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1089 lines
45 KiB
1089 lines
45 KiB
from __future__ import print_function |
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from neuron import h |
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import numpy as np |
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|
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from .cell import Cell |
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|
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# from .. import synapses |
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from ..util import nstomho |
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from ..util import Params |
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from .. import data |
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|
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__all__ = ["TStellate", "TStellateRothman", "TStellateNav11"] |
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|
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class TStellate(Cell): |
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|
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type = "tstellate" |
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|
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@classmethod |
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def create(cls, model="RM03", **kwds): |
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if model == "RM03": # original Rothman-Manis 2003, 22C, point cell, extendable |
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return TStellateRothman(**kwds) |
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elif model == "Nav11": # Xie-Manis, 2013, 37C, pointe cell, extendable |
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return TStellateNav11(**kwds) |
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else: |
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raise ValueError("TStellate type %s is unknown", type) |
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|
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def make_psd(self, terminal, psd_type, **kwds): |
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""" |
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Connect a presynaptic terminal to one post section at the specified location, with the fraction |
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of the "standard" conductance determined by gbar. |
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The default condition is to try to pass the default unit test (loc=0.5) |
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|
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Scaling is corrected by initial release probability now. |
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|
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Parameters |
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---------- |
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terminal : Presynaptic terminal (NEURON object) |
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|
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psd_type : either simple or multisite PSD for bushy cell |
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|
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kwds: dict of options. |
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Available options: |
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postsize : expect a list consisting of [sectionno, location (float)] |
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AMPAScale : float to scale the ampa currents |
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|
|
|
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""" |
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if ( |
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"postsite" in kwds |
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): # use a defined location instead of the default (soma(0.5) |
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postsite = kwds["postsite"] |
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loc = postsite[1] # where on the section? |
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uname = ( |
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"sections[%d]" % postsite[0] |
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) # make a name to look up the neuron section object |
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post_sec = self.hr.get_section(uname) # Tell us where to put the synapse. |
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else: |
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loc = 0.5 |
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post_sec = self.soma |
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# print('cells/tstellaty.py psd type: ', psd_type) |
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if psd_type == "simple": |
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if terminal.cell.type in ["sgc", "dstellate", "tuberculoventral"]: |
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weight = data.get( |
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"%s_synapse" % terminal.cell.type, |
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species=self.species, |
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post_type=self.type, |
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field="weight", |
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) |
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tau1 = data.get( |
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"%s_synapse" % terminal.cell.type, |
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species=self.species, |
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post_type=self.type, |
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field="tau1", |
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) |
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tau2 = data.get( |
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"%s_synapse" % terminal.cell.type, |
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species=self.species, |
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post_type=self.type, |
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field="tau2", |
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) |
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erev = data.get( |
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"%s_synapse" % terminal.cell.type, |
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species=self.species, |
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post_type=self.type, |
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field="erev", |
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) |
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return self.make_exp2_psd( |
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post_sec, |
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terminal, |
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weight=weight, |
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loc=loc, |
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tau1=tau1, |
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tau2=tau2, |
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erev=erev, |
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) |
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else: |
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raise TypeError( |
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"Cannot make simple PSD for %s => %s" |
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% (terminal.cell.type, self.type) |
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) |
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|
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# print('cells/tstellaty.py weight: ', weight) |
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elif psd_type == "multisite": |
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if terminal.cell.type == "sgc": |
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# Max conductances for the glu mechanisms are calibrated by |
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# running `synapses/tests/test_psd.py`. The test should fail |
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# if these values are incorrect |
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self.AMPAR_gmax = ( |
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data.get( |
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"sgc_synapse", |
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species=self.species, |
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post_type=self.type, |
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field="AMPAR_gmax", |
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) |
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* 1e3 |
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) |
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self.NMDAR_gmax = ( |
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data.get( |
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"sgc_synapse", |
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species=self.species, |
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post_type=self.type, |
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field="NMDAR_gmax", |
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) |
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* 1e3 |
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) |
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self.Pr = data.get( |
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"sgc_synapse", species=self.species, post_type=self.type, field="Pr" |
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) |
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# adjust gmax to correct for initial Pr |
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self.AMPAR_gmax = self.AMPAR_gmax / self.Pr |
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self.NMDAR_gmax = self.NMDAR_gmax / self.Pr |
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# old values: |
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# AMPA_gmax = 0.22479596944138733*1e3 # factor of 1e3 scales to pS (.mod mechanisms) from nS. |
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# NMDA_gmax = 0.12281291946623739*1e3 |
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if "AMPAScale" in kwds: |
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self.AMPAR_gmax = ( |
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self.AMPAR_gmax * kwds["AMPAScale"] |
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) # allow scaling of AMPA conductances |
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if "NMDAScale" in kwds: |
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self.NMDAR_gmax = self.NMDAR_gmax * kwds["NMDAScale"] |
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return self.make_glu_psd( |
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post_sec, terminal, self.AMPAR_gmax, self.NMDAR_gmax, loc=loc |
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) |
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elif terminal.cell.type == "dstellate": |
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self.ds_gmax = ( |
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data.get( |
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"dstellate_synapse", |
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species=self.species, |
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post_type=self.type, |
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field="gly_gmax", |
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) |
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* 1e3 |
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) |
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# print('ds max: ', self.ds_gmax) |
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return self.make_gly_psd( |
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post_sec, terminal, psdtype="glyfast", loc=loc, gmax=self.ds_gmax |
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) |
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elif terminal.cell.type == "tuberculoventral": |
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self.tv_gmax = ( |
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data.get( |
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"tuberculoventral_synapse", |
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species=self.species, |
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post_type=self.type, |
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field="gly_gmax", |
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) |
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* 1e3 |
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) |
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return self.make_gly_psd( |
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post_sec, terminal, psdtype="glyfast", loc=loc, gmax=self.tv_gmax |
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) |
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else: |
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raise TypeError( |
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"Cannot make PSD for %s => %s" % (terminal.cell.type, self.type) |
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) |
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else: |
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raise ValueError("Unsupported psd type %s" % psd_type) |
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|
|
|
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class TStellateRothman(TStellate): |
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""" |
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VCN T-stellate base model. |
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Rothman and Manis, 2003abc (Type I-c, Type I-t) |
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""" |
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|
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def __init__( |
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self, |
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morphology=None, |
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decorator=None, |
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nach=None, |
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ttx=False, |
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temperature=None, |
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species="guineapig", |
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modelType=None, |
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modelName=None, |
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debug=False, |
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): |
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""" |
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Initialize a planar stellate (T-stellate) cell, using the default parameters for guinea pig from |
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R&M2003, as a type I cell. |
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Modifications to the cell can be made by calling methods below. These include: |
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Converting to a type IA model (add transient K current) (species: guineapig-TypeIA). |
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Changing "species" to mouse or cat (scales conductances) |
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|
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Parameters |
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---------- |
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morphology : string (default: None) |
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a file name to read the cell morphology from. If a valid file is found, a cell is constructed |
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as a cable model from the hoc file. |
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If None (default), the only a point model is made, exactly according to RM03. |
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|
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decorator : Python function (default: None) |
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decorator is a function that "decorates" the morphology with ion channels according |
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to a set of rules. |
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If None, a default set of channels aer inserted into the first soma section, and the |
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rest of the structure is "bare". |
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|
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nach : string (default: None) |
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nach selects the type of sodium channel that will be used in the model. A channel mechanism |
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by that name must exist. The default is 'nacn', from R&M2003. |
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|
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ttx : Boolean (default: False) |
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If ttx is True, then the sodium channel conductance is set to 0 everywhere in the cell. |
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Currently, this is not implemented. |
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|
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species: string (default 'guineapig') |
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species defines the channel density that will be inserted for different models. Note that |
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if a decorator function is specified, this argument is ignored. |
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|
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modelType: string (default: None) |
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modelType specifies the type of the model that will be used (e.g., "I-c", "I-t"). |
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modelType is passed to the decorator, or to species_scaling to adjust point models. |
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|
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debug: boolean (default: False) |
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debug is a boolean flag. When set, there will be multiple printouts of progress and parameters. |
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|
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Returns |
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------- |
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Nothing |
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""" |
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|
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super(TStellateRothman, self).__init__() |
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self.i_test_range = {"pulse": (-0.15, 0.15, 0.01)} |
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if modelType is None: |
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modelType = "I-c" |
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if species == "guineapig": |
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modelName = "RM03" |
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temp = 22.0 |
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if nach == None: |
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nach = "nacn" |
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if species == "mouse": |
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temp = 34.0 |
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if modelName is None: |
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modelName = "XM13" |
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if nach == None: |
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nach = "nacn" |
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self.i_test_range = {"pulse": (-1.0, 1.0, 0.05)} |
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self.debug = debug |
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self.status = { |
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"species": species, |
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"cellClass": self.type, |
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"modelType": modelType, |
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"modelName": modelName, |
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"soma": True, |
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"axon": False, |
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"dendrites": False, |
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"pumps": False, |
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"na": nach, |
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"ttx": ttx, |
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"name": self.type, |
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"morphology": morphology, |
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"decorator": decorator, |
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"temperature": None, |
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} |
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self.c_m = 0.9e-6 # default in units of F/cm^2 |
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self.spike_threshold = ( |
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-40.0 |
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) # matches threshold in released CNModel (set in base cell class) |
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self.vrange = [-70.0, -55.0] |
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self._valid_temperatures = (temp,) |
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if self.status["temperature"] == None: |
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self.status["temperature"] = temp |
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|
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if morphology is None: |
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""" |
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instantiate a basic soma-only ("point") model |
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""" |
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if self.debug: |
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print( |
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"<< TStellate model: Creating point cell, type={:s} >>".format( |
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modelType |
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) |
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) |
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soma = h.Section( |
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name="TStellate_Soma_%x" % id(self) |
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) # one compartment of about 29000 um2 |
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soma.nseg = 1 |
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self.add_section(soma, "soma") |
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else: |
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""" |
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instantiate a structured model with the morphology as specified by |
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the morphology file |
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""" |
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if self.debug: |
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print("<< TStellate: Creating cell with morphology = %s>>" % morphology) |
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self.set_morphology(morphology_file=morphology) |
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|
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# decorate the morphology with ion channels |
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if decorator is None: # basic model, only on the soma |
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self.mechanisms = ["kht", "ka", "ihvcn", "leak", nach] |
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for mech in self.mechanisms: |
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self.soma.insert(mech) |
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self.soma.ek = self.e_k |
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self.soma.ena = self.e_na |
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self.soma().ihvcn.eh = self.e_h |
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self.soma().leak.erev = self.e_leak |
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self.species_scaling( |
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silent=True, species=species, modelType=modelType |
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) # set the default type II cell parameters |
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else: # decorate according to a defined set of rules on all cell compartments |
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self.decorate() |
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|
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self.save_all_mechs() # save all mechanisms inserted, location and gbar values... |
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self.get_mechs(self.soma) |
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if self.debug: |
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print("<< T-stellate: JSR Stellate Type 1 cell model created >>") |
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|
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def get_cellpars(self, dataset, species="guineapig", modelType="I-c"): |
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cellcap = data.get( |
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dataset, species=species, model_type=modelType, field="soma_Cap" |
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) |
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chtype = data.get( |
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dataset, species=species, model_type=modelType, field="na_type" |
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) |
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pars = Params(cap=cellcap, natype=chtype) |
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# pars.show() |
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|
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if self.status["modelName"] == "RM03": |
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for g in [ |
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"%s_gbar" % pars.natype, |
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"kht_gbar", |
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"ka_gbar", |
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"ih_gbar", |
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"leak_gbar", |
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"leak_erev", |
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"ih_eh", |
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"e_k", |
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"e_na", |
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]: |
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pars.additem( |
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g, data.get(dataset, species=species, model_type=modelType, field=g) |
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) |
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if self.status["modelName"] == "XM13": |
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for g in [ |
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"%s_gbar" % pars.natype, |
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"kht_gbar", |
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"ka_gbar", |
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"ihvcn_gbar", |
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"leak_gbar", |
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"leak_erev", |
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"ih_eh", |
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"e_k", |
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"e_na", |
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]: |
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pars.additem( |
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g, data.get(dataset, species=species, model_type=modelType, field=g) |
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) |
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if self.status["modelName"] == "mGBC": |
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for g in [ |
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"%s_gbar" % pars.natype, |
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"kht_gbar", |
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"ka_gbar", |
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"ihvcn_gbar", |
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"leak_gbar", |
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"leak_erev", |
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"ih_eh", |
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"e_k", |
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"e_na", |
|
]: |
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pars.additem( |
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g, data.get(dataset, species=species, model_type=modelType, field=g) |
|
) |
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return pars |
|
|
|
def species_scaling(self, species="guineapig", modelType="I-c", silent=True): |
|
""" |
|
Adjust all of the conductances and the cell size according to the species requested. |
|
Used ONLY for point models. |
|
|
|
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 |
|
|
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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 modelType == "I-c": |
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celltype = "tstellate" |
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elif modelType == "I-t": |
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celltype = "tstellate-t" |
|
else: |
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raise ValueError("model type not recognized") |
|
|
|
if species == "mouse": # and modelType == 'I-c': |
|
# use conductance levels from Cao et al., J. Neurophys., 2007. |
|
# 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 self.debug: |
|
print( |
|
" Setting Conductances for mouse I-c Tstellate cell, (modified from Xie and Manis, 2013)" |
|
) |
|
self.c_m = 0.9 # default in units of F/cm^2 |
|
dataset = "XM13_channels" |
|
self.vrange = [-75.0, -55.0] |
|
self.set_soma_size_from_Cm(25.0) |
|
self._valid_temperatures = (34.0,) |
|
if self.status["temperature"] is None: |
|
self.set_temperature(34.0) |
|
|
|
pars = self.get_cellpars(dataset, species=species, modelType=modelType) |
|
# pars.show() |
|
self.set_soma_size_from_Cm(pars.cap) |
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self.status["na"] = pars.natype |
|
self.adjust_na_chans(soma, gbar=pars.nacn_gbar, sf=1.0) |
|
soma().kht.gbar = nstomho(pars.kht_gbar, self.somaarea) |
|
soma().ka.gbar = nstomho(pars.ka_gbar, self.somaarea) |
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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 |
|
self.axonsf = 0.5 |
|
|
|
elif species == "guineapig": |
|
# and modelType == 'I-c': # values from R&M 2003, Type I |
|
if self.debug: |
|
print( |
|
" Setting Conductances for Guinea Pig I-c, Rothman and Manis, 2003" |
|
) |
|
dataset = "RM03_channels" |
|
self.c_m = 0.9 # default in units of F/cm^2 |
|
self.vrange = [-75.0, -55.0] |
|
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. |
|
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().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-type %s is not recognized for T-stellate cells" |
|
% (species, type) |
|
) |
|
|
|
self.status["species"] = species |
|
self.status["modelType"] = modelType |
|
self.check_temperature() |
|
|
|
# def channel_manager(self, modelType='RM03'): |
|
# """ |
|
# 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, called from it's private |
|
# _biophys function) to decorate the cell membrane with channels. |
|
# |
|
# 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 == 'RM03': |
|
# totcap = 12.0E-12 # TStellate cell (type I) from Rothman and Manis, 2003, as base model |
|
# refarea = totcap / self.c_m # see above for units |
|
# # Type I stellate Rothman and Manis, 2003c |
|
# self._valid_temperatures = (22., 38.) |
|
# if self.status['temperature'] is None: |
|
# self.set_temperature(22.) |
|
# sf = 1.0 |
|
# if self.status['temperature'] == 38.: # adjust for 2003 model conductance levels at 38 |
|
# sf = 3.03 # Q10 of 2, 22->38C. (p3106, R&M2003c) |
|
# self.gBar = Params(nabar=sf*1000.0E-9/refarea, |
|
# khtbar=sf*150.0E-9/refarea, |
|
# kltbar=sf*0.0E-9/refarea, |
|
# ihbar=sf*0.5E-9/refarea, |
|
# leakbar=sf*2.0E-9/refarea, |
|
# ) |
|
# |
|
# self.channelMap = { |
|
# 'axon': {'nacn': 0.0, 'klt': 0., 'kht': self.gBar.khtbar, |
|
# 'ihvcn': 0., 'leak': self.gBar.leakbar / 4.}, |
|
# 'hillock': {'nacn': self.gBar.nabar, 'klt': 0., 'kht': self.gBar.khtbar, |
|
# 'ihvcn': 0., 'leak': self.gBar.leakbar, }, |
|
# 'initseg': {'nacn': self.gBar.nabar, 'klt': 0., '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 / 2.0, 'klt': 0., 'kht': self.gBar.khtbar * 0.5, |
|
# 'ihvcn': self.gBar.ihbar / 3., 'leak': self.gBar.leakbar * 0.5, }, |
|
# 'apic': {'nacn': 0.0, 'klt': 0., 'kht': self.gBar.khtbar * 0.2, |
|
# 'ihvcn': self.gBar.ihbar / 4., |
|
# 'leak': self.gBar.leakbar * 0.2, }, |
|
# } |
|
# self.irange = np.linspace(-0.1, 0.1, 7) |
|
# self.distMap = {'dend': {'klt': {'gradient': 'linear', 'gminf': 0., 'lambda': 100.}, |
|
# 'kht': {'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.}}, # gradients are: flat, linear, exponential |
|
# } |
|
# |
|
# elif modelType == 'XM13': |
|
# totcap = 25.0E-12 # Base model from Xie and Manis, 2013 for type I stellate cell |
|
# refarea = totcap / self.c_m # see above for units |
|
# self._valid_temperatures = (34.,) |
|
# if self.status['temperature'] is None: |
|
# self.set_temperature(34.) |
|
# self.gBar = Params(nabar=1800.0E-9/refarea, |
|
# khtbar=250.0E-9/refarea, |
|
# kltbar=0.0E-9/refarea, |
|
# ihbar=18.0E-9/refarea, |
|
# leakbar=8.0E-9/refarea, |
|
# ) |
|
# self.channelMap = { |
|
# 'axon': {'nav11': 0.0, 'klt': 0., 'kht': self.gBar.khtbar, |
|
# 'ihvcn': 0., 'leak': self.gBar.leakbar / 4.}, |
|
# 'hillock': {'nav11': self.gBar.nabar, 'klt': 0., 'kht': self.gBar.khtbar, |
|
# 'ihvcn': 0., |
|
# 'leak': self.gBar.leakbar, }, |
|
# 'initseg': {'nav11': self.gBar.nabar, 'klt': 0., 'kht': self.gBar.khtbar, |
|
# 'ihvcn': self.gBar.ihbar / 2., |
|
# 'leak': self.gBar.leakbar, }, |
|
# 'soma': {'nav11': self.gBar.nabar, 'klt': self.gBar.kltbar, |
|
# 'kht': self.gBar.khtbar, 'ihvcn': self.gBar.ihbar, |
|
# 'leak': self.gBar.leakbar, }, |
|
# 'dend': {'nav11': self.gBar.nabar, 'klt': 0., 'kht': self.gBar.khtbar * 0.5, |
|
# 'ihvcn': self.gBar.ihbar / 3., 'leak': self.gBar.leakbar * 0.5, }, |
|
# 'apic': {'nav11': 0.0, 'klt': 0., 'kht': self.gBar.khtbar * 0.2, |
|
# 'ihvcn': self.gBar.ihbar / 4., |
|
# 'leak': self.gBar.leakbar * 0.2, }, |
|
# } |
|
# self.irange = np.linspace(-0.5, 0.5, 9) |
|
# self.distMap = {'dend': {'klt': {'gradient': 'linear', 'gminf': 0., 'lambda': 100.}, |
|
# 'kht': {'gradient': 'linear', 'gminf': 0., 'lambda': 100.}, |
|
# 'nav11': {'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.}, |
|
# 'nav11': {'gradient': 'exp', 'gminf': 0., 'lambda': 100.}}, # gradients are: flat, linear, exponential |
|
# } |
|
# |
|
# elif modelType == 'XM13PasDend': |
|
# # bushy form Xie and Manis, 2013, based on Cao and Oertel mouse conductances |
|
# # passive dendritestotcap = 26.0E-12 # uF/cm2 |
|
# totcap = 26.0E-12 # uF/cm2 |
|
# refarea = totcap / self.c_m # see above for units |
|
# self._valid_temperatures = (34.,) |
|
# if self.status['temperature'] is None: |
|
# self.set_temperature(34.) |
|
# self.gBar = Params(nabar=1000.0E-9/refarea, |
|
# khtbar=150.0E-9/refarea, |
|
# kltbar=0.0E-9/refarea, |
|
# ihbar=0.5E-9/refarea, |
|
# leakbar=2.0E-9/refarea, |
|
# ) |
|
# self.channelMap = { |
|
# 'axon': {'nav11': self.gBar.nabar*0, 'klt': self.gBar.kltbar * 0.25, 'kht': self.gBar.khtbar, 'ihvcn': 0., |
|
# 'leak': self.gBar.leakbar * 0.25}, |
|
# 'hillock': {'nav11': self.gBar.nabar, 'klt': self.gBar.kltbar, 'kht': self.gBar.khtbar, 'ihvcn': 0., |
|
# 'leak': self.gBar.leakbar, }, |
|
# 'initseg': {'nav11': self.gBar.nabar*3, 'klt': self.gBar.kltbar*2, 'kht': self.gBar.khtbar*2, |
|
# 'ihvcn': self.gBar.ihbar * 0.5, 'leak': self.gBar.leakbar, }, |
|
# 'soma': {'nav11': self.gBar.nabar, 'klt': self.gBar.kltbar, 'kht': self.gBar.khtbar, |
|
# 'ihvcn': self.gBar.ihbar, 'leak': self.gBar.leakbar, }, |
|
# 'dend': {'nav11': self.gBar.nabar * 0.0, 'klt': self.gBar.kltbar*0 , 'kht': self.gBar.khtbar*0, |
|
# 'ihvcn': self.gBar.ihbar*0, 'leak': self.gBar.leakbar*0.5, }, |
|
# 'apic': {'nav11': self.gBar.nabar * 0.0, 'klt': self.gBar.kltbar * 0, 'kht': self.gBar.khtbar * 0., |
|
# 'ihvcn': self.gBar.ihbar *0., 'leak': self.gBar.leakbar * 0.25, }, |
|
# } |
|
# self.irange = np.linspace(-1, 1, 21) |
|
def get_distancemap(self): |
|
return { |
|
"dend": { |
|
"klt": {"gradient": "linear", "gminf": 0.0, "lambda": 200.0}, |
|
"kht": {"gradient": "llinear", "gminf": 0.0, "lambda": 200.0}, |
|
"nav11": {"gradient": "linear", "gminf": 0.0, "lambda": 200.0}, |
|
}, # 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 |
|
} |
|
# 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, 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. |
|
|
|
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 * sf |
|
soma.ena = self.e_na |
|
if self.debug: |
|
print("jsrna gbar: ", soma().jsrna.gbar) |
|
elif nach == "nav11": |
|
soma().nav11.gbar = gnabar |
|
soma.ena = self.e_na |
|
soma().nav11.vsna = 4.3 |
|
if self.debug: |
|
print("tstellate using inva11") |
|
print("nav11 gbar: ", soma().nav11.gbar) |
|
print("nav11 vsna: ", soma().nav11.vsna) |
|
elif nach == "na": |
|
soma().nacn.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( |
|
"tstellate setting Na channels: channel %s not known" % nach |
|
) |
|
|
|
def add_axon(self): |
|
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 models. |
|
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 = 200 # length of the dendrite (not tapered) |
|
dendrites[i].diam = 1.5 # 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 TStellateNav11(TStellate): |
|
""" |
|
VCN T-stellate cell (Mouse) from Xie and Manis, 2013. |
|
Using nav11 sodium channel model. |
|
|
|
""" |
|
|
|
def __init__( |
|
self, |
|
morphology=None, |
|
decorator=None, |
|
nach="nav11", |
|
ttx=False, |
|
species="mouse", |
|
modelType=None, |
|
debug=False, |
|
): |
|
""" |
|
Initialize a planar stellate (T-stellate) cell as a point model, using the default parameters for |
|
mouse from Xie and Manis, 2013. |
|
Modifications to the cell can be made by calling methods below. |
|
Changing "species": This routine only supports "mouse" |
|
*Note:* in the original model, the temperature scaling applied only to the rate constants, and not |
|
to the conductance. Therefore, the conductances here need to be adjusted to compensate for the |
|
way the mechanisms are currently implemented (so that they scale correctly to the values |
|
used in Xie and Manis, 2013). This is done by setting q10g (the q10 for conductances) to 1 |
|
before setting up the rest of the model parameters. For those conducantances in which a Q10 for |
|
conductance is implemented, the value is typically 2. |
|
|
|
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: 'nav11') |
|
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 'mouse') |
|
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(TStellateNav11, self).__init__() |
|
if modelType == None: |
|
modelType = "XM13" |
|
self.status = { |
|
"soma": True, |
|
"axon": False, |
|
"dendrites": False, |
|
"pumps": False, |
|
"na": nach, |
|
"species": species, |
|
"modelType": modelType, |
|
"ttx": ttx, |
|
"name": "TStellate", |
|
"morphology": morphology, |
|
"decorator": decorator, |
|
} |
|
|
|
self.i_test_range = (-1, 1.0, 0.05) |
|
|
|
if morphology is None: |
|
""" |
|
instantiate a basic soma-only ("point") model |
|
""" |
|
print( |
|
"<< TStellate Xie&Manis 2013 model: Creating point cell, type={:s} >>".format( |
|
modelType |
|
) |
|
) |
|
soma = h.Section( |
|
name="TStellate_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("<< TStellate Xie&Manis 2013 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.soma.ek = self.e_k |
|
self.soma.ena = self.e_na |
|
self.soma().ihvcn.eh = self.e_h |
|
self.soma().leak.erev = self.e_leak |
|
self.soma().cm = 1.0 |
|
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.get_mechs(self.soma) |
|
self.cell_initialize(vrange=self.vrange) |
|
# self.print_mechs(self.soma) |
|
if self.debug: |
|
print("<< T-stellate: Xie&Manis 2013 cell model created >>") |
|
|
|
def species_scaling(self, species="mouse", modelType="I-c", 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 species == "mouse" and modelType == "XM13": |
|
# use conductance levels from Cao et al., J. Neurophys., 2007. |
|
# original temp for model: 32 C |
|
print("Mouse Tstellate cell, Xie and Manis, 2013") |
|
self.set_soma_size_from_Cm(25.0) |
|
self.adjust_na_chans(soma, gbar=800.0) # inav11 does not scale conductance |
|
self.e_k = -84.0 |
|
self.e_na = 50.0 |
|
soma.ek = self.e_k |
|
soma.ena = self.e_na |
|
soma().kht.gbar = nstomho(250.0, self.somaarea) |
|
soma().ka.gbar = nstomho(0.0, self.somaarea) |
|
soma().ihvcn.gbar = nstomho(18.0, self.somaarea) |
|
soma().ihvcn.eh = -43 # Rodrigues and Oertel, 2006 |
|
soma().leak.gbar = nstomho(8.0, self.somaarea) |
|
soma().leak.erev = -65.0 |
|
|
|
else: |
|
raise ValueError( |
|
"Species %s or species-type %s is not recognized for T-stellate XM13 cells" |
|
% (species, type) |
|
) |
|
|
|
self.status["species"] = species |
|
self.status["modelType"] = modelType |
|
# self.cell_initialize(showinfo=False) |
|
# if not silent: |
|
# print 'set cell as: ', species |
|
# print ' with Vm rest = %f' % self.vm0 |
|
|
|
def channel_manager(self, modelType="XM13"): |
|
""" |
|
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, called from it's private |
|
_biophys function) to decorate the cell membrane with channels. |
|
|
|
Parameters |
|
---------- |
|
modelType : string (default: 'XM13') |
|
A string that defines the type of the model. Currently, 3 types are implemented: |
|
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 == "XM13": |
|
totcap = ( |
|
25.0e-12 |
|
) # Base model from Xie and Manis, 2013 for type I stellate cell |
|
refarea = totcap / self.c_m # see above for units |
|
self.gBar = Params( |
|
nabar=800.0e-9 / refarea, |
|
khtbar=250.0e-9 / refarea, |
|
kltbar=0.0e-9 / refarea, |
|
ihbar=18.0e-9 / refarea, |
|
leakbar=8.0e-9 / refarea, |
|
) |
|
self.channelMap = { |
|
"axon": { |
|
"nav11": 0.0, |
|
"klt": 0.0, |
|
"kht": self.gBar.khtbar, |
|
"ihvcn": 0.0, |
|
"leak": self.gBar.leakbar / 4.0, |
|
}, |
|
"hillock": { |
|
"nav11": self.gBar.nabar, |
|
"klt": 0.0, |
|
"kht": self.gBar.khtbar, |
|
"ihvcn": 0.0, |
|
"leak": self.gBar.leakbar, |
|
}, |
|
"initseg": { |
|
"nav11": self.gBar.nabar, |
|
"klt": 0.0, |
|
"kht": self.gBar.khtbar, |
|
"ihvcn": self.gBar.ihbar / 2.0, |
|
"leak": self.gBar.leakbar, |
|
}, |
|
"soma": { |
|
"nav11": self.gBar.nabar, |
|
"klt": self.gBar.kltbar, |
|
"kht": self.gBar.khtbar, |
|
"ihvcn": self.gBar.ihbar, |
|
"leak": self.gBar.leakbar, |
|
}, |
|
"dend": { |
|
"nav11": self.gBar.nabar, |
|
"klt": 0.0, |
|
"kht": self.gBar.khtbar * 0.5, |
|
"ihvcn": self.gBar.ihbar / 3.0, |
|
"leak": self.gBar.leakbar * 0.5, |
|
}, |
|
} |
|
self.irange = np.linspace(-1.0, 1.0, 21) |
|
self.distMap = { |
|
"dend": { |
|
"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": 100.0}, |
|
} # linear with distance, gminf (factor) is multiplied by gbar |
|
} |
|
|
|
elif modelType == "XM13PasDend": |
|
# bushy form Xie and Manis, 2013, based on Cao and Oertel mouse conductances |
|
# passive dendrites |
|
totcap = 26.0e-12 # uF/cm2 |
|
refarea = totcap / self.c_m # see above for units |
|
self.gBar = Params( |
|
nabar=1000.0e-9 / refarea, |
|
khtbar=150.0e-9 / refarea, |
|
kltbar=0.0e-9 / refarea, |
|
ihbar=0.5e-9 / refarea, |
|
leakbar=2.0e-9 / refarea, |
|
) |
|
self.channelMap = { |
|
"axon": { |
|
"nav11": self.gBar.nabar * 0, |
|
"klt": self.gBar.kltbar * 0.25, |
|
"kht": self.gBar.khtbar, |
|
"ihvcn": 0.0, |
|
"leak": self.gBar.leakbar * 0.25, |
|
}, |
|
"hillock": { |
|
"nav11": self.gBar.nabar, |
|
"klt": self.gBar.kltbar, |
|
"kht": self.gBar.khtbar, |
|
"ihvcn": 0.0, |
|
"leak": self.gBar.leakbar, |
|
}, |
|
"initseg": { |
|
"nav11": self.gBar.nabar * 3, |
|
"klt": self.gBar.kltbar * 2, |
|
"kht": self.gBar.khtbar * 2, |
|
"ihvcn": self.gBar.ihbar * 0.5, |
|
"leak": self.gBar.leakbar, |
|
}, |
|
"soma": { |
|
"nav11": self.gBar.nabar, |
|
"klt": self.gBar.kltbar, |
|
"kht": self.gBar.khtbar, |
|
"ihvcn": self.gBar.ihbar, |
|
"leak": self.gBar.leakbar, |
|
}, |
|
"dend": { |
|
"nav11": self.gBar.nabar * 0.0, |
|
"klt": self.gBar.kltbar * 0, |
|
"kht": self.gBar.khtbar * 0, |
|
"ihvcn": self.gBar.ihbar * 0, |
|
"leak": self.gBar.leakbar * 0.5, |
|
}, |
|
} |
|
self.irange = np.linspace(-1, 1, 21) |
|
self.distMap = { |
|
"dend": { |
|
"klt": {"gradient": "linear", "gminf": 0.0, "lambda": 200.0}, |
|
"kht": {"gradient": "llinear", "gminf": 0.0, "lambda": 200.0}, |
|
"nav11": {"gradient": "linear", "gminf": 0.0, "lambda": 200.0}, |
|
} # linear with distance, gminf (factor) is multiplied by gbar |
|
} |
|
else: |
|
raise ValueError("model type %s is not implemented" % modelType) |
|
|
|
def adjust_na_chans(self, soma, gbar=800.0): |
|
""" |
|
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: 800.) |
|
The "maximal" conductance to be set in the model. |
|
|
|
Returns |
|
------- |
|
Nothing |
|
""" |
|
if self.status["ttx"]: |
|
gnabar = 0.0 |
|
else: |
|
gnabar = nstomho(gbar, self.somaarea) |
|
nach = self.status["na"] |
|
if nach == "nav11": |
|
soma().nav11.gbar = gnabar |
|
soma.ena = self.e_na |
|
soma().nav11.vsna = 4.3 |
|
if self.debug: |
|
print("tstellate using inva11") |
|
else: |
|
raise ValueError( |
|
"tstellate setting Na channels only supporting nav11: channel %s not known" |
|
% nach |
|
)
|
|
|