# soma
compartment_lengths[structure == 4] = diameter_soma / nof_segments_soma
# postsomatic region
compartment_lengths[structure == 5] = length_postsomatic_region
# total length neuron
length_neuron = sum(compartment_lengths)
##### compartment diameters
# initialize
compartment_diameters = np.zeros(nof_comps + 1) * um
# dendrite
compartment_diameters[0:start_index_soma] = diameter_dendrite
dendrite_outer_diameter = diameter_dendrite + nof_myelin_layers_dendrite * thicknes_myelin_layer * 2
# soma
soma_comp_diameters = calc.get_soma_diameters(nof_segments_soma,
diameter_dendrite, diameter_soma,
diameter_axon)
compartment_diameters[start_index_soma:end_index_soma +
2] = soma_comp_diameters
# axon
compartment_diameters[end_index_soma + 2:] = diameter_axon
axon_outer_diameter = diameter_axon + nof_myelin_layers_axon * thicknes_myelin_layer * 2
##### Compartment middle point distances (needed for plots)
distance_comps_middle = np.zeros_like(compartment_lengths)
distance_comps_middle[0] = 0.5 * compartment_lengths[0]
for ii in range(0, nof_comps - 1):
distance_comps_middle[
ii +
1] = 0.5 * compartment_lengths[ii] + 0.5 * compartment_lengths[ii + 1]
def set_up_model(dt, model, update=False):
"""This function calculates the stimulus current at the current source for
a single monophasic pulse stimulus at each point of time
Parameters
----------
dt : time
Sets the defaultclock.
model : module
Contains all morphologic and physiologic data of a model
Returns
-------
neuron
Gives back a brian2 neuron
model
Gives back the whole module
"""
start_scope()
##### Update model parameters (should be done, if original parameters have been changed)
if update:
##### rates for resting potential
alpha_m_0 = 0.1 * 25 / (np.exp(25 / 10) - 1)
beta_m_0 = 4
alpha_h_0 = 0.07
beta_h_0 = 1 / (np.exp(3) + 1)
alpha_n_0 = 0.01 * 10 / (np.exp(1) - 1)
beta_n_0 = 0.125
##### initial values for gating variables
model.m_init = alpha_m_0 / (alpha_m_0 + beta_m_0)
model.n_init = alpha_n_0 / (alpha_n_0 + beta_n_0)
model.h_init = alpha_h_0 / (alpha_h_0 + beta_h_0)
model.w_init = 1 / (np.exp(13 / 5) + 1)**(1 / 4)
model.z_init = 1 / (2 * (np.exp(0.74) + 1)) + 0.5
model.r_init = 1 / (np.exp(+62 / 35) + 1)
##### calculate Nerst potential for leakage current
model.E_L = -(1 / model.g_L) * (
model.g_Na * model.m_init**3 * model.h_init * model.E_Na +
model.g_K * model.n_init**4 * model.E_K +
model.g_KLT * model.w_init**4 * model.z_init * model.E_K +
model.g_HCN * model.r_init * model.E_HCN)
model.E_L_presomatic_region = -(1 / model.g_L) * (
model.g_Na * model.m_init**3 * model.h_init * model.E_Na +
model.g_K * model.n_init**4 * model.E_K +
model.g_KLT_soma * model.w_init**4 * model.z_init * model.E_K +
model.g_HCN_soma * model.r_init * model.E_HCN)
model.E_L_soma = -(1 / model.g_L_soma) * (
model.g_Na_soma * model.m_init**3 * model.h_init * model.E_Na +
model.g_K_soma * model.n_init**4 * model.E_K +
model.g_KLT_soma * model.w_init**4 * model.z_init * model.E_K +
model.g_HCN_soma * model.r_init * model.E_HCN)
##### structure of ANF
# terminal = 0
# internode = 1
# node = 2
# presomatic region = 3
# Soma = 4
# postsomatic region = 5)
model.structure = np.array([0] + list(np.tile([1,2],5)) + [1] + list(np.tile([3],model.nof_segments_presomatic_region)) + \
list(np.tile([4],model.nof_segments_soma)) + [5] + list(np.tile([1,2],model.nof_axonal_internodes-1)) + [1])
##### indexes presomatic region
model.index_presomatic_region = np.argwhere(model.structure == 3)
model.start_index_presomatic_region = int(
model.index_presomatic_region[0])
##### indexes of soma
model.index_soma = np.argwhere(model.structure == 4)
model.start_index_soma = int(model.index_soma[0])
model.end_index_soma = int(model.index_soma[-1])
##### further structural data
model.nof_comps = len(model.structure)
model.nof_comps_dendrite = len(
model.structure[:model.start_index_soma])
model.nof_comps_axon = len(model.structure[model.end_index_soma + 1:])
##### compartment lengths
# initialize
model.compartment_lengths = np.zeros_like(model.structure) * um
# peripheral terminal
model.compartment_lengths[np.where(
model.structure == 0)] = model.length_peripheral_terminal
# internodes dendrite
model.compartment_lengths[0:model.start_index_soma][
model.structure[0:model.start_index_soma] ==
1] = model.length_internodes_dendrite
# internodes axon
model.compartment_lengths[model.end_index_soma + 1:][
model.structure[model.end_index_soma +
1:] == 1] = model.length_internodes_axon
# nodes dendrite
model.compartment_lengths[0:model.start_index_soma][
model.structure[0:model.start_index_soma] ==
2] = model.length_nodes_dendrite
# nodes axon
model.compartment_lengths[model.end_index_soma + 1:][
model.structure[model.end_index_soma +
1:] == 2] = model.length_nodes_axon
# presomatic region
model.compartment_lengths[
model.structure ==
3] = model.length_presomatic_region / model.nof_segments_presomatic_region
# soma
model.compartment_lengths[
model.structure ==
4] = model.diameter_soma / model.nof_segments_soma
# postsomatic region
model.compartment_lengths[model.structure ==
5] = model.length_postsomatic_region
# total length neuron
model.length_neuron = sum(model.compartment_lengths)
##### compartment diameters
# initialize
model.compartment_diameters = np.zeros(model.nof_comps + 1) * um
# dendrite
model.compartment_diameters[0:model.
start_index_soma] = model.diameter_dendrite
# soma
soma_comp_diameters = calc.get_soma_diameters(model.nof_segments_soma,
model.diameter_dendrite,
model.diameter_soma,
model.diameter_axon)
model.compartment_diameters[model.
start_index_soma:model.end_index_soma +
2] = soma_comp_diameters
# axon
model.compartment_diameters[model.end_index_soma +
2:] = model.diameter_axon
##### Compartment middle point distances (needed for plots)
model.distance_comps_middle = np.zeros_like(model.compartment_lengths)
model.distance_comps_middle[0] = 0.5 * model.compartment_lengths[0]
for ii in range(0, model.nof_comps - 1):
model.distance_comps_middle[
ii + 1] = 0.5 * model.compartment_lengths[
ii] + 0.5 * model.compartment_lengths[ii + 1]
##### Capacities
# initialize
model.c_m = np.zeros_like(model.structure) * uF / cm**2
# all but internodes
model.c_m[np.where(model.structure != 1)] = model.c_m_layer
# dendrite internodes
model.c_m[0:model.start_index_soma][
model.structure[0:model.start_index_soma] ==
1] = model.c_m_layer / (1 + model.nof_myelin_layers_dendrite)
# soma
model.c_m[np.where(model.structure == 4)] = model.c_m_layer / (
1 + model.nof_myelin_layers_soma)
# axon internodes
model.c_m[model.end_index_soma +
1:][model.structure[model.end_index_soma + 1:] ==
1] = model.c_m_layer / (1 + model.nof_myelin_layers_axon)
##### Condactivities internodes
# initialize
model.g_m = np.zeros_like(model.structure) * msiemens / cm**2
# dendritic internodes
model.g_m[0:model.start_index_soma][
model.structure[0:model.start_index_soma] ==
1] = model.g_m_layer / (1 + model.nof_myelin_layers_dendrite)
# axonal internodes
model.g_m[model.end_index_soma +
1:][model.structure[model.end_index_soma + 1:] ==
1] = model.g_m_layer / (1 + model.nof_myelin_layers_axon)
##### Axoplasmatic resistances
model.compartment_center_diameters = np.zeros(model.nof_comps) * um
model.compartment_center_diameters = (
model.compartment_diameters[0:-1] +
model.compartment_diameters[1:]) / 2
model.R_a = (model.compartment_lengths * model.rho_in) / (
(model.compartment_center_diameters * 0.5)**2 * np.pi)
##### Surface arias
# lateral surfaces
m = [
np.sqrt(
abs(model.compartment_diameters[i + 1] -
model.compartment_diameters[i])**2 +
model.compartment_lengths[i]**2)
for i in range(0, model.nof_comps)
]
# total surfaces
model.A_surface = [
(model.compartment_diameters[i + 1] +
model.compartment_diameters[i]) * np.pi * m[i] * 0.5
for i in range(0, model.nof_comps)
]
##### Noise term
model.g_Na_vector = np.zeros(model.nof_comps) * msiemens / cm**2
model.g_Na_vector[:] = model.g_Na
model.g_Na_vector[model.structure == 1] = 0 * msiemens / cm**2
model.g_Na_vector[model.structure == 4] = model.g_Na_soma
model.noise_term = np.sqrt(model.A_surface * model.g_Na_vector)
##### Compartments to plot
# get indexes of all compartments that are not segmented
model.indexes_comps = np.where(
np.logical_or(
np.logical_or(
np.logical_or(model.structure == 0, model.structure == 1),
model.structure == 2), model.structure == 5))
# calculate middle compartments of presomatic region and soma
model.middle_comp_presomatic_region = int(
model.start_index_presomatic_region +
np.floor((model.nof_segments_presomatic_region) / 2))
model.middle_comp_soma = int(model.start_index_soma +
np.floor((model.nof_segments_soma) / 2))
# create array with all compartments to plot
model.comps_to_plot = np.sort(
np.append(
model.indexes_comps,
[model.middle_comp_presomatic_region, model.middle_comp_soma]))
##### initialize defaultclock
defaultclock.dt = dt
##### define morphology
morpho = Section(n=model.nof_comps,
length=model.compartment_lengths,
diameter=model.compartment_diameters)
##### define neuron
neuron = SpatialNeuron(morphology=morpho,
model=model.eqs,
Cm=model.c_m,
Ri=model.rho_in,
method="exponential_euler")
##### initial values
neuron.v = model.V_res
neuron.m = model.m_init
neuron.n = model.n_init
neuron.h = model.h_init
neuron.w = model.w_init
neuron.z = model.z_init
neuron.r = model.r_init
##### Set parameter values of differential equations
# conductances active compartments
neuron.g_Na = model.g_Na
neuron.g_K = model.g_K
neuron.g_KLT = model.g_KLT
neuron.g_HCN = model.g_HCN
neuron.g_L = model.g_L
# conductances soma
neuron.g_Na[model.index_soma] = model.g_Na_soma
neuron.g_K[model.index_soma] = model.g_K_soma
neuron.g_KLT[model.index_soma] = model.g_KLT_soma
neuron.g_HCN[model.index_soma] = model.g_HCN_soma
neuron.g_L[model.index_soma] = model.g_L_soma
# conductances presomatic region
neuron.g_KLT[model.index_presomatic_region] = model.g_KLT_somatic_region
neuron.g_HCN[model.index_presomatic_region] = model.g_HCN_somatic_region
# conductances internodes
neuron.g_myelin = model.g_m
neuron.g_Na[np.asarray(
np.where(model.structure == 1))] = 0 * msiemens / cm**2
neuron.g_K[np.asarray(
np.where(model.structure == 1))] = 0 * msiemens / cm**2
neuron.g_KLT[np.asarray(
np.where(model.structure == 1))] = 0 * msiemens / cm**2
neuron.g_HCN[np.asarray(
np.where(model.structure == 1))] = 0 * msiemens / cm**2
neuron.g_L[np.asarray(
np.where(model.structure == 1))] = 0 * msiemens / cm**2
# Nernst potential for leakage current
neuron.E_Leak = model.E_L
neuron.E_Leak[index_presomatic_region] = E_L_presomatic_region
neuron.E_Leak[model.index_soma] = E_L_soma
# other parameters
neuron.T_celsius = model.T_celsius
neuron.V_res = model.V_res
neuron.E_Na = model.E_Na
neuron.E_K = model.E_K
neuron.E_HCN = model.E_HCN
return neuron, model
def set_up_model(dt, model, update=False):
"""This function calculates the stimulus current at the current source for
a single monophasic pulse stimulus at each point of time
Parameters
----------
dt : time
Sets the defaultclock.
model : module
Contains all morphologic and physiologic data of a model
Returns
-------
neuron
Gives back a brian2 neuron
model
Gives back the whole module
"""
start_scope()
##### Update model parameters (should be done, if original parameters have been changed)
if update:
##### Temperature
model.T_kelvin = model.zero_celsius + model.T_celsius * kelvin
##### Nernst potentials Smit
# Nernst potential sodium
model.E_Na_Smit = model.R * model.T_kelvin / model.F * np.log(
model.Na_ratio) - model.V_res
# Nernst potential potassium
model.E_K_Smit = model.R * model.T_kelvin / model.F * np.log(
model.K_ratio) - model.V_res
##### rates for resting potential
alpha_m_Rat_0 = 0.1 * 25 / (np.exp(25 / 10) - 1)
beta_m_Rat_0 = 4
alpha_h_Rat_0 = 0.07
beta_h_Rat_0 = 1 / (np.exp(3) + 1)
alpha_n_Rat_0 = 0.01 * 10 / (np.exp(1) - 1)
beta_n_Rat_0 = 0.125
alpha_m_t_Smit_0 = 4.42 * 2.5 / (np.exp(2.5) -
1) * 2.23**(0.1 *
(model.T_celsius - 20))
alpha_m_p_Smit_0 = 2.06 * (2.5 - 0.1 * (-20)) / (
1 * (np.exp(2.5 - 0.1 *
(-20))) - 1) * 1.99**(0.1 * (model.T_celsius - 20))
alpha_n_Smit_0 = 0.2 * 1.0 / (10 * (np.exp(1) - 1)) * 1.5**(
0.1 * (model.T_celsius - 20))
alpha_h_Smit_0 = 1.47 * 0.07 * 1.5**(0.1 * (model.T_celsius - 20))
beta_m_t_Smit_0 = 4.42 * 4.0 * 2.23**(0.1 * (model.T_celsius - 20))
beta_m_p_Smit_0 = 2.06 * 4.0 * np.exp(
20 / 18) * 1.99**(0.1 * (model.T_celsius - 20))
beta_n_Smit_0 = 0.2 * 0.125 * 1 * 1.5**(0.1 * (model.T_celsius - 20))
beta_h_Smit_0 = 1.47 / (1 +
np.exp(3.0)) * 1.5**(0.1 *
(model.T_celsius - 20))
##### initial values for gating variables
model.m_init_Rat = alpha_m_Rat_0 / (alpha_m_Rat_0 + beta_m_Rat_0)
model.n_init_Rat = alpha_n_Rat_0 / (alpha_n_Rat_0 + beta_n_Rat_0)
model.h_init_Rat = alpha_h_Rat_0 / (alpha_h_Rat_0 + beta_h_Rat_0)
model.m_t_init_Smit = alpha_m_t_Smit_0 / (alpha_m_t_Smit_0 +
beta_m_t_Smit_0)
model.m_p_init_Smit = alpha_m_p_Smit_0 / (alpha_m_p_Smit_0 +
beta_m_p_Smit_0)
model.n_init_Smit = alpha_n_Smit_0 / (alpha_n_Smit_0 + beta_n_Smit_0)
model.h_init_Smit = alpha_h_Smit_0 / (alpha_h_Smit_0 + beta_h_Smit_0)
##### calculate Nerst potential for leakage current
model.E_L_Rat = -(1 / model.g_L_Rat) * (
model.g_Na_Rat * model.m_init_Rat**3 * model.h_init_Rat *
model.E_Na_Rat +
model.g_K_Rat * model.n_init_Rat**4 * model.E_K_Rat)
model.E_L_Smit = -(1 / model.g_L_Smit) * (
0.975 * model.g_Na_Smit * model.m_t_init_Smit**3 *
model.h_init_Smit * model.E_Na_Smit + 0.025 * model.g_Na_Smit *
model.m_p_init_Smit**3 * model.h_init_Smit * model.E_Na_Smit +
model.g_K_Smit * model.n_init_Smit**4 * model.E_K_Smit)
##### structure of ANF
# terminal = 0
# internode = 1
# node = 2
# presomatic region = 3
# Soma = 4
# postsomatic region = 5)
model.structure = np.array([0] + list(np.tile([1,2],4)) + [1] + list(np.tile([3],model.nof_segments_presomatic_region)) +\
list(np.tile([4],model.nof_segments_soma)) + [5] + list(np.tile([1,2],model.nof_axonal_internodes)) + [1])
# indexes presomatic region
model.index_presomatic_region = np.argwhere(model.structure == 3)
model.start_index_presomatic_region = int(
model.index_presomatic_region[0])
# indexes of soma
model.index_soma = np.argwhere(model.structure == 4)
model.start_index_soma = int(model.index_soma[0])
model.end_index_soma = int(model.index_soma[-1])
# further structural data
model.nof_comps = len(model.structure)
model.nof_comps_dendrite = len(
model.structure[:model.start_index_soma])
model.nof_comps_axon = len(model.structure[model.end_index_soma + 1:])
##### Compartment lengths
# initialize
model.compartment_lengths = np.zeros_like(model.structure) * um
# peripheral terminal
model.compartment_lengths[np.where(
model.structure == 0)] = model.length_peripheral_terminal
# internodes dendrite
model.compartment_lengths[0:model.start_index_soma][
model.structure[0:model.start_index_soma] ==
1] = model.length_internodes_dendrite
# internodes axon
model.compartment_lengths[model.end_index_soma + 1:][
model.structure[model.end_index_soma +
1:] == 1] = model.length_internodes_axon
# nodes dendrite
model.compartment_lengths[0:model.start_index_soma][
model.structure[0:model.start_index_soma] ==
2] = model.length_nodes_dendrite
# nodes axon
model.compartment_lengths[model.end_index_soma + 1:][
model.structure[model.end_index_soma +
1:] == 2] = model.length_nodes_axon
# presomatic region
model.compartment_lengths[np.where(
model.structure == 3
)] = model.length_presomatic_region / model.nof_segments_presomatic_region
# soma
model.compartment_lengths[np.where(
model.structure ==
4)] = model.diameter_soma / model.nof_segments_soma
# postsomatic region
model.compartment_lengths[np.where(
model.structure == 5)] = model.length_postsomatic_region
# total length neuron
model.length_neuron = sum(model.compartment_lengths)
##### Compartment diameters
# initialize
model.compartment_diameters = np.zeros(model.nof_comps + 1) * um
# dendrite
model.compartment_diameters[0:model.
start_index_soma] = model.diameter_dendrite
# soma
model.soma_comp_diameters = calc.get_soma_diameters(
model.nof_segments_soma, model.diameter_dendrite,
model.diameter_soma, model.diameter_axon)
model.compartment_diameters[model.
start_index_soma:model.end_index_soma +
2] = model.soma_comp_diameters
# axon
model.compartment_diameters[model.end_index_soma +
2:] = model.diameter_axon
##### number of axonal myelin layers
model.nof_myelin_layers_axon = np.floor(
0.5 * (model.axon_outer_diameter - model.diameter_axon) /
model.myelin_layer_thicknes_axon)
##### Compartment middle point distances (needed for plots)
model.distance_comps_middle = np.zeros_like(model.compartment_lengths)
model.distance_comps_middle[0] = 0.5 * model.compartment_lengths[0]
for ii in range(0, model.nof_comps - 1):
model.distance_comps_middle[
ii + 1] = 0.5 * model.compartment_lengths[
ii] + 0.5 * model.compartment_lengths[ii + 1]
##### Capacities
# initialize
model.c_m = np.zeros_like(model.structure) * uF / cm**2
# all but internodes dendrite
model.c_m[0:model.start_index_soma][
model.structure[0:model.start_index_soma] != 1] = model.c_m_layer
# dendritic internodes
model.c_m[0:model.start_index_soma][
model.structure[0:model.start_index_soma] ==
1] = model.c_m_layer / (1 + model.nof_myelin_layers_dendrite)
# soma
model.c_m[np.where(model.structure == 4)] = model.c_m_layer / (
1 + model.nof_myelin_layers_soma)
# all but internodes axon
model.c_m[model.end_index_soma +
1:][model.structure[model.end_index_soma +
1:] != 1] = model.c_mem
# axonal internodes
model.c_m[model.end_index_soma +
1:][model.structure[model.end_index_soma + 1:] ==
1] = 1 / (1 / model.c_mem +
model.nof_myelin_layers_axon / model.c_my)
##### Condactivities internodes
# initialize
model.g_m = np.zeros_like(model.structure) * msiemens / cm**2
# dendritic internodes
model.g_m[0:model.start_index_soma][
model.structure[0:model.start_index_soma] ==
1] = model.g_m_layer / (1 + model.nof_myelin_layers_dendrite)
# axonal internodes
model.g_m[model.end_index_soma +
1:][model.structure[model.end_index_soma + 1:] == 1] = 1 / (
model.r_mem + model.nof_myelin_layers_axon * model.r_my)
##### Axoplasmatic resistances
model.compartment_center_diameters = np.zeros(model.nof_comps) * um
model.compartment_center_diameters = (
model.compartment_diameters[0:-1] +
model.compartment_diameters[1:]) / 2
model.R_a = (model.compartment_lengths * model.rho_in) / (
(model.compartment_center_diameters * 0.5)**2 * np.pi)
##### Surface arias
# lateral surfaces
m = [
np.sqrt(
abs(model.compartment_diameters[i + 1] -
model.compartment_diameters[i])**2 +
model.compartment_lengths[i]**2)
for i in range(0, model.nof_comps)
]
# total surfaces
model.A_surface = [
(model.compartment_diameters[i + 1] +
model.compartment_diameters[i]) * np.pi * m[i] * 0.5
for i in range(0, model.nof_comps)
]
##### Noise term
model.g_Na_vector = np.zeros(model.nof_comps) * msiemens / cm**2
model.g_Na_vector[0:model.start_index_soma][
model.structure[0:model.start_index_soma] != 1] = model.g_Na_Rat
model.g_Na_vector[np.where(model.structure == 4)] = model.g_Na_soma
model.g_Na_vector[model.end_index_soma +
1:][model.structure[model.end_index_soma +
1:] != 1] = model.g_Na_Smit
model.noise_term = np.sqrt(model.A_surface * model.g_Na_vector)
##### Compartments to plot
# get indexes of all compartments that are not segmented
model.indexes_comps = np.where(
np.logical_or(
np.logical_or(
np.logical_or(model.structure == 0, model.structure == 1),
model.structure == 2), model.structure == 5))
# calculate middle compartments of presomatic region and soma
model.middle_comp_presomatic_region = int(
model.start_index_presomatic_region +
np.floor((model.nof_segments_presomatic_region) / 2))
model.middle_comp_soma = int(model.start_index_soma +
np.floor((model.nof_segments_soma) / 2))
# create array with all compartments to plot
model.comps_to_plot = np.sort(
np.append(
model.indexes_comps,
[model.middle_comp_presomatic_region, model.middle_comp_soma]))
##### initialize defaultclock
defaultclock.dt = dt
##### define morphology
morpho = Section(n=model.nof_comps,
length=model.compartment_lengths,
diameter=model.compartment_diameters)
##### define neuron
neuron = SpatialNeuron(morphology=morpho,
model=model.eqs,
Cm=model.c_m,
Ri=model.rho_in,
method="exponential_euler")
##### initial values
neuron.v = V_res
neuron.m_t_Smit = model.m_t_init_Smit
neuron.m_p_Smit = model.m_p_init_Smit
neuron.n_Smit = model.n_init_Smit
neuron.h_Smit = model.h_init_Smit
neuron.m_Rat = model.m_init_Rat
neuron.n_Rat = model.n_init_Rat
neuron.h_Rat = model.h_init_Rat
##### Set parameter values of differential equations
# conductances dentritic nodes and peripheral terminal
neuron.g_Na_Rat[0:model.start_index_soma] = model.g_Na_Rat
neuron.g_K_Rat[0:model.start_index_soma] = model.g_K_Rat
neuron.g_L_Rat[0:model.start_index_soma] = model.g_L_Rat
neuron.g_Na_Smit[0:model.start_index_soma] = 0 * msiemens / cm**2
neuron.g_K_Smit[0:model.start_index_soma] = 0 * msiemens / cm**2
neuron.g_L_Smit[0:model.start_index_soma] = 0 * msiemens / cm**2
# conductances axonal nodes
neuron.g_Na_Smit[model.end_index_soma + 1:] = model.g_Na_Smit
neuron.g_K_Smit[model.end_index_soma + 1:] = model.g_K_Smit
neuron.g_L_Smit[model.end_index_soma + 1:] = model.g_L_Smit
neuron.g_Na_Rat[model.end_index_soma + 1:] = 0 * msiemens / cm**2
neuron.g_K_Rat[model.end_index_soma + 1:] = 0 * msiemens / cm**2
neuron.g_L_Rat[model.end_index_soma + 1:] = 0 * msiemens / cm**2
# conductances soma
neuron.g_Na_Rat[model.index_soma] = model.g_Na_soma
neuron.g_K_Rat[model.index_soma] = model.g_K_soma
neuron.g_L_Rat[model.index_soma] = model.g_L_soma
neuron.g_Na_Smit[model.index_soma] = 0 * msiemens / cm**2
neuron.g_K_Smit[model.index_soma] = 0 * msiemens / cm**2
neuron.g_L_Smit[model.index_soma] = 0 * msiemens / cm**2
# conductances internodes
neuron.g_myelin = model.g_m
neuron.g_Na_Rat[np.asarray(
np.where(model.structure == 1))] = 0 * msiemens / cm**2
neuron.g_K_Rat[np.asarray(
np.where(model.structure == 1))] = 0 * msiemens / cm**2
neuron.g_L_Rat[np.asarray(
np.where(model.structure == 1))] = 0 * msiemens / cm**2
neuron.g_Na_Smit[np.asarray(
np.where(model.structure == 1))] = 0 * msiemens / cm**2
neuron.g_K_Smit[np.asarray(
np.where(model.structure == 1))] = 0 * msiemens / cm**2
neuron.g_L_Smit[np.asarray(
np.where(model.structure == 1))] = 0 * msiemens / cm**2
# other parameters
neuron.V_res = model.V_res
neuron.E_Na_Smit = model.E_Na_Smit
neuron.E_K_Smit = model.E_K_Smit
neuron.E_L_Smit = model.E_L_Smit
neuron.E_Na_Rat = model.E_Na_Rat
neuron.E_K_Rat = model.E_K_Rat
neuron.E_L_Rat = model.E_L_Rat
neuron.T_celsius = model.T_celsius
return neuron, model