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