def __init__(self, **parameters):
# define ion channel parameters
leak = Mechanism('pas', e=-65, g=parameters['g_leak'])
hh = Mechanism('hh',
gl=parameters['g_leak'],
el=-65,
gnabar=parameters['gnabar'],
gkbar=parameters['gkbar'])
# create cable sections
self.soma = Section(L=30, diam=30, mechanisms=[hh])
self.apical = Section(L=600,
diam=2,
nseg=5,
mechanisms=[leak],
parent=self.soma,
connect_to=1)
self.basilar = Section(L=600,
diam=2,
nseg=5,
mechanisms=[leak],
parent=self.soma)
self.axon = Section(L=1000, diam=1, nseg=37, mechanisms=[hh])
# synaptic input
self.apical.add_synapse('ampa', 'Exp2Syn', e=0.0, tau1=0.1, tau2=5.0)
# needed for PyNN
self.source_section = self.soma
self.source = self.soma(0.5)._ref_v
self.parameter_names = ('g_leak', 'gnabar', 'gkbar')
self.traces = {}
self.recording_time = False
def build_sections(self, Ra=160, cm=1, axon=True, soma=True, *args, **kwargs):
# membrane properties across the cell
self.Ra = Ra # Ohm cm cytoplasmic resistivity
self.cm = cm # uF/cm^2 specific membrane capacitance
# geometry and topology
if soma:
if 'soma_L' in kwargs:
soma_l = kwargs['soma_L']
else:
soma_l = 15
if 'soma_diam' in kwargs:
soma_diam = kwargs['soma_diam']
else:
soma_diam = 15
self.soma = Section(L=soma_l, diam=soma_diam, Ra=self.Ra, cm=self.cm,
mechanisms=self.mechanisms)
self.soma.name = 'soma'
self.sections.append(self.soma)
if axon:
self.axon = Section(L=100, diam=0.2, Ra=self.Ra, cm=self.cm,
mechanisms=self.mechanisms,
parent=self.soma,
connection_point=PROXIMAL)
self.axon.name = 'axon'
self.sections.append(self.axon)
class SimpleNeuron(object):
def __init__(self, **parameters):
# define ion channel parameters
leak = Mechanism('pas', e=-65, g=parameters['g_leak'])
hh = Mechanism('hh', gl=parameters['g_leak'], el=-65,
gnabar=parameters['gnabar'], gkbar=parameters['gkbar'])
# create cable sections
self.soma = Section(L=30, diam=30, mechanisms=[hh])
self.apical = Section(L=600, diam=2, nseg=5, mechanisms=[leak], parent=self.soma,
connection_point=DISTAL)
self.basilar = Section(L=600, diam=2, nseg=5, mechanisms=[leak], parent=self.soma)
self.axon = Section(L=1000, diam=1, nseg=37, mechanisms=[hh])
# synaptic input
self.apical.add_synapse('ampa', 'Exp2Syn', e=0.0, tau1=0.1, tau2=5.0)
# needed for PyNN
self.source_section = self.soma
self.source = self.soma(0.5)._ref_v
self.parameter_names = ('g_leak', 'gnabar', 'gkbar')
self.traces = {}
self.recording_time = False
def _set_g_leak(self, value):
for sec in (self.apical, self.basilar):
for seg in sec:
seg.pas.g = value
for sec in (self.soma, self.axon):
for seg in sec:
seg.hh.gl = value
def _get_g_leak(self):
return self.apical(0.5).pas.g
g_leak = property(fget=_get_g_leak, fset=_set_g_leak)
def _set_gnabar(self, value):
for sec in (self.soma, self.axon):
for seg in sec:
seg.hh.gnabar = value
def _get_gnabar(self):
return self.soma(0.5).hh.gnabar
gnabar = property(fget=_get_gnabar, fset=_set_gnabar)
def _set_gkbar(self, value):
for sec in (self.soma, self.axon):
for seg in sec:
seg.hh.gkbar = value
def _get_gkbar(self):
return self.soma(0.5).hh.gkbar
gkbar = property(fget=_get_gkbar, fset=_set_gkbar)
def memb_init(self):
"""needed for PyNN"""
for sec in (self.soma, self.axon, self.apical, self.basilar):
for seg in sec:
seg.v = self.v_init
class SimpleNeuron(object):
def __init__(self, **parameters):
# define ion channel parameters
leak = Mechanism('pas', e=-65, g=parameters['g_leak'])
hh = Mechanism('hh', gl=parameters['g_leak'], el=-65,
gnabar=parameters['gnabar'], gkbar=parameters['gkbar'])
# create cable sections
self.soma = Section(L=30, diam=30, mechanisms=[hh])
self.apical = Section(L=600, diam=2, nseg=5, mechanisms=[leak], parent=self.soma,
connection_point=DISTAL)
self.basilar = Section(L=600, diam=2, nseg=5, mechanisms=[leak], parent=self.soma)
self.axon = Section(L=1000, diam=1, nseg=37, mechanisms=[hh])
# synaptic input
self.apical.add_synapse('ampa', 'Exp2Syn', e=0.0, tau1=0.1, tau2=5.0)
# needed for PyNN
self.source_section = self.soma
self.source = self.soma(0.5)._ref_v
self.parameter_names = ('g_leak', 'gnabar', 'gkbar')
self.traces = {}
self.recording_time = False
def _set_g_leak(self, value):
for sec in (self.apical, self.basilar):
for seg in sec:
seg.pas.g = value
for sec in (self.soma, self.axon):
for seg in sec:
seg.hh.gl = value
def _get_g_leak(self):
return self.apical(0.5).pas.g
g_leak = property(fget=_get_g_leak, fset=_set_g_leak)
def _set_gnabar(self, value):
for sec in (self.soma, self.axon):
for seg in sec:
seg.hh.gnabar = value
def _get_gnabar(self):
return self.soma(0.5).hh.gnabar
gnabar = property(fget=_get_gnabar, fset=_set_gnabar)
def _set_gkbar(self, value):
for sec in (self.soma, self.axon):
for seg in sec:
seg.hh.gkbar = value
def _get_gkbar(self):
return self.soma(0.5).hh.gkbar
gkbar = property(fget=_get_gkbar, fset=_set_gkbar)
def memb_init(self):
"""needed for PyNN"""
for sec in (self.soma, self.axon, self.apical, self.basilar):
for seg in sec:
seg.v = self.v_init
def __init__(self,
ion_type,
current_type,
L=20,
diam=20,
Ra=150,
g_pas=0.00003334):
self.ion_type = ion_type
self.current_type = current_type
leak = Mechanism('pas', e=-65, g=g_pas)
self.soma = Section(L, diam, Ra=Ra, mechanisms=[leak])
def build_sections(self, Ra=100, **kwargs):
super(SingleDend, self).build_sections(Ra=Ra, **kwargs)
self.dend.append(Section(L=self.L, diam=self.diam, nseg=self.nseg, Ra=self.Ra, cm=self.cm,
mechanisms=[self.pas],
parent=self.soma))
self.dend[0].name = 'dend_1'
self.sections += self.dend
def __init__(self, **parameters):
# Create single compartment Rubin and Terman GP cell section, i.e. soma section
self.soma = Section(L=parameters['L'],
diam=parameters['diam'],
nseg=parameters['nseg'],
Ra=parameters['Ra'],
cm=parameters['cm'],
mechanisms=[
Mechanism('myions'),
Mechanism('GPeA',
gnabar=0.04,
gkdrbar=0.0042,
gkcabar=0.1e-3,
gcatbar=6.7e-5,
kca=2,
gl=4e-5)
])
# Initialize ion concentrations
h("cai0_ca_ion = 5e-6 ")
h("cao0_ca_ion = 2")
h("ki0_k_ion = 105")
h("ko0_k_ion = 3")
h("nao0_na_ion = 108")
h("nai0_na_ion = 10")
# insert current source
self.stim = h.IClamp(0.5, sec=self.soma)
self.stim.delay = 0
self.stim.dur = 1e12
self.stim.amp = parameters['bias_current']
# Add DBS stimulation current to neuron model
self.DBS_stim = h.IClamp(0.5, sec=self.soma)
self.DBS_stim.delay = 0
self.DBS_stim.dur = 1e9
self.DBS_stim.amp = 0
# Append the DBS stimulation iclamps to global list
GV.GPe_stimulation_iclamps.append(self.DBS_stim)
# Add AMPA and GABAa synapses to the cell, i.e. add to the soma section
self.AMPA = h.AMPA_S(0.5, sec=self.soma)
self.GABAa = h.GABAa_S(0.5, sec=self.soma)
# needed for PyNN
self.source_section = self.soma
self.source = self.soma(0.5)._ref_v
self.rec = h.NetCon(self.source, None,
sec=self.soma) # Needed to clear the simulator
self.spike_times = h.Vector(0)
self.parameter_names = ('L', 'diam', 'nseg', 'Ra', 'cm',
'bias_current_density')
self.traces = {}
self.recording_time = False
def __init__(self, axNum=15, axon_mechanisms=list(), bouton_mechanisms=list(), all_ena=55, all_ek=-97):
self.axNum = axNum
#See comment below on measuring the total ion currents
#if add_charge:
# axon_mechanisms.append(Mechanism("charge_", tmin=charge_start, tmax=charge_end))
# bouton_mechanisms.append(Mechanism("charge_", tmin=charge_start, tmax=charge_end))
# Define axon
self.axons = [Section(nseg = 5 if i < axNum else 20,
L = 35 if i < axNum else 150,
diam = 0.8,
cm = 0.9/10,
mechanisms=axon_mechanisms)
for i in range(axNum+1)]
self.boutons = [Section(nseg=1,
L=8,
diam=8,
cm=0.9,
mechanisms=bouton_mechanisms)
for i in range(axNum)]
# Connectivity
for axon, bouton in zip(self.axons, self.boutons):
bouton.connect(axon) # connect bouton 0-end to preceeding axon 1-end
for bouton, axon in zip(self.boutons, self.axons[1:]):
axon.connect(bouton(1),0) # connect axon 0-end to preceeding bouton 1-end
# Someting for all
h.celsius = 37
h.Ra = 120
for section in h.allsec():
section.ena = all_ena
section.ek = all_ek
def __init__(self, **parameters):
# Create single compartment Otsuka STN cell section, i.e. soma section
self.soma = Section(L=parameters['L'],
diam=parameters['diam'],
nseg=parameters['nseg'],
Ra=parameters['Ra'],
cm=parameters['cm'],
mechanisms=[
Mechanism('myions'),
Mechanism('stn',
gnabar=49e-3,
gkdrbar=57e-3,
gkabar=5e-3,
gkcabar=1.0e-3,
gcalbar=15e-3,
gcatbar=5e-3,
kca=2,
gl=0.35e-3)
])
# Initialize ion concentrations
h("cai0_ca_ion = 5e-6 ")
h("cao0_ca_ion = 2")
h("ki0_k_ion = 105")
h("ko0_k_ion = 3")
h("nao0_na_ion = 108")
h("nai0_na_ion = 10")
# Add bias current to neuron model
self.stim = h.IClamp(0.5, sec=self.soma)
self.stim.delay = 0
self.stim.dur = 1e12
self.stim.amp = parameters['bias_current'] # bias current density (nA)
# Add AMPA and GABAa synapses to the cell, i.e. add to the soma section
self.AMPA = h.AMPA_S(0.5, sec=self.soma)
self.GABAa = h.GABAa_S(0.5, sec=self.soma)
# needed for PyNN
self.source_section = self.soma
self.source = self.soma(0.5)._ref_v
self.rec = h.NetCon(self.source, None,
sec=self.soma) # Needed to clear the simulator
self.spike_times = h.Vector(0)
self.parameter_names = ('L', 'diam', 'nseg', 'Ra', 'cm',
'bias_current')
self.traces = {}
self.recording_time = False
def __init__(self, **parameters):
# define ion channel parameters
leak = Mechanism("pas", e=-65, g=parameters["g_leak"])
hh = Mechanism("hh", gl=parameters["g_leak"], el=-65, gnabar=parameters["gnabar"], gkbar=parameters["gkbar"])
# create cable sections
self.soma = Section(L=30, diam=30, mechanisms=[hh])
self.apical = Section(L=600, diam=2, nseg=5, mechanisms=[leak], parent=self.soma, connection_point=DISTAL)
self.basilar = Section(L=600, diam=2, nseg=5, mechanisms=[leak], parent=self.soma)
self.axon = Section(L=1000, diam=1, nseg=37, mechanisms=[hh])
# synaptic input
self.apical.add_synapse("ampa", "Exp2Syn", e=0.0, tau1=0.1, tau2=5.0)
# needed for PyNN
self.source_section = self.soma
self.source = self.soma(0.5)._ref_v
self.parameter_names = ("g_leak", "gnabar", "gkbar")
self.traces = {}
self.recording_time = False
def __init__(self, **parameters):
# define ion channel parameters
leak = Mechanism('pas', e=-65, g=parameters['g_leak'])
hh = Mechanism('hh', gl=parameters['g_leak'], el=-65,
gnabar=parameters['gnabar'], gkbar=parameters['gkbar'])
# create cable sections
self.soma = Section(L=30, diam=30, mechanisms=[hh])
self.apical = Section(L=600, diam=2, nseg=5, mechanisms=[leak], parent=self.soma,
connect_to=1)
self.basilar = Section(L=600, diam=2, nseg=5, mechanisms=[leak], parent=self.soma)
self.axon = Section(L=1000, diam=1, nseg=37, mechanisms=[hh])
# synaptic input
self.apical.add_synapse('ampa', 'Exp2Syn', e=0.0, tau1=0.1, tau2=5.0)
# needed for PyNN
self.source_section = self.soma
self.source = self.soma(0.5)._ref_v
self.parameter_names = ('g_leak', 'gnabar', 'gkbar')
self.traces = {}
self.recording_time = False
def __init__(self, **parameters):
# Create single compartment Rubin and Terman Thalamic cell section, i.e. soma section
self.soma = Section(L=parameters['L'],
diam=parameters['diam'],
nseg=parameters['nseg'],
Ra=parameters['Ra'],
cm=parameters['cm'],
mechanisms=(Mechanism('thalamic_i_leak'),
Mechanism('thalamic_i_na_k'),
Mechanism('thalamic_i_t')))
"""
# Note: Thalamic current has no bias current in original paper, i.e. bias_current_density = 0
# Can be added in if required.
# insert current source
self.stim = h.IClamp(0.5, sec=self.soma)
self.stim.delay = 0
self.stim.dur = 1e12
self.stim.amp = parameters['bias_current_density']*(self.area())*(0.001) # (0.001 or 1e-3) is conversion factor so pA -> nA
"""
# insert synaptic noise
self.noise = h.SynNoise(0.5, sec=self.soma)
self.noise.f0 = 0
self.noise.f1 = 0.3
# Add AMPA and GABAa synapses to the cell, i.e. add to the soma section
self.AMPA = h.AMPA_S(0.5, sec=self.soma)
self.GABAa = h.GABAa_S(0.5, sec=self.soma)
# needed for PyNN
self.source_section = self.soma
self.source = self.soma(0.5)._ref_v
self.rec = h.NetCon(self.source, None,
sec=self.soma) # Needed to clear the simulator
self.spike_times = h.Vector(0)
self.parameter_names = ('L', 'diam', 'nseg', 'Ra', 'cm',
'bias_current_density')
self.traces = {}
self.recording_time = False
def __init__(self, **parameters):
# Create single compartment Destexhe Interneuron cell section, i.e. soma section
self.soma = Section(L=parameters['L'],
diam=parameters['diam'],
nseg=parameters['nseg'],
Ra=parameters['Ra'],
cm=parameters['cm'],
mechanisms=(Mechanism('interneuron_i_leak'),
Mechanism('interneuron_i_na'),
Mechanism('interneuron_i_k')))
# Add bias current to neuron model - current amplitude is in terms of original model paper, nA
self.stim = h.IClamp(0.5, sec=self.soma)
self.stim.delay = 0
self.stim.dur = 1e12
self.stim.amp = parameters['bias_current_amp'] # nA
# insert synaptic noise
self.noise = h.SynNoise(0.5, sec=self.soma)
self.noise.f0 = 0
self.noise.f1 = 0.3
# Add AMPA and GABAa synapses to the cell, i.e. add to the soma section
self.AMPA = h.AMPA_S(0.5, sec=self.soma)
self.GABAa = h.GABAa_S(0.5, sec=self.soma)
# needed for PyNN
self.source_section = self.soma
self.source = self.soma(0.5)._ref_v
self.rec = h.NetCon(
self.source, None,
sec=self.source_section) # Needed to clear the simulator
self.spike_times = h.Vector(0)
self.parameter_names = ('L', 'diam', 'nseg', 'Ra', 'cm',
'bias_current_amp')
self.traces = {}
self.recording_time = False
class ICGCell(object):
def __init__(self,
ion_type,
current_type,
L=20,
diam=20,
Ra=150,
g_pas=0.00003334):
self.ion_type = ion_type
self.current_type = current_type
leak = Mechanism('pas', e=-65, g=g_pas)
self.soma = Section(L, diam, Ra=Ra, mechanisms=[leak])
def insert_mechanism(self, mech):
"""
Insert density mechanism into cell
:param mech:
"""
if isinstance(mech, basestring):
mech = Mechanism(mech)
mech.insert_into(self.soma)
self.setRevVar()
self.setConc()
insert_distributed_channel = insert_mechanism
def insert_synapse(self, suffix, **parameters):
"""
:param suffix:
:param parameters: passed as e=-70, g=0.0001
:return:
"""
self.soma.add_synapses(suffix.lower(), suffix, **parameters)
return getattr(self.soma, suffix.lower())
insert_single_channel = insert_synapse
def setRevVar(self):
self.eRev = {
'kv': 'ek',
'nav': 'ena',
'cav': 'eca',
'kca': 'ek',
'ih': 'eh'
}[self.ion_type]
tmp_eRev = {
'kv': -86.7,
'nav': 50.0,
'cav': 135.0,
'kca': -86.7,
'ih': -45.0
}[self.ion_type]
setattr(self.soma, self.eRev, tmp_eRev)
def setConc(self):
self.iConc = {
'kv': 'ki',
'nav': 'nai',
'cav': 'cai',
'kca': 'ki',
'ih': None
}[self.ion_type]
self.oConc = {
'kv': 'ko',
'nav': 'nao',
'cav': 'cao',
'kca': 'ko',
'ih': None
}[self.ion_type]
tmp_iConc = {
'kv': 85.0,
'nav': 21.0,
'cav': 8.1929e-5,
'kca': 85.0,
'ih': None
}[self.ion_type]
tmp_oConc = {
'kv': 3.3152396,
'nav': 136.3753955,
'cav': 2.0,
'kca': 3.3152396,
'ih': None
}[self.ion_type]
setattr(self.soma, self.iConc, tmp_iConc)
setattr(self.soma, self.iConc, tmp_oConc)
def __init__(self, **parameters):
# Create cortical pyramidal neuron soma compartment using Pospischil (2008) single compartment model
self.soma = Section(L=parameters['soma_L'],
diam=parameters['soma_diam'],
nseg=parameters['soma_nseg'],
Ra=parameters['soma_Ra'],
cm=parameters['soma_cm'],
mechanisms=(Mechanism('cortical_soma_i_leak'),
Mechanism('cortical_soma_i_na'),
Mechanism('cortical_soma_i_k'),
Mechanism('cortical_soma_i_m')))
# Create cortical pyramidal neuron axon compartments using Foust (2011) axon model
self.ais = Section(L=parameters['ais_L'],
diam=parameters['ais_diam'],
nseg=parameters['ais_nseg'],
Ra=parameters['ais_Ra'],
cm=parameters['ais_cm'],
mechanisms=(Mechanism('cortical_axon_i_leak',
g_l=3.3e-5),
Mechanism('cortical_axon_i_na',
g_Na=4000e-4),
Mechanism('cortical_axon_i_kv',
g_Kv=20e-4),
Mechanism('cortical_axon_i_kd',
g_Kd=0.015)),
parent=self.soma)
# Use loop to create myelin and node sections of axon
self.myelin = []
self.node = []
for i in np.arange(parameters['num_axon_compartments']):
if i == 0:
self.myelin.append(
Section(L=parameters['myelin_L'],
diam=parameters['myelin_diam'],
nseg=11,
Ra=parameters['myelin_Ra'],
cm=parameters['myelin_cm'],
mechanisms=(Mechanism('cortical_axon_i_leak',
g_l=0),
Mechanism('cortical_axon_i_na',
g_Na=10e-4)),
parent=self.ais))
else:
self.myelin.append(
Section(L=parameters['myelin_L'],
diam=parameters['myelin_diam'],
nseg=11,
Ra=parameters['myelin_Ra'],
cm=parameters['myelin_cm'],
mechanisms=(Mechanism('cortical_axon_i_leak',
g_l=0),
Mechanism('cortical_axon_i_na',
g_Na=10e-4)),
parent=self.node[i - 1]))
self.node.append(
Section(L=parameters['node_L'],
diam=parameters['node_diam'],
nseg=parameters['node_nseg'],
Ra=parameters['node_Ra'],
cm=parameters['node_cm'],
mechanisms=(Mechanism('cortical_axon_i_leak',
g_l=0.02),
Mechanism('cortical_axon_i_na',
g_Na=2800e-4),
Mechanism('cortical_axon_i_kv', g_Kv=5e-4),
Mechanism('cortical_axon_i_kd',
g_Kd=0.0072)),
parent=self.myelin[i]))
self.collateral = Section(
L=parameters['collateral_L'],
diam=parameters['collateral_diam'],
nseg=parameters['collateral_nseg'],
Ra=parameters['collateral_Ra'],
cm=parameters['collateral_cm'],
mechanisms=(Mechanism('cortical_axon_i_leak'),
Mechanism('cortical_axon_i_na', g_Na=1333.33333e-4),
Mechanism('cortical_axon_i_kv', g_Kv=10e-4),
Mechanism('cortical_axon_i_kd', g_Kd=0.006)),
parent=self.node[-1])
middle_index = int((parameters['num_axon_compartments'] / 2.0))
self.middle_node = self.node[middle_index]
self.middle_myelin = self.myelin[middle_index]
# Add extracellular and xtra mechanisms to collateral
self.collateral.insert('extracellular')
self.collateral.insert('xtra')
# Assign default rx values to the segments rx_xtra
# - these values are updated in the main run file
# where rx is calculated as the transfer resistance
# for each collateral segments to the stimulation
# electrode in the homogenous extracellular medium
for seg in self.collateral:
seg.xtra.rx = seg.x * 3e-1
# Setting pointers to couple extracellular and xtra mechanisms for simulating extracellular DBS
for seg in self.collateral:
h.setpointer(seg._ref_e_extracellular, 'ex', seg.xtra)
h.setpointer(seg._ref_i_membrane, 'im', seg.xtra)
# Add bias current to neuron model - current amplitude is in terms of original model paper, nA
self.stim = h.IClamp(0.5, sec=self.soma)
self.stim.delay = 0
self.stim.dur = 1e12
self.stim.amp = parameters['soma_bias_current_amp']
# insert synaptic noise
self.noise = h.SynNoise(0.5, sec=self.soma)
self.noise.f0 = 0
self.noise.f1 = 0.3
# Add AMPA and GABAa synapses to the cell, i.e. add to the soma section
self.AMPA = h.AMPA_S(0.5, sec=self.soma)
self.GABAa = h.GABAa_S(0.5, sec=self.soma)
# needed for PyNN
self.source = {
'soma': self.soma(0.5)._ref_v,
'middle_axon_node': self.middle_node(0.5)._ref_v,
'collateral': self.collateral(0.5)._ref_v
}
self.source_section = {
'soma': self.soma,
'middle_axon_node': self.middle_node,
'collateral': self.collateral
}
self.rec = h.NetCon(self.source['collateral'],
None,
sec=self.source_section['collateral']
) # Needed to clear the simulator
self.spike_times = h.Vector(0)
self.traces = {}
self.recording_time = False
self.parameter_names = ()
