nap napi 
+        }
+ 
+ASSIGNED {
+	gnap (mho/cm2)
+	ina (mA/cm2)
+	nap_inf
+	nap_tau (ms)
+    nap_tau1 (/ms)
+    nap_tau2 (/ms)
+	napi_inf
+	napi_tau (ms)
+    napi_tau1 (/ms)
+    napi_tau2 (/ms)
+    qg ()  : computed q10 for gnabar based on q10g
+    q10 ()
+}
+
+BREAKPOINT {
+	SOLVE states METHOD cnexp
+	gnap = gbar*nap*nap*nap*napi
+	ina = gnap*(v-ena)
+}
+
+ 
+INITIAL {
+    qg = q10g^((celsius-22)/10 (degC))
+    q10 = q10tau^((celsius - 22)/10 (degC)) : if you don't like room temp, it can be changed!
+	rates(v)
+	nap = nap_inf
+	napi = napi_inf
+}
+
+
+DERIVATIVE states {  
+	rates(v)
+	nap' = (nap_inf - nap) / nap_tau
+	napi' = (napi_inf - napi) / napi_tau
+}
+ 
+
+PROCEDURE rates(v (mV)) {  :Computes rate and other constants at current v.
+	                      :Call once from HOC to initialize inf at resting v.
+    LOCAL x
+	: "nap" persistent sodium system
+	nap_inf = na_p(v + nap_shift)
+	nap_tau1 = nap_A/(nap_B + exp((v + nap_shift + nap_C)/nap_D))
+    nap_tau2 = nap_E/(nap_F + exp((v + nap_shift + nap_G)/nap_H))
+	nap_tau = 1./(nap_tau1 + nap_tau2)
+
+	:nap_tau = na_ptau(v + nap_shift)
+
+	: "nap" persistent sodium system - inactivation...
+	napi_inf = na_pi(v + nap_shift)
+    napi_tau1 = (1 (/ms)) /(0.06435/(1+exp((v + nap_shift + 73.26415)/3.71928 (mV))))
+    napi_tau2 = (0.13496 (/ms))/(1 +exp((v + nap_shift + 10.27853)/(-9.09334 (mV))))
+	napi_tau = 1 /(napi_tau1 + napi_tau2)
+}
+ 
+LOCAL p
+
+FUNCTION na_p(v (mV)) { : persistent sodium activation
+: Bowers
+	p = nap_A/(nap_B + exp((v + nap_shift + nap_C)/nap_D))
+	na_p = p/(p+nap_E/(nap_F + exp((v + nap_shift + nap_G)/nap_H)))
+}
+
+
+FUNCTION na_pi(x (mV)) { : persistent sodium inactivation
+: Bowers
+	na_pi = 0.06435/(1+exp((x+73.26415)/3.71928 (mV)))
+	na_pi = na_pi/(na_pi + (0.13496/(1+exp((v+10.27853)/(-9.09334 (mV))))))
+}
+
+

cnmodel/mechanisms/napyr.mod

@@ -0,0 +1,137 @@
+TITLE pyrna.mod   DCN pyramidal cell model sodium channel
+ 
+COMMENT
+
+Revised version of DCN Pyramidal cell model sodium channel
+
+This model implements part of a Dorsal Cochlear Nucleus Pyramidal point cell
+based on kinetic data from Kanold and Manis (1999) and Kanold's dissertation (1999)
+
+-- 15 Jan 1999 P. Manis
+
+This mechanism is the fast sodium channel portion of the model.
+
+
+Orignal: 2/10/02. P. Manis.
+
+Extraced from Pyr.mod, 7/24/2014.
+
+ENDCOMMENT
+ 
+ 
+UNITS {
+    (mA) = (milliamp)
+    (mV) = (millivolt)
+}
+ 
+
+NEURON {
+    THREADSAFE
+    SUFFIX napyr
+    USEION na READ ena WRITE ina
+    RANGE gna, minf, hinf, ninf, gbar	: sodium channels and delayed rectifier
+    RANGE mtau, htau, ntau    : time constants for sodium channels and delayed rectifier
+}
+ 
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+ 
+PARAMETER {
+ v (mV)
+ celsius (degC)
+ dt (ms)
+ ek (mV) : = -81.5 (mV)
+ ena (mV) : = 50.0 (mV)
+ gbar =  0.02857 (mho/cm2)	<0,1e9>
+ mtau0 = 0.05 (ms) <0.01,100>
+ htau0 = 0.5 (ms) <0.1,100>
+ ntau = 0.5 (ms) <0.1,100>
+ }
+ 
+STATE {
+        m h 
+        }
+ 
+ASSIGNED {
+	gna (mho/cm2)
+	ina (mA/cm2)
+	minf hinf mtau htau
+}
+
+LOCAL mexp, hexp
+
+BREAKPOINT {
+	SOLVE states METHOD cnexp
+	gna = gbar*m*m*h
+	ina = gna*(v - ena)
+}
+
+UNITSOFF 
+ 
+INITIAL {
+	rates(v)
+	m = minf
+	h = hinf
+}
+
+DERIVATIVE states {
+	rates(v)
+	m' = (minf - m) / mtau
+	h' = (hinf - h) / htau
+}
+ 
+LOCAL q10
+
+
+PROCEDURE rates(v(mV)) {  :Computes rate and other constants at current v.
+	                      :Call once from HOC to initialize inf at resting v.
+	LOCAL  alpha, beta, sum
+	TABLE minf, mtau, hinf, htau  DEPEND celsius FROM -200 TO 100 WITH 400
+
+UNITSOFF
+	q10 = 3^((celsius - 22)/10)
+
+	: "m" sodium activation system
+	minf = na_m(v)
+	mtau = na_mt(v)
+	
+	: "h" sodium inactivation system
+	hinf = na_h(v)
+	htau = na_ht(v)
+	
+}
+ 
+: Make these as functions so we can view them from hoc, although this 
+: may slow things down a bit
+
+FUNCTION na_m(x) { : sodium activation
+	na_m = 1/(1+exp(-(x+38)/3.0))	: POK version
+:	na_m = alphbet(x,35,0,5,-10) :de Schutter (doesn't work well in our version)
+:	na_m = na_m/(na_m + alphbet(x,7,0,65,20))
+}
+
+FUNCTION na_mt(x) { : sodium activation with taus
+	na_mt = mtau0 : flat time constants
+:	na_mt = alphbet(x,35,0,5,-10)
+:	na_mt = 1/(na_mt + alphbet(x,7,0,65,20))
+}
+
+FUNCTION na_h(x) { : sodium inactivation
+	na_h = 1/(1+exp((x+43)/3.0))	: flat time constants (POK version)
+:	na_h = alphbet(x,0.225,1,80,10)	
+:	na_h = na_h/(na_h + alphbet(x,7.5,0,-3,-18))
+}
+
+FUNCTION na_ht(x) { : sodium inactivation tau
+	na_ht = htau0 : POK: flat time constants
+:	na_ht = alphbet(x,0.225,1,80,10) : de Schutter version (doesn't work well with other stuff)
+:	na_ht = 1/(na_ht + alphbet(x,7.5,0,-3,-18))
+}
+
+
+FUNCTION alphbet(x,A,B,C,D) { 	: alpha/beta general functions for
+				: transcrbing GENESIS models
+alphbet = A/(B+exp((x+C)/D))
+}
+
+UNITSON
+

cnmodel/mechanisms/pkjlk.mod

@@ -0,0 +1,17 @@
+TITLE Purkinje Leak Current
+
+: A passive purkinje cell leak current
+NEURON {
+	SUFFIX lkpkj
+	NONSPECIFIC_CURRENT i
+	RANGE i, e, gbar
+}
+PARAMETER {
+	gbar = 5e-5	(siemens/cm2)  < 0, 1e9 >
+	e = -60.5	(millivolt)
+}
+ASSIGNED {
+	i  (milliamp/cm2)
+	v  (millivolt)
+}
+BREAKPOINT { i = gbar*(v - e) }

cnmodel/mechanisms/rsg.mod

@@ -0,0 +1,196 @@
+TITLE Rsg sodium channel
+: Resurgent sodium channel (with blocking particle)
+: with updated kinetic parameters from Raman and Bean  
+
+NEURON {
+  SUFFIX naRsg
+  USEION na READ ena WRITE ina
+  RANGE gna, gbar
+}
+
+UNITS { 
+	(mV) = (millivolt)
+	(S) = (siemens)
+}
+
+PARAMETER {
+	gbar = .015			(S/cm2)
+
+	: kinetic parameters
+	Con = 0.005			(/ms)					: closed -> inactivated transitions
+	Coff = 0.5			(/ms)					: inactivated -> closed transitions
+	Oon = .75			(/ms)					: open -> Ineg transition
+	Ooff = 0.005		(/ms)					: Ineg -> open transition
+	alpha = 150			(/ms)					: activation
+	beta = 3			(/ms)					: deactivation
+	gamma = 150			(/ms)					: opening
+	delta = 40			(/ms)					: closing, greater than BEAN/KUO = 0.2
+	epsilon = 1.75		(/ms)					: open -> Iplus for tau = 0.3 ms at +30 with x5
+	zeta = 0.03			(/ms)					: Iplus -> open for tau = 25 ms at -30 with x6
+
+	: Vdep
+	x1 = 20				(mV)								: Vdep of activation (alpha)
+	x2 = -20			(mV)								: Vdep of deactivation (beta)
+	x3 = 1e12			(mV)								: Vdep of opening (gamma)
+	x4 = -1e12			(mV)								: Vdep of closing (delta)
+	x5 = 1e12			(mV)								: Vdep into Ipos (epsilon)
+	x6 = -25			(mV)								: Vdep out of Ipos (zeta)
+}
+
+ASSIGNED {
+	alfac   				: microscopic reversibility factors
+	btfac				
+
+	: rates
+	f01  		(/ms)
+	f02  		(/ms)
+	f03 		(/ms)
+	f04			(/ms)
+	f0O 		(/ms)
+	fip 		(/ms)
+	f11 		(/ms)
+	f12 		(/ms)
+	f13 		(/ms)
+	f14 		(/ms)
+	f1n 		(/ms)
+	fi1 		(/ms)
+	fi2 		(/ms)
+	fi3 		(/ms)
+	fi4 		(/ms)
+	fi5 		(/ms)
+	fin 		(/ms)
+
+	b01 		(/ms)
+	b02 		(/ms)
+	b03 		(/ms)
+	b04		(/ms)
+	b0O 		(/ms)
+	bip 		(/ms)
+	b11  		(/ms)
+	b12 		(/ms)
+	b13 		(/ms)
+	b14 		(/ms)
+	b1n 		(/ms)
+	bi1 		(/ms)
+	bi2 		(/ms)
+	bi3 		(/ms)
+	bi4 		(/ms)
+	bi5 		(/ms)
+	bin 		(/ms)
+	
+	v					(mV)
+ 	ena					(mV)
+	ina 					(milliamp/cm2)
+	gna					(S/cm2)
+}
+
+STATE {
+	C1 FROM 0 TO 1
+	C2 FROM 0 TO 1
+	C3 FROM 0 TO 1
+	C4 FROM 0 TO 1
+	C5 FROM 0 TO 1
+	I1 FROM 0 TO 1
+	I2 FROM 0 TO 1
+	I3 FROM 0 TO 1
+	I4 FROM 0 TO 1
+	I5 FROM 0 TO 1
+	O FROM 0 TO 1
+	B FROM 0 TO 1
+	I6 FROM 0 TO 1
+}
+
+BREAKPOINT {
+ SOLVE activation METHOD sparse
+ gna = gbar * O  : O is "open state"
+ ina = gna * (v - ena)
+}
+
+INITIAL {
+ rates(v)
+ SOLVE seqinitial
+}
+
+KINETIC activation
+{
+	rates(v)
+	~ C1 <-> C2					(f01,b01)
+	~ C2 <-> C3					(f02,b02)
+	~ C3 <-> C4					(f03,b03)
+	~ C4 <-> C5					(f04,b04)
+	~ C5 <-> O					(f0O,b0O)
+	~ O <-> B					(fip,bip)
+	~ O <-> I6					(fin,bin)
+	~ I1 <-> I2					(f11,b11)
+	~ I2 <-> I3					(f12,b12)
+	~ I3 <-> I4					(f13,b13)
+	~ I4 <-> I5					(f14,b14)
+	~ I5 <-> I6					(f1n,b1n)
+	~ C1 <-> I1					(fi1,bi1)
+	~ C2 <-> I2					(fi2,bi2)
+	~ C3 <-> I3					(fi3,bi3)
+ 	~ C4 <-> I4					(fi4,bi4)
+ 	~ C5 <-> I5					(fi5,bi5)
+
+CONSERVE C1 + C2 + C3 + C4 + C5 + O + B + I1 + I2 + I3 + I4 + I5 + I6 = 1
+}
+
+LINEAR seqinitial { : sets initial equilibrium
+ ~          I1*bi1 + C2*b01 - C1*(    fi1+f01) = 0
+ ~ C1*f01 + I2*bi2 + C3*b02 - C2*(b01+fi2+f02) = 0
+ ~ C2*f02 + I3*bi3 + C4*b03 - C3*(b02+fi3+f03) = 0
+ ~ C3*f03 + I4*bi4 + C5*b04 - C4*(b03+fi4+f04) = 0
+ ~ C4*f04 + I5*bi5 + O*b0O - C5*(b04+fi5+f0O) = 0
+ ~ C5*f0O + B*bip + I6*bin - O*(b0O+fip+fin) = 0
+ ~ O*fip + B*bip = 0
+
+ ~          C1*fi1 + I2*b11 - I1*(    bi1+f11) = 0
+ ~ I1*f11 + C2*fi2 + I3*b12 - I2*(b11+bi2+f12) = 0
+ ~ I2*f12 + C3*fi3 + I4*bi3 - I3*(b12+bi3+f13) = 0
+ ~ I3*f13 + C4*fi4 + I5*b14 - I4*(b13+bi4+f14) = 0
+ ~ I4*f14 + C5*fi5 + I6*b1n - I5*(b14+bi5+f1n) = 0
+ 
+ ~ C1 + C2 + C3 + C4 + C5 + O + B + I1 + I2 + I3 + I4 + I5 + I6 = 1
+}
+
+PROCEDURE rates(v(mV) )
+{
+ alfac = (Oon/Con)^(1/4)
+ btfac = (Ooff/Coff)^(1/4) 
+ f01 = 4 * alpha * exp(v/x1)
+ f02 = 3 * alpha * exp(v/x1)
+ f03 = 2 * alpha * exp(v/x1)
+ f04 = 1 * alpha * exp(v/x1)
+ f0O = gamma * exp(v/x3)
+ fip = epsilon * exp(v/x5)
+ f11 = 4 * alpha * alfac * exp(v/x1)
+ f12 = 3 * alpha * alfac * exp(v/x1)
+ f13 = 2 * alpha * alfac * exp(v/x1)
+ f14 = 1 * alpha * alfac * exp(v/x1)
+ f1n = gamma * exp(v/x3)
+ fi1 = Con
+ fi2 = Con * alfac
+ fi3 = Con * alfac^2
+ fi4 = Con * alfac^3
+ fi5 = Con * alfac^4
+ fin = Oon
+
+ b01 = 1 * beta * exp(v/x2)
+ b02 = 2 * beta * exp(v/x2)
+ b03 = 3 * beta * exp(v/x2)
+ b04 = 4 * beta * exp(v/x2)
+ b0O = delta * exp(v/x4)
+ bip = zeta * exp(v/x6)
+ b11 = 1 * beta * btfac * exp(v/x2)
+ b12 = 2 * beta * btfac * exp(v/x2)
+ b13 = 3 * beta * btfac * exp(v/x2)
+ b14 = 4 * beta * btfac * exp(v/x2)
+ b1n = delta * exp(v/x4)
+ bi1 = Coff
+ bi2 = Coff * btfac
+ bi3 = Coff * btfac^2
+ bi4 = Coff * btfac^3
+ bi5 = Coff * btfac^4
+ bin = Ooff
+}
+

cnmodel/mechanisms/tests/test_mechanisms.py

@@ -0,0 +1,40 @@
+import numpy as np
+from neuron import h
+from cnmodel.util import reset
+
+
+def test_max_open_probability():
+    reset(
+        raiseError=False
+    )  # reset() fails as unable to remove all neuron objects, unless we ignore the error
+    sec = h.Section()
+
+    # Create AMPA and NMDA mechanisms
+    # AMPA uses mode=0; no rectification
+    apsd = h.AMPATRUSSELL(0.5, sec=sec)
+    # For NMDA we will hold the cell at +40 mV
+    npsd = h.NMDA_Kampa(0.5, sec=sec)
+
+    # And a presynaptic terminal to provide XMTR input
+    term = h.MultiSiteSynapse(0.5, sec=sec)
+    term.nZones = 1
+    h.setpointer(term._ref_XMTR[0], "XMTR", apsd)
+    h.setpointer(term._ref_XMTR[0], "XMTR", npsd)
+
+    h.celsius = 34.0
+    h.finitialize()
+    op = [[], []]
+    for i in range(100):
+        # force very high transmitter concentration for every timestep
+        term.XMTR[0] = 10000
+        sec.v = 40.0
+        h.fadvance()
+        op[0].append(apsd.Open)
+        op[1].append(npsd.Open)
+
+    assert np.allclose(max(op[0]), apsd.MaxOpen)
+    assert np.allclose(max(op[1]), npsd.MaxOpen)
+
+
+if __name__ == "__main__":
+    test_max_open_probability()

cnmodel/mechanisms/vecevent.mod

@@ -0,0 +1,72 @@
+:  Vector stream of events
+:  From NEURON source: nrn/examples/nrniv/netcon/vecevent.mod
+    
+NEURON {
+	ARTIFICIAL_CELL VecStim
+}
+
+ASSIGNED {
+	index
+	etime (ms)
+	space
+}
+
+INITIAL {
+	index = 0
+	element()
+	if (index > 0) {
+		net_send(etime - t, 1)
+	}
+}
+
+NET_RECEIVE (w) {
+	if (flag == 1) {
+		net_event(t)
+		element()
+		if (index > 0) {
+			net_send(etime - t, 1)
+		}
+	}
+}
+
+VERBATIM
+extern double* vector_vec();
+extern int vector_capacity();
+extern void* vector_arg();
+ENDVERBATIM
+
+PROCEDURE element() {
+VERBATIM	
+  { void* vv; int i, size; double* px;
+	i = (int)index;
+	if (i >= 0) {
+		vv = *((void**)(&space));
+		if (vv) {
+			size = vector_capacity(vv);
+			px = vector_vec(vv);
+			if (i < size) {
+				etime = px[i];
+				index += 1.;
+			}else{
+				index = -1.;
+			}
+		}else{
+			index = -1.;
+		}
+	}
+  }
+ENDVERBATIM
+}
+
+PROCEDURE play() {
+VERBATIM
+	void** vv;
+	vv = (void**)(&space);
+	*vv = (void*)0;
+	if (ifarg(1)) {
+		*vv = vector_arg(1);
+	}
+ENDVERBATIM
+}
+        
+

cnmodel/morphology/__init__.py

@@ -0,0 +1,12 @@
+#!/usr/bin/python
+#
+# Morphology definitions for models.
+#
+# This file includes readers for
+#
+# Paul B. Manis, Ph.D. 2009 (August - November 2009)
+#
+from neuron import h
+
+from .morphology import Morphology
+from .hoc_reader import HocReader

cnmodel/morphology/bushy_stick.hoc

@@ -0,0 +1,90 @@
+objref soma
+soma = new SectionList()
+objref primarydendrite
+primarydendrite = new SectionList()
+objref secondarydendrite
+secondarydendrite = new SectionList()
+objref hillock
+hillock = new SectionList()
+objref unmyelinatedaxon
+unmyelinatedaxon = new SectionList()
+objref myelinatedaxon
+myelinatedaxon = new SectionList()
+
+create sections[9]
+access sections[0]
+soma.append()
+sections[0] {
+    pt3dadd(0., 0., 0., 25.)
+    pt3dadd(0., 25., 0., 25.)
+}
+
+// axon hillock, 15 microns long, tapered
+access sections[1]
+hillock.append()
+connect sections[1](0), sections[0](0)
+sections[1] {
+    pt3dadd(0., 0., 0., 2.5)
+    pt3dadd(0., -15., 0., 1.0)
+}
+
+// initial segment, unmyelinated, 10 microns long, not tapering
+access sections[2]
+unmyelinatedaxon.append()
+connect sections[2](0), sections[1](1)
+sections[2] {
+    pt3dadd(0., -15., 0., 1.0)
+    pt3dadd(0., -25., 0., 1.0)
+
+}
+
+// beginning of myelinated axon, 20 micron, enlarging with distance
+access sections[3]
+myelinatedaxon.append()
+connect sections[3](0), sections[2](1)
+sections[3] {
+    pt3dadd(0., -25., 0., 1.0)
+    pt3dadd(0., -45., 0., 2.0)
+
+}
+
+access sections[4]
+primarydendrite.append()
+connect sections[4](0), sections[0](1)
+sections[4] {
+    pt3dadd(0., 25., 0., 3.0)
+    pt3dadd(0., 45., 0., 2.0)
+}
+
+access sections[5]
+secondarydendrite.append()
+connect sections[5](0), sections[4](1)
+sections [5] {
+    pt3dadd(0., 45., 0., 2.0)
+    pt3dadd(0., 55., -50., 1.5)
+}
+
+access sections[6]
+secondarydendrite.append()
+connect sections[6](0), sections[4](1)
+sections [6] {
+    pt3dadd(0., 45., 0., 2.0)
+    pt3dadd(0., 55., 50., 1.5)
+}
+
+access sections[7]
+secondarydendrite.append()
+connect sections[7](0), sections[4](1)
+sections [7] {
+    pt3dadd(0., 45., 0., 2.0)
+    pt3dadd(-50., 55., 0., 1.5)
+}
+
+access sections[8]
+secondarydendrite.append()
+connect sections[8](0), sections[4](1)
+sections [8] {
+    pt3dadd(0., 45., 0., 2.0)
+    pt3dadd(50., 55., 0, 1.5)
+}
+