-
Notifications
You must be signed in to change notification settings - Fork 0
/
D_nucleus.py
121 lines (92 loc) · 4.31 KB
/
D_nucleus.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
"""
Created on Sat Feb 3 19:08:12 2024
@author: celinesoeiro
"""
import numpy as np
from tcm_params import TCM_model_parameters, coupling_matrix_normal, 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_D = neuron_quantities['D']
vr = TCM_model_parameters()['vr']
vp = TCM_model_parameters()['vp']
a_D = neuron_params['a_D']
b_D = neuron_params['b_D']
c_D = neuron_params['c_D']
d_D = neuron_params['d_D']
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_N = coupling_matrix_normal()['weights']
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']
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']
I_D = currents['D']
noise = TCM_model_parameters()['noise']
kisi_D = noise['kisi_D']
zeta_D = noise['zeta_D']
I_ps = TCM_model_parameters()['poisson_bg_activity']
I_ps_D = I_ps['D']
def 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):
I_syn = np.zeros((1, n_D))
I_syn_t = np.zeros((1, n_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_bl - td_syn - 1]
# Coupling D to M - Excitatory
coupling_D_M = W_D_M[d][0]*1*PSC_M[0][t - td_bl - 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 TC - Excitatory
coupling_D_TC = W_D_TC[d][0]*1*PSC_T_D[0][t - td_tc - td_syn - 1]
# Coupling D to TR - Excitatory
coupling_D_TR = W_D_TR[d][0]*1*PSC_TR[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])
coupling_cortex = (coupling_D_S + coupling_D_M + coupling_D_D + coupling_D_CI)/n_D
coupling_thalamus = (coupling_D_TC + coupling_D_TR)/n_D
bg_activity = 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]
v_D[d][t] = v_D_aux + dt*(dv_D + coupling_cortex + coupling_thalamus + bg_activity)
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)
# Synapse - With Thalamus
syn_D_T = 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_D_T, 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[0][d] = 1*syn_D['Ipost']
I_syn_t[0][d] = 1*syn_D_T['Ipost']
PSC_D[0][t] = np.sum(I_syn)
PSC_D_T[0][t] = np.sum(I_syn_t)
return v_D, u_D, PSC_D, u_D_syn, I_D_syn, R_D_syn, PSC_D_T