# -*- coding: utf-8 -*-
""" ------------------------------------------------------------------------------------
Cortical Basal Ganglia Neurons: file containing classes for defining network neurons
------------------------------------------------------------------------------------
Model References
------------------------------------------------------------------------------------
Cortical Pyramical Cell Soma:
Pospischil, M., Toledo-Rodriguez, M., Monier, C., Piwkowska, Z.,
Bal, T., Frégnac, Y., Markram, H. and Destexhe, A., 2008.
"Minimal Hodgkin–Huxley type models for different classes of
cortical and thalamic neurons."
Biological cybernetics, 99(4-5), pp.427-441.
Cortical Pyramidal Cell Axon:
Foust, A.J., Yu, Y., Popovic, M., Zecevic, D. and McCormick, D.A.,
2011. "Somatic membrane potential and Kv1 channels control spike
repolarization in cortical axon collaterals and presynaptic boutons."
Journal of Neuroscience, 31(43), pp.15490-15498.
Cortical Interneurons:
Pospischil, M., Toledo-Rodriguez, M., Monier, C., Piwkowska, Z.,
Bal, T., Frégnac, Y., Markram, H. and Destexhe, A., 2008.
"Minimal Hodgkin–Huxley type models for different classes of
cortical and thalamic neurons."
Biological cybernetics, 99(4-5), pp.427-441.
STN Neurons:
Otsuka, T., Abe, T., Tsukagawa, T. and Song, W.J., 2004.
"Conductance-based model of the voltage-dependent generation
of a plateau potential in subthalamic neurons."
Journal of neurophysiology, 92(1), pp.255-264.
GP Neurons:
Terman, D., Rubin, J.E., Yew, A.C. and Wilson, C.J., 2002.
"Activity patterns in a model for the subthalamopallidal
network of the basal ganglia."
Journal of Neuroscience, 22(7), pp.2963-2976.
*Note: The NEURON implementations of the STN and GP neurons are
from the following publication -
Hahn, P.J. and McIntyre, C.C., 2010.
"Modeling shifts in the rate and pattern of subthalamopallidal
network activity during deep brain stimulation."
Journal of computational neuroscience, 28(3), pp.425-441.
Thalamic Neurons:
Rubin, J.E. and Terman, D., 2004. High frequency stimulation of
the subthalamic nucleus eliminates pathological thalamic rhythmicity
in a computational model. Journal of computational neuroscience,
16(3), pp.211-235.
Model Implemented by John Fleming - john.fleming@ucdconnect.ie - 06/12/18
Edits: 14-02-20: Added additional getters and setters for model
neuron parameters
16-01-19: Created classes for cell models so they can be
utilized in PyNN.
Created on Tues Jan 15 12:51:26 2019
"""
from math import pi
from neuron import h
from nrnutils import Mechanism, Section
from pyNN.neuron import NativeCellType
from pyNN.parameters import Sequence
import numpy as np
from scipy import signal
# Import global variables for GPe DBS
import Global_Variables as GV
try:
reduce
except NameError:
from functools import reduce
def _new_property(obj_hierarchy, attr_name):
"""
Returns a new property, mapping attr_name to obj_hierarchy.attr_name.
For example, suppose that an object of class A has an attribute b which
itself has an attribute c which itself has an attribute d. Then placing
e = _new_property('b.c', 'd')
in the class definition of A makes A.e an alias for A.b.c.d
"""
def set(self, value):
obj = reduce(getattr, [self] + obj_hierarchy.split('.'))
setattr(obj, attr_name, value)
def get(self):
obj = reduce(getattr, [self] + obj_hierarchy.split('.'))
return getattr(obj, attr_name)
return property(fset=set, fget=get)
class Cortical_Neuron(object):
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 = ()
def soma_area(self):
"""Membrane area in µm²"""
return pi * self.soma.L * self.soma.diam
def memb_init(self):
for seg in self.soma:
seg.v = self.v_init
def _set_collateral_rx(self, sequence_values):
rx_values = sequence_values.value
for ii, seg in enumerate(self.collateral):
seg.xtra.rx = rx_values[ii]
def _get_collateral_rx(self):
print("Getter Working!")
rx_values = np.zeros((1,self.collateral.nseg))
for i, seg in enumerate(self.collateral):
rx_values[0,i] = seg.xtra.rx
print(Sequence(rx_values.flatten()))
collateral_rx = property(fget=_get_collateral_rx, fset=_set_collateral_rx)
class Cortical_Neuron_Type(NativeCellType):
default_parameters = {'soma_L': 35, 'soma_diam': 25, 'soma_nseg': 1, 'soma_Ra': 150, 'soma_cm': 1, 'soma_bias_current_amp': 0.12,
'ais_L': 20, 'ais_diam': 1.2, 'ais_nseg': 5, 'ais_Ra': 150, 'ais_cm': 0.8,
'myelin_L': 500, 'myelin_L_0': 80, 'myelin_diam': 1.4, 'myelin_Ra': 150, 'myelin_cm': 0.04,
'node_L': 2, 'node_diam': 1.2, 'node_nseg': 1,'node_Ra': 150, 'node_cm': 0.8,
'collateral_L': 500, 'collateral_diam': 0.5, 'collateral_nseg': 11, 'collateral_Ra': 150, 'collateral_cm': 0.8,
'num_axon_compartments': 10}
# Define initial vector of transfer resistances for the collateral segments
initial_collateral_rx = np.zeros((1,default_parameters['collateral_nseg'])).flatten()
initial_collateral_rx_Sequence = Sequence(initial_collateral_rx)
default_parameters['collateral_rx'] = initial_collateral_rx_Sequence
default_initial_values = {'v': -68.0}
recordable = ['soma(0.5).v','collateral(0.5).v', 'collateral(0.5).i_membrane_', 'ais(0.5).v', 'middle_node(0.5).v' , 'middle_myelin(0.5).v', 'AMPA.i', 'GABAa.i']
units = {'soma(0.5).v' : 'mV', 'collateral(0.5).v': 'mV', 'collateral(0.5).i_membrane_': 'nA', 'ais(0.5).v': 'mV', 'middle_node(0.5).v': 'mV' , 'middle_myelin(0.5).v': 'mV', 'AMPA.i': 'nA', 'GABAa.i': 'nA'}
receptor_types = ['AMPA', 'GABAa']
model = Cortical_Neuron
class Interneuron(object):
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
L = _new_property('soma', 'L')
diam = _new_property('soma', 'diam')
nseg = _new_property('soma', 'nseg')
Ra = _new_property('soma', 'Ra')
cm = _new_property('soma', 'cm')
bias_current_amp = _new_property('stim', 'amp')
def area(self):
"""Membrane area in µm²"""
return pi * self.soma.L * self.soma.diam
def memb_init(self):
for seg in self.soma:
seg.v = self.v_init
class Interneuron_Type(NativeCellType):
default_parameters = {'L': 35, 'diam': 25, 'nseg': 1, 'Ra': 150, 'cm': 1, 'bias_current_amp': 0.25}
default_initial_values = {'v': -68.0}
recordable = ['soma(0.5).v']
units = {'soma(0.5).v' : 'mV'}
receptor_types = ['AMPA', 'GABAa']
model = Interneuron
class STN_Neuron(object):
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
L = _new_property('soma', 'L')
diam = _new_property('soma', 'diam')
nseg = _new_property('soma', 'nseg')
Ra = _new_property('soma', 'Ra')
cm = _new_property('soma', 'cm')
bias_current_amp = _new_property('stim', 'amp')
def area(self):
"""Membrane area in µm²"""
return pi * self.soma.L * self.soma.diam
def memb_init(self):
for seg in self.soma:
seg.v = self.v_init
class STN_Neuron_Type(NativeCellType):
default_parameters = {'L': 60, 'diam': 60, 'nseg': 1, 'Ra': 200, 'cm': 1, 'bias_current': 0.0, 'num_AMPA_Synapses': 5, 'num_GABAa_Synapses': 5}
default_initial_values = {'v': -68.0}
recordable = ['soma(0.5).v', 'AMPA.i', 'GABAa.i']
units = {'soma(0.5).v' : 'mV', 'AMPA.i': 'nA', 'GABAa.i': 'nA'}
receptor_types = ['AMPA', 'GABAa']
model = STN_Neuron
class GP_Neuron(object):
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
L = _new_property('soma', 'L')
diam = _new_property('soma', 'diam')
nseg = _new_property('soma', 'nseg')
Ra = _new_property('soma', 'Ra')
cm = _new_property('soma', 'cm')
bias_current = _new_property('stim', 'amp')
def area(self):
"""Membrane area in µm²"""
return pi * self.soma.L * self.soma.diam
def memb_init(self):
for seg in self.soma:
seg.v = self.v_init
class GP_Neuron_Type(NativeCellType):
default_parameters = {'L': 60, 'diam': 60, 'nseg': 1, 'Ra': 200, 'cm': 1.0, 'bias_current': 0.03}
default_initial_values = {'v': -68.0}
recordable = ['soma(0.5).v']
units = {'soma(0.5).v' : 'mV'}
receptor_types = ['AMPA', 'GABAa']
model = GP_Neuron
class Thalamic_Neuron(object):
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
L = _new_property('soma', 'L')
diam = _new_property('soma', 'diam')
nseg = _new_property('soma', 'nseg')
Ra = _new_property('soma', 'Ra')
cm = _new_property('soma', 'cm')
def area(self):
"""Membrane area in µm²"""
return pi * self.soma.L * self.soma.diam
def memb_init(self):
for seg in self.soma:
seg.v = self.v_init
class Thalamic_Neuron_Type(NativeCellType):
default_parameters = {'L': 100, 'diam': 100, 'nseg': 1, 'Ra': 150, 'cm': 100, 'bias_current_amplitude': 0}
default_initial_values = {'v': -68.0}
recordable = ['soma(0.5).v']
units = {'soma(0.5).v' : 'mV'}
receptor_types = ['AMPA', 'GABAa']
model = Thalamic_Neuron
