def convert_hhchannel(channel):
"""Convert a moose HHChannel object into a neuroml element.
TODO: need to check useConcentration option for Ca2+ and V
dependent gates. How to handle generic expressions???
"""
nml_channel = neuroml.IonChannel(id=channel.id_.value
, name=channel.name, type='ionChannelHH'
, conductance=channel.Gbar
)
if channel.Xpower > 0:
hh_rate_x = convert_hhgate(channel.gateX[0])
hh_rate_x.instances = channel.Xpower
nml_channel.gate.append(hh_rate_x)
if channel.Ypower > 0:
hh_rate_y = convert_hhgate(channel.gateY[0])
hh_rate_y.instances = channel.Ypower
nml_channel.gate.append(hh_rate_y)
if channel.Zpower > 0:
hh_rate_z = convert_hhgate(channel.gateZ[0])
hh_rate_y.instances = channel.Zpower
nml_channel.gate.append(hh_rate_z)
return nml_channel
def __init__(self,
k_fast_specific_gbar=36.0,
k_slow_specific_gbar=0.0,
ca_channel_specific_gbar=120.0,
ca_h_A_F=20.0,
k_tau_factor=1.0,
ca_tau_factor=1.0,
nocompile=True):
# print 'tau factor is:'
# print tau_factor
k_tau_factor = float(k_tau_factor)
ca_tau_factor = float(ca_tau_factor)
ca_h_A_F = float(ca_h_A_F)
self.k_fast_specific_gbar = float(k_fast_specific_gbar)
self.k_slow_specific_gbar = float(k_slow_specific_gbar)
self.ca_channel_specific_gbar = float(ca_channel_specific_gbar)
#First build a compartment, I infered these dimensions from the hyperpolarizing pulses
p = neuroml.Point3DWithDiam(x=0.0, y=0.0, z=0.0, diameter=800.0)
d = neuroml.Point3DWithDiam(x=800.0, y=800.0, z=800.0, diameter=800.0)
compartment = neuroml.Segment(proximal=p, distal=d)
#create a leak ion channel:
leak = neuroml.IonChannel(id="passive",
type="ionChannelPassive",
conductance="10pS")
#create membrane properties:
membrane_properties = neuroml.MembraneProperties()
leak_density = neuroml.ChannelDensity(id="leak",
ion_channels="passive",
cond_density="3.0 S_per_m2",
erev="-54.3mV")
membrane_properties.channel_densities.append(leak_density)
# self.passive = kinetics.PassiveProperties(init_vm=-30.0,
# rm=1/0.3,
# cm=10.0, #1.0
# ra=0.03)
#leave leak current out for now
#Create a LeakCurrent object:
# self.leak = kinetics.LeakCurrent(em=-30.0)
#get a Morphology object from the compartment: (no longer needed?)
#self.morphology = self.compartment.morphology
#insert the passive properties and leak current into the morphology:
self.cell.leak_current = self.leak
# self.morphology.passive_properties = self.passive
# self.morphology.leak_current = self.leak
#create two current clamp stimuli:
self.stim = kinetics.IClamp(current=0.25, delay=100.0, duration=1000.0)
#problem with Pyramidal here, inserting this overrides the previous?!
#create a current clamp stimulus:
# self.stim2 = kinetics.IClamp(current=0.10,
# delay=600.0,
# duration=500.0)
#insert the stimulus into the morphology
self.morphology[0].insert(self.stim)
if nocompile == False:
#create Ca ion channel:
ca_channel = kinetics.HHChannel(
name='ca',
specific_gbar=self.ca_channel_specific_gbar,
ion='ca',
e_rev=20.0,
x_power=3.0,
y_power=1.0)
#create K fast ion channel:
k_fast = kinetics.HHChannel(
name='kfast',
specific_gbar=self.k_fast_specific_gbar,
ion='k',
e_rev=-10.0,
x_power=4.0,
y_power=0.0)
k_slow = kinetics.HHChannel(
name='kslow',
specific_gbar=self.k_slow_specific_gbar,
ion='k',
e_rev=-10.0,
x_power=4.0,
y_power=0.0)
#create dicts containing gating parameters:
ca_m_params = {
'A_A': 0.1 * 25.0 * ca_tau_factor,
'A_B': -0.1 * ca_tau_factor,
'A_C': -1.0,
'A_D': -25.0,
'A_F': -10.0,
'B_A': 4.0 * ca_tau_factor,
'B_B': 0.0 * ca_tau_factor,
'B_C': 0.0,
'B_D': 0.0,
'B_F': 18.0
}
ca_h_params = {
'A_A': 0.07 * ca_tau_factor,
'A_B': 0.0 * ca_tau_factor,
'A_C': 0.0,
'A_D': 0.0,
'A_F': ca_h_A_F, #20
'B_A': 1.0 * ca_tau_factor,
'B_B': 0.0 * ca_tau_factor,
'B_C': 1.0,
'B_D': -30.0,
'B_F': -10.0
}
k_fast_n_params = {
'A_A': 0.01 * (10.0) * k_tau_factor,
'A_B': -0.01 * k_tau_factor, #0.01
'A_C': -1.0,
'A_D': -10.0,
'A_F': -10.0,
'B_A': 0.125 * k_tau_factor,
'B_B': 0.0 * k_tau_factor,
'B_C': 0.0,
'B_D': 0.0,
'B_F': 80.0
}
k_fast_h_params = {
'A_A': 6.6 * k_tau_factor,
'A_B': 0.0 * k_tau_factor,
'A_C': 1.0,
'A_D': 15.6,
'A_F': 10.0,
'B_A': 6.6 * k_tau_factor,
'B_B': 0.0 * k_tau_factor,
'B_C': 1.0,
'B_D': 15.6,
'B_F': -10.0
}
k_slow_n_params = {
'A_A': 0.01 * (10.0),
'A_B': -0.01,
'A_C': -2.0,
'A_D': -10.0,
'A_F': -10.0,
'B_A': 0.125,
'B_B': 0.0,
'B_C': 0.0,
'B_D': 0.0,
'B_F': 80.0
}
#setup the channel gating parameters:
ca_channel.setup_alpha(gate='X',
params=ca_m_params,
vdivs=150,
vmin=-30,
vmax=120)
ca_channel.setup_alpha(gate='Y',
params=ca_h_params,
vdivs=150,
vmin=-30,
vmax=120)
k_fast.setup_alpha(gate='X',
params=k_fast_n_params,
vdivs=150,
vmin=-30,
vmax=120)
k_fast.setup_alpha(gate='Y',
params=k_fast_h_params,
vdivs=300,
vmin=-150,
vmax=150)
k_slow.setup_alpha(gate='X',
params=k_slow_n_params,
vdivs=150,
vmin=-30,
vmax=120)
else:
ca_attributes = {
'gbar': self.ca_channel_specific_gbar,
'h_A_F': ca_h_A_F,
'm_A_A': 0.1 * 25.0 * ca_tau_factor,
'm_A_B': -0.1 * ca_tau_factor,
'm_B_A': 4.0 * ca_tau_factor,
'm_B_B': 0.0 * ca_tau_factor,
'h_A_A': 0.07 * ca_tau_factor,
'h_A_B': 0.0 * ca_tau_factor,
'h_A_F': ca_h_A_F, #20
'h_B_A': 1.0 * ca_tau_factor,
'h_B_B': 0.0 * ca_tau_factor
}
kfast_attributes = {
'gbar': self.k_fast_specific_gbar,
'm_A_A': 0.01 * (10.0) * k_tau_factor,
'm_A_B': -0.01 * k_tau_factor, #0.01
'm_B_A': 0.125 * k_tau_factor,
'm_B_B': 0.0 * k_tau_factor
}
#bug in pyramidal, if Y == 0 (no inactivation gates) it doesn't compile these values, but it should
# 'h_A_A': 6.6*k_tau_factor,
# 'h_A_B': 0.0*k_tau_factor,
# 'h_B_A': 6.6*k_tau_factor,
# 'h_B_B': 0.0*k_tau_factor}
kslow_attributes = {'gbar': self.k_slow_specific_gbar}
ca_channel = kinetics.Nmodl('ca', ca_attributes)
k_fast = kinetics.Nmodl('kfast', kfast_attributes)
k_slow = kinetics.Nmodl('kslow', kslow_attributes)
#insert the channels:
self.morphology[0].insert(ca_channel)
self.morphology[0].insert(k_fast)
self.morphology[0].insert(k_slow)
#create the NEURON environment
self.neuron_env = envs.NeuronEnv(sim_time=100, dt=1e-2)
#import morphology into environment:
self.neuron_env.import_cell(self.morphology)
"""
Generating a Hodgkin-Huxley Ion Channel and writing it to NeuroML
"""
import neuroml
import neuroml.writers as writers
from lxml import etree
from urllib import urlopen
chan = neuroml.IonChannel(id='na',
type='ionChannelHH',
conductance='10pS',
species='na',
notes="This is an example voltage-gated Na channel")
m_gate = neuroml.GateHHRates(id='m', instances='3')
h_gate = neuroml.GateHHRates(id='h', instances='1')
m_gate.forward_rate = neuroml.HHRate(type="HHExpRate",
rate="0.07per_ms",
midpoint="-65mV",
scale="-20mV")
m_gate.reverse_rate = neuroml.HHRate(type="HHSigmoidRate",
rate="1per_ms",
midpoint="-35mV",
scale="10mV")
h_gate.forward_rate = neuroml.HHRate(type="HHExpLinearRate",
rate="0.1per_ms",
midpoint="-55mV",