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967 lines
44 KiB
967 lines
44 KiB
from __future__ import print_function |
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from neuron import h |
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from collections import OrderedDict |
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from .cell import Cell |
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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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import numpy as np |
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from .. import data |
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import pprint |
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|
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pp = pprint.PrettyPrinter(indent=4, width=60) |
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|
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__all__ = ["Bushy", "BushyRothman"] |
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|
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|
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class Bushy(Cell): |
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|
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type = "bushy" |
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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": |
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return BushyRothman(**kwds) |
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else: |
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raise ValueError("Bushy model %s is unknown", model) |
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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 designed to pass the unit test (loc=0.5) |
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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: dictionary of options. |
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Two are currently handled: |
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postsite : 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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|
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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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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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self.NMDAR_vshift = 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_vshift", |
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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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|
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# original values (now in synapses.py): |
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# self.AMPA_gmax = 3.314707700918133*1e3 # factor of 1e3 scales to pS (.mod mechanisms) from nS. |
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# self.NMDA_gmax = 0.4531929783503451*1e3 |
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if "AMPAScale" in kwds: # normally, this should not be done! |
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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"] # and NMDA... |
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return self.make_glu_psd( |
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post_sec, |
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terminal, |
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self.AMPAR_gmax, |
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self.NMDAR_gmax, |
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loc=loc, |
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nmda_vshift=self.NMDAR_vshift, |
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) |
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elif terminal.cell.type == "dstellate": |
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return self.make_gly_psd(post_sec, terminal, psdtype="glyslow", loc=loc) |
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elif terminal.cell.type == "tuberculoventral": |
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return self.make_gly_psd(post_sec, terminal, psdtype="glyslow", loc=loc) |
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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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def make_terminal(self, post_cell, term_type, **kwds): |
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if term_type == "simple": |
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return synapses.SimpleTerminal(self.soma, post_cell, **kwds) |
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|
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elif term_type == "multisite": |
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if post_cell.type in ["mso"]: |
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nzones = data.get( |
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"bushy_synapse", |
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species=self.species, |
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post_type=post_cell.type, |
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field="n_rsites", |
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) |
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delay = data.get( |
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"bushy_synapse", |
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species=self.species, |
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post_type=post_cell.type, |
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field="delay", |
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) |
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else: |
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raise NotImplementedError( |
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"No knowledge as to how to connect Bushy cell to cell type %s" |
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% type(post_cell) |
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) |
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pre_sec = self.soma |
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return synapses.StochasticTerminal( |
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pre_sec, |
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post_cell, |
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nzones=nzones, |
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spike_source=self.spike_source, |
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delay=delay, |
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**kwds, |
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) |
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else: |
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raise ValueError("Unsupported terminal type %s" % term_type) |
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class BushyRothman(Bushy): |
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""" |
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VCN bushy cell models. |
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Rothman and Manis, 2003abc (Type II, Type II-I) |
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Xie and Manis, 2013 |
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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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species="guineapig", |
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modelType=None, |
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modelName=None, |
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debug=False, |
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temperature=None, |
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): |
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""" |
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Create a bushy cell, using the default parameters for guinea pig from |
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R&M2003, as a type II cell. |
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Additional modifications to the cell can be made by calling methods below. |
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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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Name of a .hoc file representing the morphology. This file is used to constructe |
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an electrotonic (cable) model. |
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If None (default), then a "point" (really, single cylinder) 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 is 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 channel is set to 'nacn' (R&M03) |
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|
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temperature : float (default: 22) |
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temperature to run the cell at. |
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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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This flag duplicates the effects of tetrodotoxin in the model. Currently, the flag is not implemented. |
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|
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species: string (default 'guineapig') |
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species defines the pattern of ion channel densities that will be inserted, according to |
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prior measurements in various species. Note that |
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if a decorator function is specified, this argument is ignored as the decorator will |
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specify the channel density. |
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|
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modelName: string (default: None) |
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modelName specifies the source conductance pattern (RM03, XM13, etc). |
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modelName is passed to the decorator, or to species_scaling to adjust point (single cylinder) models. |
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|
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modelType: string (default: None) |
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modelType specifies the subtype of the cell model that will be used (e.g., "II", "II-I", etc). |
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modelType is passed to the decorator, or to species_scaling to adjust point (single cylinder) models. |
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debug: boolean (default: False) |
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When True, 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(BushyRothman, self).__init__() |
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self.i_test_range = { |
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"pulse": (-1, 1, 0.05) |
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} # note that this might get reset with decorator according to channels |
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# Changing the default values will cause the unit tests to fail! |
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if modelType == None: |
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modelType = "II" |
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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 = "na" |
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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 is None: |
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nach = "na" |
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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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"hillock": False, |
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"initialsegment": False, |
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"myelinatedaxon": False, |
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"unmyelinatedaxon": 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": temperature, |
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} |
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|
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self.spike_threshold = -40 |
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self.vrange = [-70.0, -55.0] # set a default vrange for searching for rmp |
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if self.debug: |
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print( |
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"model type, model name, species: ", modelType, modelName, species, nach |
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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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|
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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("<< Bushy model: Creating point cell >>") |
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soma = h.Section( |
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name="Bushy_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( |
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"<< Bushy model: Creating cell with morphology from %s >>" |
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% morphology |
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) |
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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, does not use tables. |
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self.mechanisms = ["klt", "kht", "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.ena = self.e_na |
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self.soma.ek = self.e_k |
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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.c_m = 0.9 |
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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, with tables. |
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self.decorate() |
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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 debug: |
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print(" << Created cell >>") |
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|
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def get_cellpars(self, dataset, species="guineapig", modelType="II"): |
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""" |
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Read data for ion channels and cell parameters from the tables |
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""" |
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# cell_type = self.map_celltype(cell_type) |
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# print('getcellpars: dataset, species, mmodeltype: ', dataset, species, modelType) |
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# print('model name: ', self.status['modelName']) |
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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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# print('pars cell/chtype: ') |
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if self.debug: |
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pars.show() |
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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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"klt_gbar", |
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"ih_gbar", |
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"leak_gbar", |
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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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"klt_gbar", |
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"ihvcn_gbar", |
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"leak_gbar", |
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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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"klt_gbar", |
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"ihvcn_gbar", |
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"leak_gbar", |
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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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|
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return pars |
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|
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def species_scaling(self, species="guineapig", modelType="II", silent=True): |
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""" |
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This is called for POINT CELLS ONLY |
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Adjust all of the conductances and the cell size according to the species requested. |
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This scaling should be used ONLY for point models, as no other compartments |
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are scaled. |
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|
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This scaling routine also sets the temperature for the model to a default value. Some models |
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can be run at multiple temperatures, and so a default from one of the temperatures is used. |
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The calling cell.set_temperature(newtemp) will change the conductances and reinitialize |
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the cell to the new temperature settings. |
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|
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Parameters |
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---------- |
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species : string (default: 'guineapig') |
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name of the species to use for scaling the conductances in the base point model |
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Must be one of mouse, cat, guineapig |
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|
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modelType: string (default: 'II') |
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definition of model type from RM03 models, type II or type II-I |
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|
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silent : boolean (default: True) |
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run silently (True) or verbosely (False) |
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|
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""" |
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# print '\nSpecies scaling: %s %s' % (species, type) |
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knownspecies = ["mouse", "guineapig", "cat"] |
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|
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soma = self.soma |
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# cellType = self.map_celltype(modelType) |
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|
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if species == "mouse": |
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# use conductance levels determined from Cao et al., J. Neurophys., 2007. as |
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# model description in Xie and Manis 2013. Note that |
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# conductances were not scaled for temperature (rates were) |
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# so here we reset the default Q10's for conductance (g) to 1.0 |
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if modelType not in ["II", "II-I"]: |
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raise ValueError( |
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"\nModel type %s is not implemented for mouse bushy cells" |
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% modelType |
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) |
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if self.debug: |
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print( |
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" Setting conductances for mouse bushy cell (%s), Xie and Manis, 2013" |
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% modelType |
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) |
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if modelname == "XM13": |
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dataset = "XM13_channels" |
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elif modelname == "XM13nacncoop": |
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dataset = "XM13_channels_nacncoop" |
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elif modelname.startswith("mGBC"): |
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dataset = "mGBC_channels" |
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else: |
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raise ValueError( |
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f"ModelName {modelname:s} not recognized for mouse bushy cells" |
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) |
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self.vrange = [-68.0, -55.0] # set a default vrange for searching for rmp |
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self.i_test_range = {"pulse": (-1.0, 1.0, 0.05)} |
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self._valid_temperatures = (34.0,) |
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if self.status["temperature"] is None: |
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self.status["temperature"] = 34.0 |
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|
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pars = self.get_cellpars(dataset, species=species, modelType=modelType) |
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self.set_soma_size_from_Cm(pars.cap) |
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self.status["na"] = pars.natype |
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self.adjust_na_chans(soma, sf=1.0) |
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soma().kht.gbar = nstomho(pars.kht_gbar, self.somaarea) |
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soma().klt.gbar = nstomho(pars.klt_gbar, self.somaarea) |
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soma().ihvcn.gbar = nstomho(pars.ihvcn_gbar, self.somaarea) |
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soma().leak.gbar = nstomho(pars.leak_gbar, self.somaarea) |
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self.axonsf = 0.57 |
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|
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elif species == "guineapig": |
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if self.debug: |
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print( |
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" Setting conductances for guinea pig %s bushy cell, Rothman and Manis, 2003" |
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% modelType |
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) |
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self._valid_temperatures = (22.0, 38.0) |
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if self.status["temperature"] is None: |
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self.status["temperature"] = 22.0 |
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self.i_test_range = {"pulse": (-0.4, 0.4, 0.02)} |
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sf = 1.0 |
|
if ( |
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self.status["temperature"] == 38.0 |
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): # adjust for 2003 model conductance levels at 38 |
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sf = 2 # Q10 of 2, 22->38C. (p3106, R&M2003c) |
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# note that kinetics are scaled in the mod file. |
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dataset = "RM03_channels" |
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pars = self.get_cellpars(dataset, species=species, modelType=modelType) |
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self.set_soma_size_from_Cm(pars.cap) |
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self.status["na"] = pars.natype |
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self.adjust_na_chans(soma, sf=sf) |
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soma().kht.gbar = nstomho(pars.kht_gbar, self.somaarea) |
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soma().klt.gbar = nstomho(pars.klt_gbar, self.somaarea) |
|
soma().ihvcn.gbar = nstomho(pars.ih_gbar, self.somaarea) |
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soma().leak.gbar = nstomho(pars.leak_gbar, self.somaarea) |
|
|
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self.axonsf = 0.57 |
|
|
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else: |
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errmsg = ( |
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'Species "%s" or model type "%s" is not recognized for Bushy cells.' |
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% (species, modelType) |
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) |
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errmsg += "\n Valid species are: \n" |
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for s in knownspecies: |
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errmsg += " %s\n" % s |
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errmsg += "-" * 40 |
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raise ValueError(errmsg) |
|
|
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self.status["species"] = species |
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self.status["modelType"] = modelType |
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self.check_temperature() |
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# self.cell_initialize(vrange=self.vrange) # no need to do this just yet. |
|
if not silent: |
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print(" set cell as: ", species) |
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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. |
|
# |
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# 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. |
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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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# Notes |
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# ----- |
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# This routine defines the following variables for the class: |
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# |
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# * conductances (gBar) |
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# * a channelMap (dictonary of channel densities in defined anatomical compartments) |
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# * a current injection range for IV's (used for testing) |
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# * a distance map, which defines how each conductance in a selected compartment |
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# changes with distance from the soma. The current implementation includes both |
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# linear and exponential gradients, |
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# the minimum conductance at the end of the gradient, and the space constant or |
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# slope for the gradient. |
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# |
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# """ |
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# |
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# |
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# dataset = '%s_channels' % modelType |
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# decorationmap = dataset + '_compartments' |
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# # print('dataset: {0:s} decorationmap: {1:s}'.format(dataset, decorationmap)) |
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# cellpars = self.get_cellpars(dataset, species=self.status['species'], celltype=cell_type) |
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# refarea = 1e-3*cellpars.cap / self.c_m |
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# |
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# table = data.get_table_info(dataset) |
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# chscale = data.get_table_info(decorationmap) |
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# pars = {} |
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# # retrive the conductances from the data set |
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# for g in table['field']: |
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# x = data.get(dataset, species=self.status['species'], cell_type=cell_type, |
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# field=g) |
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# if not isinstance(x, float): |
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# continue |
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# if '_gbar' in g: |
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# pars[g] = x/refarea |
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# else: |
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# pars[g] = x |
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# |
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# self.channelMap = OrderedDict() |
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# for c in chscale['compartment']: |
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# self.channelMap[c] = {} |
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# for g in pars.keys(): |
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# if g not in chscale['parameter']: |
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# # print ('Parameter %s not found in chscale parameters!' % g) |
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# continue |
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# scale = data.get(decorationmap, species=self.status['species'], cell_type=cell_type, |
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# compartment=c, parameter=g) |
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# if '_gbar' in g: |
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# self.channelMap[c][g] = pars[g]*scale |
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# else: |
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# self.channelMap[c][g] = pars[g] |
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# |
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# self.irange = np.linspace(-0.6, 1, 9) |
|
|
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def get_distancemap(self): |
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return { |
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"dend": { |
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"klt": {"gradient": "exp", "gminf": 0.0, "lambda": 50.0}, |
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"kht": {"gradient": "exp", "gminf": 0.0, "lambda": 50.0}, |
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"nav11": {"gradient": "exp", "gminf": 0.0, "lambda": 50.0}, |
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}, # linear with distance, gminf (factor) is multiplied by gbar |
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"dendrite": { |
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"klt": {"gradient": "linear", "gminf": 0.0, "lambda": 100.0}, |
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"kht": {"gradient": "linear", "gminf": 0.0, "lambda": 100.0}, |
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"nav11": {"gradient": "linear", "gminf": 0.0, "lambda": 100.0}, |
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}, # linear with distance, gminf (factor) is multiplied by gbar |
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"apic": { |
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"klt": {"gradient": "linear", "gminf": 0.0, "lambda": 100.0}, |
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"kht": {"gradient": "linear", "gminf": 0.0, "lambda": 100.0}, |
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"nav11": {"gradient": "exp", "gminf": 0.0, "lambda": 200.0}, |
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}, # gradients are: flat, linear, exponential |
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} |
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# self.check_temperature() |
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# return |
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# |
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|
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# |
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# |
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# if modelType == 'RM03': |
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# # |
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# # Create a model based on the Rothman and Manis 2003 conductance set from guinea pig |
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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._valid_temperatures = (22., 38.) |
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# sf = 1.0 |
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# if self.status['temperature'] == None: |
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# self.status['temperature'] = 22. |
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# if self.status['temperature'] == 38: |
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# sf = 3.03 |
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# dataset = 'RM03_channels' |
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# pars = self.get_cellpars(dataset, species=self.status['species'], celltype='bushy-II') |
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# refarea = 1e-3*pars.cap / self.c_m |
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# self.gBar = Params(nabar=sf*pars.soma_na_gbar/refarea, # 1000.0E-9/refarea, |
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# khtbar=sf*pars.soma_kht_gbar/refarea, |
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# kltbar=sf*pars.soma_klt_gbar/refarea, |
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# ihbar=sf*pars.soma_ih_gbar/refarea, |
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# leakbar=sf*pars.soma_leak_gbar/refarea, |
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# ) |
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# print 'RM03 gbar:\n', self.gBar.show() |
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# |
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# self.channelMap = { |
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# 'axon': {'nacn': self.gBar.nabar, 'klt': self.gBar.kltbar, 'kht': self.gBar.khtbar, 'ihvcn': 0., |
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# 'leak': self.gBar.leakbar / 2.}, |
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# 'hillock': {'nacn': self.gBar.nabar, 'klt': self.gBar.kltbar, 'kht': self.gBar.khtbar, 'ihvcn': 0., |
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# 'leak': self.gBar.leakbar, }, |
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# 'initseg': {'nacn': self.gBar.nabar, 'klt': self.gBar.kltbar, 'kht': self.gBar.khtbar, |
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# 'ihvcn': self.gBar.ihbar / 2., 'leak': self.gBar.leakbar, }, |
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# 'soma': {'nacn': self.gBar.nabar, 'klt': self.gBar.kltbar, 'kht': self.gBar.khtbar, |
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# 'ihvcn': self.gBar.ihbar, 'leak': self.gBar.leakbar, }, |
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# 'dend': {'nacn': self.gBar.nabar, 'klt': self.gBar.kltbar * 0.5, 'kht': self.gBar.khtbar * 0.5, |
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# 'ihvcn': self.gBar.ihbar / 3., 'leak': self.gBar.leakbar * 0.5, }, |
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# 'apic': {'nacn': self.gBar.nabar, 'klt': self.gBar.kltbar * 0.2, 'kht': self.gBar.khtbar * 0.2, |
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# 'ihvcn': self.gBar.ihbar / 4., 'leak': self.gBar.leakbar * 0.2, }, |
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# } |
|
# # self.irange = np.linspace(-1., 1., 21) |
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# self.distMap = {'dend': {'klt': {'gradient': 'linear', 'gminf': 0., 'lambda': 100.}, |
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# 'kht': {'gradient': 'linear', 'gminf': 0., 'lambda': 100.}, |
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# 'nacn': {'gradient': 'exp', 'gminf': 0., 'lambda': 100.}}, # linear with distance, gminf (factor) is multiplied by gbar |
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# 'apic': {'klt': {'gradient': 'linear', 'gminf': 0., 'lambda': 100.}, |
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# 'kht': {'gradient': 'linear', 'gminf': 0., 'lambda': 100.}, |
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# 'nacn': {'gradient': 'exp', 'gminf': 0., 'lambda': 100.}}, # gradients are: flat, linear, exponential |
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# } |
|
# |
|
# elif modelType == 'XM13': |
|
# # |
|
# # Create a model for a mouse bushy cell from Xie and Manis, 2013 |
|
# # based on Cao and Oertel mouse conductance values |
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# # 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') |
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# refarea = 1e-3*pars.cap / self.c_m |
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# # self.gBar = Params(nabar=pars.soma_nav11_gbar/refarea, # 1000.0E-9/refarea, |
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# # khtbar=pars.soma_kht_gbar/refarea, |
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# # kltbar=pars.soma_klt_gbar/refarea, |
|
# # ihbar=pars.soma_ihvcn_gbar/refarea, |
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# # 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., |
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# # 'leak': self.gBar.leakbar * 0.25}, |
|
# # 'hillock': {'nav11': self.gBar.nabar*2, 'klt': self.gBar.kltbar, 'kht': self.gBar.khtbar*2.0, 'ihvcn': 0., |
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# # 'leak': self.gBar.leakbar, }, |
|
# # 'initialsegment': {'nav11': self.gBar.nabar*3.0, 'klt': self.gBar.kltbar*1, 'kht': self.gBar.khtbar*2, |
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# # 'ihvcn': self.gBar.ihbar * 0.5, 'leak': self.gBar.leakbar, }, |
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# # 'soma': {'nav11': self.gBar.nabar*1.0, 'klt': self.gBar.kltbar, 'kht': self.gBar.khtbar, |
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# # 'ihvcn': self.gBar.ihbar, 'leak': self.gBar.leakbar, }, |
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# # 'dend': {'nav11': self.gBar.nabar * 0.25, 'klt': self.gBar.kltbar *0.5, 'kht': self.gBar.khtbar *0.5, |
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# # 'ihvcn': self.gBar.ihbar *0.5, 'leak': self.gBar.leakbar * 0.5, }, |
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# # 'primarydendrite': {'nav11': self.gBar.nabar * 0.25, 'klt': self.gBar.kltbar *0.5, 'kht': self.gBar.khtbar *0.5, |
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# # 'ihvcn': self.gBar.ihbar *0.5, 'leak': self.gBar.leakbar * 0.5, }, |
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# # 'apic': {'nav11': self.gBar.nabar * 0.25, 'klt': self.gBar.kltbar * 0.25, 'kht': self.gBar.khtbar * 0.25, |
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# # '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. |
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""" |
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if self.debug: |
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print("Adding dendrite to Bushy model") |
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section = h.Section |
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primarydendrite = section(cell=self.soma) |
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primarydendrite.connect(self.soma) |
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primarydendrite.nseg = 10 |
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primarydendrite.L = 100.0 |
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primarydendrite.diam = 2.5 |
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primarydendrite.insert("klt") |
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primarydendrite.insert("ihvcn") |
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primarydendrite().klt.gbar = self.soma().klt.gbar / 2.0 |
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primarydendrite().ihvcn.gbar = self.soma().ihvcn.gbar / 2.0 |
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|
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primarydendrite.cm = self.c_m |
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primarydendrite.Ra = self.R_a |
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nsecd = range(0, 5) |
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secondarydendrite = [] |
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for ibd in nsecd: |
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secondarydendrite.append(section(cell=self.soma)) |
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for ibd in nsecd: |
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secondarydendrite[ibd].connect(primarydendrite) |
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secondarydendrite[ibd].diam = 1.0 |
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secondarydendrite[ibd].L = 15.0 |
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secondarydendrite[ibd].cm = self.c_m |
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secondarydendrite[ibd].Ra = self.R_a |
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self.primarydendrite = primarydendrite |
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self.secondarydendrite = secondarydendrite |
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self.status["dendrite"] = True |
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if self.debug: |
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print("Bushy: added dendrites") |
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h.topology() |
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self.add_section(maindend, "primarydendrite") |
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self.add_section(secdend, "secondarydendrite")
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