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step-3-cortical.py
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step-3-cortical.py
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"""
Created on Sun Jan 28 18:35:18 2024
@author: celinesoeiro
"""
import numpy as np
import pandas as pd
from tcm_params import TCM_model_parameters, coupling_matrix_normal, coupling_matrix_PD
from model_functions import izhikevich_dudt, izhikevich_dvdt, tm_synapse_eq, poisson_spike_generator
from model_plots import plot_heat_map, layer_raster_plot, plot_voltages
# =============================================================================
# INITIAL VALUES
# =============================================================================
print("-- Initializing the global values")
neuron_quantities = TCM_model_parameters()['neuron_quantities']
neuron_types_per_structure = TCM_model_parameters()['neuron_types_per_structure']
neuron_params = TCM_model_parameters()['neuron_paramaters']
currents = TCM_model_parameters()['currents_per_structure']
W_N = coupling_matrix_normal()['weights']
syn_params = TCM_model_parameters()['synapse_params_excitatory']
td_wl = TCM_model_parameters()['time_delay_within_layers']
td_bl = TCM_model_parameters()['time_delay_between_layers']
td_ct = TCM_model_parameters()['time_delay_cortex_thalamus']
td_tc = TCM_model_parameters()['time_delay_thalamus_cortex']
td_syn = TCM_model_parameters()['time_delay_synapse']
p = TCM_model_parameters()['synapse_total_params']
t_f_E = syn_params['t_f']
t_d_E = syn_params['t_d']
t_s_E = syn_params['t_s']
U_E = syn_params['U']
A_E = syn_params['distribution']
A_E_D_T = syn_params['distribution_D_T']
t_f_I = syn_params['t_f']
t_d_I = syn_params['t_d']
t_s_I = syn_params['t_s']
U_I = syn_params['U']
A_I = syn_params['distribution']
# Neuron quantities
n_S = neuron_quantities['S']
n_M = neuron_quantities['M']
n_D = neuron_quantities['D']
n_CI = neuron_quantities['CI']
n_TR = neuron_quantities['TR']
n_TC = neuron_quantities['TC']
n_Hyper = neuron_quantities['HD']
n_total = neuron_quantities['total']
vr = TCM_model_parameters()['vr']
vp = TCM_model_parameters()['vp']
dt = TCM_model_parameters()['dt']
sim_time = TCM_model_parameters()['simulation_time']
T = TCM_model_parameters()['simulation_time_ms']
sim_steps = TCM_model_parameters()['simulation_steps']
time = np.arange(1, sim_steps)
# =============================================================================
# COUPLING MATRIXES
# =============================================================================
# Weight Matrix Normal Condition
Z_N = coupling_matrix_normal()['matrix']
# Weight Matrix Parkinsonian Desease Condition
Z_PD = coupling_matrix_PD()['matrix']
# normalizing Normal coupling matrix
Z_N_norm = Z_N/np.linalg.norm(Z_N)
# normalizing PD coupling matrix
Z_PD_norm = Z_PD/np.linalg.norm(Z_PD)
# =============================================================================
# Graphs - Coupling Matrixes - Normal vs Parkinsonian
# =============================================================================
print("-- Printing the coupling matrixes")
CM_Normal = pd.DataFrame(Z_N_norm, columns=['S', 'M', 'D', 'CI', 'TC', 'TR'])
CM_PD = pd.DataFrame(Z_PD_norm, columns=['S', 'M', 'D', 'CI', 'TC', 'TR'])
plot_heat_map(matrix_normal = CM_Normal, matrix_PD = CM_PD)
# =============================================================================
# NEURON PARAMS
# =============================================================================
a_S = neuron_params['a_S']
b_S = neuron_params['b_S']
c_S = neuron_params['c_S']
d_S = neuron_params['d_S']
a_M = neuron_params['a_M']
b_M = neuron_params['b_M']
c_M = neuron_params['c_M']
d_M = neuron_params['d_M']
a_D = neuron_params['a_D']
b_D = neuron_params['b_D']
c_D = neuron_params['c_D']
d_D = neuron_params['d_D']
a_CI = neuron_params['a_CI']
b_CI = neuron_params['b_CI']
c_CI = neuron_params['c_CI']
d_CI = neuron_params['d_CI']
I_S = currents['S']
I_M = currents['M']
I_D = currents['D']
I_CI = currents['CI']
# =============================================================================
# SYNAPTIC WEIGHTS AND PARAMETERS
# =============================================================================
# Weight Matrix Normal Condition
W_N = coupling_matrix_normal()['weights']
# Weight Matrix Parkinsonian Desease Condition
W_PD = coupling_matrix_PD()['weights']
# S
W_S_self = W_N['W_EE_s']
W_S_M = W_N['W_EE_s_m']
W_S_D = W_N['W_EE_s_d']
W_S_CI = W_N['W_EI_s_ci']
W_S_TR = W_N['W_EI_s_tr']
W_S_TC = W_N['W_EE_s_tc']
#M
W_M_self = W_N['W_EE_m']
W_M_S = W_N['W_EE_m_s']
W_M_D = W_N['W_EE_m_d']
W_M_CI = W_N['W_EI_m_ci']
W_M_TR = W_N['W_EI_m_tr']
W_M_TC = W_N['W_EE_m_tc']
# CI
W_CI_self = W_N['W_II_ci']
W_CI_S = W_N['W_IE_ci_s']
W_CI_M = W_N['W_IE_ci_m']
W_CI_D = W_N['W_IE_ci_d']
W_CI_TR = W_N['W_II_ci_tr']
W_CI_TC = W_N['W_IE_ci_tc']
# D
W_D_self = W_N['W_EE_d']
W_D_S = W_N['W_EE_d_s']
W_D_M = W_N['W_EE_d_m']
W_D_CI = W_N['W_EI_d_ci']
W_D_TR = W_N['W_EI_d_tr']
W_D_TC = W_N['W_EE_d_tc']
# =============================================================================
# NOISE
# =============================================================================
noise = TCM_model_parameters()['noise']
kisi_CI = noise['kisi_CI']
zeta_CI = noise['zeta_CI']
kisi_D = noise['kisi_D']
zeta_D = noise['zeta_D']
kisi_S = noise['kisi_S']
zeta_S = noise['zeta_S']
kisi_M = noise['kisi_M']
zeta_M = noise['zeta_M']
I_ps = TCM_model_parameters()['poisson_bg_activity']
I_ps_CI = I_ps['CI']
I_ps_D = I_ps['D']
I_ps_S = I_ps['S']
I_ps_M = I_ps['M']
# =============================================================================
# NEURON VARIABELS
# =============================================================================
## S
AP_S = np.zeros((n_S, sim_steps))
v_S = np.zeros((n_S, sim_steps))
u_S = np.zeros((n_S, sim_steps))
for i in range(n_S):
v_S[i][0] = vr
u_S[i][0] = b_S[0][0]*vr
u_S_syn = np.zeros((1, p))
R_S_syn = np.ones((1, p))
I_S_syn = np.zeros((1, p))
PSC_S = np.zeros((1, sim_steps))
del i
## M
AP_M = np.zeros((n_M, sim_steps))
v_M = np.zeros((n_M, sim_steps))
u_M = np.zeros((n_M, sim_steps))
for i in range(n_M):
v_M[i][0] = vr
u_M[i][0] = b_M[0][0]*vr
u_M_syn = np.zeros((1, p))
R_M_syn = np.ones((1, p))
I_M_syn = np.zeros((1, p))
PSC_M = np.zeros((1, sim_steps))
del i
## D
AP_D = np.zeros((n_D, sim_steps))
v_D = np.zeros((n_D, sim_steps))
u_D = np.zeros((n_D, sim_steps))
for i in range(n_D):
v_D[i][0] = vr
u_D[i][0] = b_D[0][0]*vr
## D - Self
u_D_syn = np.zeros((1, p))
R_D_syn = np.ones((1, p))
I_D_syn = np.zeros((1, p))
PSC_D = np.zeros((1, sim_steps))
del i
## CI
AP_CI = np.zeros((n_CI, sim_steps))
v_CI = np.zeros((n_CI, sim_steps))
u_CI = np.zeros((n_CI, sim_steps))
for i in range(n_CI):
v_CI[i][0] = vr
u_CI[i][0] = b_CI[0][0]*vr
u_CI_syn = np.zeros((1, p))
R_CI_syn = np.ones((1, p))
I_CI_syn = np.zeros((1, p))
PSC_CI = np.zeros((1, sim_steps))
[spike_T, I_T] = poisson_spike_generator(num_steps = sim_steps, dt = dt, num_neurons = 1, thalamic_firing_rate = 20, current_value=None)
# =============================================================================
# MAIN
# =============================================================================
print("-- Running model")
for t in time:
I_syn_D = np.zeros((1, n_D))
I_syn_CI = np.zeros((1, n_CI))
I_syn_S = np.zeros((1, n_S))
I_syn_M = np.zeros((1, n_M))
# =============================================================================
# S
# =============================================================================
for s in range(n_S):
v_S_aux = 1*v_S[s][t - 1]
u_S_aux = 1*u_S[s][t - 1]
AP_S_aux = 0
if (v_S_aux >= vp + zeta_S[s][t - 1]):
AP_S_aux = 1
AP_S[s][t] = t - 1
v_S_aux = v_S[s][t]
v_S[s][t] = c_S[0][s]
u_S[s][t] = u_S_aux + d_S[0][s]
else:
AP_S[s][t] = 0
AP_S_aux = 0
# Self feedback - Inhibitory
coupling_S_S = W_S_self[s][0]*1*PSC_S[0][t - td_wl - td_syn - 1]
# Coupling S to M - Excitatory
coupling_S_M = W_S_M[s][0]*1*PSC_M[0][t - td_wl - td_syn - 1]
# Coupling S to D - Excitatory
coupling_S_D = W_S_D[s][0]*1*PSC_D[0][t - td_wl - td_syn - 1]
# Coupling S to CI - Inhibitory
coupling_S_CI = W_S_CI[s][0]*1*PSC_CI[0][t - td_wl - td_syn - 1]
# Coupling S to T - Excitatory
coupling_S_T = W_S_TC[s][0]*I_T[0][t - td_tc - td_syn - 1]
dv_S = izhikevich_dvdt(v = v_S_aux, u = u_S_aux, I = I_S[s])
du_S = izhikevich_dudt(v = v_S_aux, u = u_S_aux, a = a_S[0][s], b = b_S[0][s])
v_S[s][t] = v_S_aux + dt*(dv_S + coupling_S_S + coupling_S_M + coupling_S_D + coupling_S_CI + coupling_S_T + kisi_S[s][t - 1] + I_ps_S[0][t - td_wl - td_syn - 1] - I_ps_S[1][t - td_wl - td_syn - 1])
u_S[s][t] = u_S_aux + dt*du_S
# Synapse - Within cortex
syn_S = tm_synapse_eq(u = u_S_syn, R = R_S_syn, I = I_S_syn,AP = AP_S_aux, t_f = t_f_E, t_d = t_d_E, t_s = t_s_E,U = U_E, A = A_E, dt = dt, p = p)
R_S_syn = 1*syn_S['R']
u_S_syn = 1*syn_S['u']
I_S_syn = 1*syn_S['I']
I_syn_S[0][s] = 1*syn_S['Ipost']
PSC_S[0][t] = np.sum(I_syn_S)
# =============================================================================
# M
# =============================================================================
for m in range(n_M):
v_M_aux = 1*v_M[m][t - 1]
u_M_aux = 1*u_M[m][t - 1]
AP_M_aux = 0
if (v_M_aux >= vp + zeta_M[m][t - 1]):
AP_M_aux = 1
AP_M[m][t] = t - 1
v_M_aux = v_M[m][t]
v_M[m][t] = c_M[0][m]
u_M[m][t] = u_M_aux + d_M[0][m]
else:
AP_M[m][t] = 0
AP_M_aux = 0
# Self feedback - Inhibitory
coupling_M_M = W_M_self[m][0]*1*PSC_M[0][t - td_wl - td_syn - 1]
# Coupling M to S - Excitatory
coupling_M_S = W_M_S[m][0]*1*PSC_S[0][t - td_wl - td_syn - 1]
# Coupling M to D - Excitatory
coupling_M_D = W_M_D[m][0]*1*PSC_D[0][t - td_wl - td_syn - 1]
# Coupling M to CI - Inhibitory
coupling_M_CI = W_M_CI[m][0]*1*PSC_CI[0][t - td_wl - td_syn - 1]
# Coupling M to T - Excitatory
coupling_M_T = W_M_TC[m][0]*I_T[0][t - td_tc - td_syn - 1]
dv_M = izhikevich_dvdt(v = v_M_aux, u = u_M_aux, I = I_M[m])
du_M = izhikevich_dudt(v = v_M_aux, u = u_M_aux, a = a_M[0][m], b = b_M[0][m])
v_M[m][t] = v_M_aux + dt*(dv_M + coupling_M_M + coupling_M_S + coupling_M_D + coupling_M_CI + coupling_M_T + kisi_M[m][t - 1] + I_ps_M[0][t - td_wl - td_syn - 1] - I_ps_M[1][t - td_wl - td_syn - 1])
u_M[m][t] = u_M_aux + dt*du_M
# Synapse - Within cortex
syn_M = tm_synapse_eq(u = u_M_syn, R = R_M_syn, I = I_M_syn, AP = AP_M_aux,t_f = t_f_E,t_d = t_d_E,t_s = t_s_E,U = U_E, A = A_E,dt = dt,p = p)
R_M_syn = 1*syn_M['R']
u_M_syn = 1*syn_M['u']
I_M_syn = 1*syn_M['I']
I_syn_M[0][m] = 1*syn_M['Ipost']
PSC_M[0][t] = np.sum(I_syn_M)
# =============================================================================
# D
# =============================================================================
for d in range(n_D):
v_D_aux = 1*v_D[d][t - 1]
u_D_aux = 1*u_D[d][t - 1]
AP_D_aux = 0
if (v_D_aux >= vp + zeta_D[d][t - 1]):
AP_D_aux = 1
AP_D[d][t] = t - 1
v_D_aux = v_D[d][t]
v_D[d][t] = c_D[0][d]
u_D[d][t] = u_D_aux + d_D[0][d]
else:
AP_D[d][t] = 0
AP_D_aux = 0
# Self feedback - Inhibitory
coupling_D_D = W_D_self[d][0]*1*PSC_D[0][t - td_wl - td_syn - 1]
# Coupling D to S - Excitatory
coupling_D_S = W_D_S[d][0]*1*PSC_S[0][t - td_wl - td_syn - 1]
# Coupling D to M - Excitatory
coupling_D_M = W_D_M[d][0]*1*PSC_M[0][t - td_wl - td_syn - 1]
# Coupling D to CI - Inhibitory
coupling_D_CI = W_D_CI[d][0]*1*PSC_CI[0][t - td_wl - td_syn - 1]
# Coupling D to T - Excitatory
coupling_D_T = W_D_TC[d][0]*I_T[0][t - td_tc - td_syn - 1]
dv_D = izhikevich_dvdt(v = v_D_aux, u = u_D_aux, I = I_D[d])
du_D = izhikevich_dudt(v = v_D_aux, u = u_D_aux, a = a_D[0][d], b = b_D[0][d])
v_D[d][t] = v_D_aux + dt*(dv_D + coupling_D_S + coupling_D_M + coupling_D_D + coupling_D_CI + coupling_D_T + kisi_D[d][t - 1] + I_ps_D[0][t - td_wl - td_syn - 1] - I_ps_D[1][t - td_wl - td_syn - 1])
u_D[d][t] = u_D_aux + dt*du_D
# Synapse - Within cortex
syn_D = tm_synapse_eq(u = u_D_syn, R = R_D_syn,I = I_D_syn, AP = AP_D_aux, t_f = t_f_E, t_d = t_d_E, t_s = t_s_E,U = U_E, A = A_E,dt = dt,p = p)
R_D_syn = 1*syn_D['R']
u_D_syn = 1*syn_D['u']
I_D_syn = 1*syn_D['I']
I_syn_D[0][d] = 1*syn_D['Ipost']
PSC_D[0][t] = np.sum(I_syn_D)
# =============================================================================
# CI
# =============================================================================
for ci in range(n_CI):
v_CI_aux = 1*v_CI[ci][t - 1]
u_CI_aux = 1*u_CI[ci][t - 1]
AP_C_aux = 0
if (v_CI_aux >= vp + zeta_CI[ci][t - 1]):
AP_CI[ci][t] = t - 1
AP_C_aux = 1
v_CI_aux = v_CI[ci][t]
v_CI[ci][t] = c_CI[0][ci]
u_CI[ci][t] = u_CI_aux + d_CI[0][ci]
else:
AP_C_aux = 0
AP_CI[ci][t] = 0
# Self feeback - Inhibitory
coupling_CI_CI = W_CI_self[ci][0]*PSC_CI[0][t - td_wl - td_syn - 1]
# Coupling CI to S - Inhibitory
coupling_CI_S = W_CI_S[ci][0]*PSC_S[0][t - td_wl - td_syn - 1]
# Coupling CI to M - Inhibitory
coupling_CI_M = W_CI_M[ci][0]*PSC_M[0][t - td_wl - td_syn - 1]
# Coupling CI to D - Inhibitory
coupling_CI_D = W_CI_D[ci][0]*PSC_D[0][t - td_wl - td_syn - 1]
# Coupling CI to T - Inhibitory
coupling_CI_T = W_CI_TC[ci][0]*I_T[0][t - td_tc - td_syn - 1]
dv_CI = izhikevich_dvdt(v = v_CI_aux, u = u_CI_aux, I = I_CI[ci])
du_CI = izhikevich_dudt(v = v_CI_aux, u = u_CI_aux, a = a_CI[0][ci], b = b_CI[0][ci])
v_CI[ci][t] = v_CI_aux + dt*(dv_CI + coupling_CI_M + coupling_CI_S + coupling_CI_CI + coupling_CI_D + coupling_CI_T + kisi_CI[ci][t - 1] + I_ps_CI[0][t - td_wl - td_syn - 1] - I_ps_CI[0][t - td_wl - td_syn - 1])
u_CI[ci][t] = u_CI_aux + dt*du_CI
# Synapse
syn_CI = tm_synapse_eq(u = u_CI_syn, R = R_CI_syn, I = I_CI_syn, AP = AP_C_aux, t_f = t_f_I, t_d = t_d_I,t_s = t_s_I, U = U_I, A = A_I,dt = dt, p = p)
R_CI_syn = 1*syn_CI['R']
u_CI_syn = 1*syn_CI['u']
I_CI_syn = 1*syn_CI['I']
I_syn_CI[0][ci] = 1*syn_CI['Ipost']
PSC_CI[0][t] = np.sum(I_syn_CI)
# =============================================================================
# PLOTS
# =============================================================================
print("-- Plotting results")
plot_voltages(n_neurons = n_S, voltage = v_S, title = "v - S", neuron_types = neuron_types_per_structure['S'])
layer_raster_plot(n = n_S, AP = AP_S, sim_steps = sim_steps, layer_name = 'S', dt = dt)
print('APs in S layer = ', np.count_nonzero(AP_S))
plot_voltages(n_neurons = n_M, voltage = v_M, title = "v - M", neuron_types = neuron_types_per_structure['M'])
layer_raster_plot(n = n_M, AP = AP_M, sim_steps = sim_steps, layer_name = 'M', dt = dt)
print('APs in M layer = ', np.count_nonzero(AP_M))
plot_voltages(n_neurons = n_D, voltage = v_D, title = "v - D", neuron_types=neuron_types_per_structure['D'])
layer_raster_plot(n = n_D, AP = AP_D, sim_steps = sim_steps, layer_name = 'D', dt = dt)
print('APs in D layer = ', np.count_nonzero(AP_D))
plot_voltages(n_neurons = n_CI, voltage = v_CI, title = "v - CI", neuron_types=neuron_types_per_structure['CI'])
layer_raster_plot(n = n_CI, AP = AP_CI, sim_steps = sim_steps, layer_name = 'CI', dt = dt)
print('APS in CI layer = ',np.count_nonzero(AP_CI))
print("-- Done!")