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CI_nucleus_DBS.py
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CI_nucleus_DBS.py
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"""
Created on Tue Feb 6 23:09:41 2024
@author: celinesoeiro
"""
import numpy as np
from tcm_params import TCM_model_parameters, coupling_matrix_PD
from model_functions import izhikevich_dudt, izhikevich_dvdt, tm_synapse_eq
neuron_quantities = TCM_model_parameters()['neuron_quantities']
neuron_per_structure = TCM_model_parameters()['neuron_per_structure']
neuron_params = TCM_model_parameters()['neuron_paramaters']
currents = TCM_model_parameters()['currents_per_structure']
dt = TCM_model_parameters()['dt']
syn_params = TCM_model_parameters()['synapse_params_excitatory']
n_CI = neuron_quantities['CI']
vr = TCM_model_parameters()['vr']
vp = TCM_model_parameters()['vp']
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']
W_PD = coupling_matrix_PD()['weights']
W_CI_self = W_PD['W_II_ci']
W_CI_S = W_PD['W_IE_ci_s']
W_CI_M = W_PD['W_IE_ci_m']
W_CI_D = W_PD['W_IE_ci_d']
W_CI_TR = W_PD['W_II_ci_tr']
W_CI_TC = W_PD['W_IE_ci_tc']
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_CI = currents['CI']
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']
noise = TCM_model_parameters()['noise']
kisi_CI = noise['kisi_CI']
zeta_CI = noise['zeta_CI']
I_ps = TCM_model_parameters()['poisson_bg_activity']
I_ps_CI = I_ps['CI']
syn_fid = TCM_model_parameters()['synaptic_fidelity_layers']
CI_fid = syn_fid['CI']
affected_neurons = TCM_model_parameters()['neurons_connected_with_hyperdirect_neurons']
CI_affected = affected_neurons['CI']
def 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, I_dbs):
I_syn = np.zeros((1, n_CI))
for ci in range(n_CI):
if (ci >= 0 and ci <= CI_affected):
dbs_I = 1*I_dbs[t - 1]
else:
dbs_I = 0
v_CI_aux = 1*v_CI[ci][t - 1]
u_CI_aux = 1*u_CI[ci][t - 1]
AP_CI_aux = 0
if (v_CI_aux >= vp + zeta_CI[ci][t - 1]):
AP_CI[ci][t] = t - 1
AP_CI_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_CI_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 TC - Inhibitory
coupling_CI_TC = W_CI_TC[ci][0]*PSC_TC[0][t - td_tc - td_syn - 1]
# Coupling CI to TR - Inhibitory
coupling_CI_TR = W_CI_TR[ci][0]*PSC_TR[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])
coupling_cortex = (coupling_CI_M + coupling_CI_S + coupling_CI_CI + coupling_CI_D)/n_CI
coupling_thalamus = (coupling_CI_TC + coupling_CI_TR)/n_CI
bg_activity = 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]
v_CI[ci][t] = v_CI_aux + dt*(dv_CI + coupling_cortex + coupling_thalamus+ bg_activity + CI_fid*dbs_I)
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_CI_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[0][ci] = 1*syn_CI['Ipost']
PSC_CI[0][t] = np.sum(I_syn)
return v_CI, u_CI, PSC_CI, u_CI_syn, I_CI_syn, R_CI_syn