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step-4-tc-normal.py
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step-4-tc-normal.py
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from tcm_params import TCM_model_parameters, coupling_matrix_normal, coupling_matrix_PD
from model_plots import plot_heat_map, layer_raster_plot, plot_raster, plot_BP_filter, plot_PSD, plot_BP_filter_normal
from model_functions import LFP, butter_bandpass_filter, PSD
from TR_nucleus import TR_nucleus
from TC_nucleus import TC_nucleus
from S_nucleus import S_nucleus
from M_nucleus import M_nucleus
from D_nucleus import D_nucleus
from CI_nucleus import CI_nucleus
import numpy as np
import pandas as pd
from random import seed, random
seed(1)
random_factor = random()
# =============================================================================
# INITIAL VALUES
# =============================================================================
print("-- Initializing the global values")
neuron_quantities = TCM_model_parameters()['neuron_quantities']
neuron_per_structure = TCM_model_parameters()['neuron_per_structure']
neuron_params = TCM_model_parameters()['neuron_paramaters']
neuron_types_per_structure = TCM_model_parameters()['neuron_types_per_structure']
currents = TCM_model_parameters()['currents_per_structure']
p = TCM_model_parameters()['synapse_total_params']
noise = TCM_model_parameters()['noise']
# 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']
n_CI_FS = neuron_per_structure['neurons_ci_1']
n_CI_LTS = neuron_per_structure['neurons_ci_2']
n_D_RS = neuron_per_structure['neurons_d_1']
n_D_IB = neuron_per_structure['neurons_d_2']
n_S_RS = neuron_per_structure['neurons_s_1']
n_S_IB = neuron_per_structure['neurons_s_2']
vr = TCM_model_parameters()['vr']
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_v = TCM_model_parameters()['time_vector']
time = np.arange(1, sim_steps)
lowcut = TCM_model_parameters()['beta_low']
highcut = TCM_model_parameters()['beta_high']
fs = TCM_model_parameters()['sampling_frequency']
dbs_begin = TCM_model_parameters()['dbs_begin']
dbs_end = TCM_model_parameters()['dbs_end']
# =============================================================================
# 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)
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 VARIABELS
# =============================================================================
print("-- Initializing the neuron variables")
## Post-Synaptic Currents
PSC_S = np.zeros((1, sim_steps))
PSC_M = np.zeros((1, sim_steps))
PSC_D = np.zeros((1, sim_steps))
PSC_CI = np.zeros((1, sim_steps))
PSC_TC = np.zeros((1, sim_steps))
PSC_TR = np.zeros((1, sim_steps))
## Thalamus-Cortex coupling params
PSC_T_D = np.zeros((1, sim_steps)) # from Thalamus to D
PSC_D_T = np.zeros((1, sim_steps)) # from D to Thalamus
## 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] = neuron_params['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))
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] = neuron_params['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))
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] = neuron_params['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))
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] = neuron_params['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))
del i
## TC
AP_TC = np.zeros((n_TC, sim_steps))
v_TC = np.zeros((n_TC, sim_steps))
u_TC = np.zeros((n_TC, sim_steps))
for i in range(n_TC):
v_TC[i][0] = vr
u_TC[i][0] = neuron_params['b_TC'][0][0]*vr
u_TC_syn = np.zeros((1, p))
R_TC_syn = np.ones((1, p))
I_TC_syn = np.zeros((1, p))
del i
## TR
AP_TR = np.zeros((n_TR, sim_steps))
v_TR = np.zeros((n_TR, sim_steps))
u_TR = np.zeros((n_TR, sim_steps))
for i in range(n_TR):
v_TR[i][0] = vr
u_TR[i][0] = neuron_params['b_TR'][0][0]*vr
u_TR_syn = np.zeros((1, p))
R_TR_syn = np.ones((1, p))
I_TR_syn = np.zeros((1, p))
# =============================================================================
# MAIN
# =============================================================================
print("-- Running the model")
for t in time:
# =============================================================================
# TR
# =============================================================================
v_TR, u_TR, PSC_TR, u_TR_syn, I_TR_syn, R_TR_syn = TR_nucleus(t, v_TR, u_TR, AP_TR, PSC_TR, PSC_TC, PSC_CI, PSC_D_T, PSC_M, PSC_S, u_TR_syn, R_TR_syn, I_TR_syn)
# =============================================================================
# TC
# =============================================================================
v_TC, u_TC, PSC_TC, u_TC_syn, I_TC_syn, R_TC_syn, PSC_T_D = TC_nucleus(t, v_TC, u_TC, AP_TC, PSC_TC, PSC_S, PSC_M, PSC_D_T, PSC_TR, PSC_CI, PSC_T_D, R_TC_syn, u_TC_syn, I_TC_syn)
# =============================================================================
# S
# =============================================================================
v_S, u_S, PSC_S, u_S_syn, I_S_syn, R_S_syn = S_nucleus(t, v_S, u_S, AP_S, PSC_S, PSC_M, PSC_D, PSC_CI, PSC_TC, PSC_TR, u_S_syn, R_S_syn, I_S_syn)
# =============================================================================
# M
# =============================================================================
v_M, u_M, PSC_M, u_M_syn, I_M_syn, R_M_syn = M_nucleus(t, v_M, u_M, AP_M, PSC_M, PSC_S, PSC_D, PSC_CI, PSC_TC, PSC_TR, u_M_syn, R_M_syn, I_M_syn)
# =============================================================================
# D
# =============================================================================
v_D, u_D, PSC_D, u_D_syn, I_D_syn, R_D_syn, PSC_D_T = D_nucleus(t, v_D, u_D, AP_D, PSC_D, PSC_S, PSC_M, PSC_T_D, PSC_CI, PSC_TR, PSC_D_T, u_D_syn, R_D_syn, I_D_syn)
# =============================================================================
# CI
# =============================================================================
v_CI, u_CI, PSC_CI, u_CI_syn, I_CI_syn, R_CI_syn = CI_nucleus(t, v_CI, u_CI, AP_CI, PSC_CI, PSC_D, PSC_M, PSC_S, PSC_TC, PSC_TR, u_CI_syn, R_CI_syn, I_CI_syn)
# =============================================================================
# PLOTS
# =============================================================================
print("-- Plotting the results")
# plot_voltages(n_neurons = n_S, voltage = v_S, title = "v - Layer 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 - Layer 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 - Layer 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 = "Layer 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))
# plot_voltages(n_neurons = n_TC, voltage = v_TC, title = "TC", neuron_types=neuron_types_per_structure['TC'])
layer_raster_plot(n = n_TC, AP = AP_TC, sim_steps = sim_steps, layer_name = 'TC', dt = dt)
print('APS in TC layer = ',np.count_nonzero(AP_TC))
# plot_voltages(n_neurons = n_TR, voltage = v_TR, title = "TR", neuron_types=neuron_types_per_structure['TR'])
layer_raster_plot(n = n_TR, AP = AP_TR, sim_steps = sim_steps, layer_name = 'TR', dt = dt)
print('APS in TR layer = ',np.count_nonzero(AP_TR))
plot_raster(
sim_steps=sim_steps,
dt = dt,
chop_till = 0,
n_TR = n_TR,
n_TC = n_TC,
n_CI = n_CI,
n_D = n_D,
n_M = n_M,
n_S = n_S,
n_total = n_total,
n_CI_LTS = n_CI_LTS,
n_D_IB = n_D_IB,
n_S_IB = n_S_IB,
spike_times_TR = AP_TR,
spike_times_TC = AP_TC,
spike_times_CI = AP_CI,
spike_times_D = AP_D,
spike_times_M = AP_M,
spike_times_S = AP_S
)
# =============================================================================
# Signal analysis
# =============================================================================
print("-- Signal analysis")
## Getting the Local Field Potential
LFP_D_normal = LFP(PSC_D[0], PSC_CI[0])
## Bandpass filtering the LFP to get the beta waves
LFP_normal = LFP_D_normal[dbs_begin:dbs_end]
beta_waves_normal_full = butter_bandpass_filter(LFP_D_normal, lowcut, highcut, fs)
plot_BP_filter_normal(beta_waves_normal_full)
# Power Spectral Density
f_normal_full, S_normal_full = PSD(beta_waves_normal_full, fs)
plot_PSD(f_normal_full, S_normal_full)
beta_waves_normal = butter_bandpass_filter(LFP_normal, lowcut, highcut, fs)
plot_BP_filter_normal(beta_waves_normal)
# Power Spectral Density
f_normal, S_normal = PSD(beta_waves_normal, fs)
plot_PSD(f_normal, S_normal)
print("-- Done!")