-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathFEF_and_LIP_parallel_alternate3.py
266 lines (210 loc) · 8.97 KB
/
FEF_and_LIP_parallel_alternate3.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
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 11 13:56:08 2020
@author: amelie
"""
from brian2 import *
from scipy import signal
from FEF_full3_alternate3 import *
from LIP_full_alternate3 import *
from itertools import *
from joblib import Parallel, delayed
import multiprocessing
def save_raster(name,raster_i,raster_t,path):
raster_file=open(path+'/raster_'+name+'_i.txt','w')
for elem in raster_i:
raster_file.write(str(elem)+',')
raster_file.close()
raster_file=open(path+'/raster_'+name+'_t.txt','w')
for elem in raster_t:
raster_file.write(str(elem)+',')
raster_file.close()
return
def generate_syn(source,target,syntype,connection_pattern,g_i,taur_i,taud_i,V_i):
eq_syn='''_post=s_i*g_i*(V_post-V_i) : amp * meter ** -2 (summed)
ds_i/dt=-s_i/taud_i+(1-s_i)/taur_i*0.5*(1+tanh(V_pre/10/mV)) : 1
g_i : siemens * meter**-2
V_i : volt
taud_i : second
taur_i : second
'''
S=Synapses(source,target,model=syntype+eq_syn,method='exact')
if connection_pattern=='':
S.connect()
else :
S.connect(j=connection_pattern, skip_if_invalid=True)
S.g_i=g_i
S.taur_i=taur_i
S.taud_i=taud_i
S.V_i=V_i
return S
def FEF_and_LIP(simu,path):
prefs.codegen.target = 'numpy'
target_time,N_simu,t_SI,t_FS=simu[0],simu[1],simu[2],simu[3]
new_path=path+"/results_"+str(N_simu)
os.mkdir(new_path)
theta_phase='mixed'
target_on=True
# target_time = 500*msecond
start_scope()
close('all')
runtime=2*second
Vrev_inp=0*mV
taurinp=0.1*ms
taudinp=0.5*ms
tauinp=taudinp
Vhigh=0*mV
Vlow=-80*mV
NN=1 #multiplicative factor on the number of neurons
N_RS,N_FS,N_SI,N_IB= NN*80,NN*20,NN*20,NN*20 #Number of neurons of RE, TC, and HTC type
N_SI,N_RS_gran,N_SI_gran=20,20,20
N_RS_vis,N_FS_vis,N_RS_mot,N_SI_mot,N_dSI_vm,N_RS_vm,N_gSI_vm=[20]*7
all_SIdFSg=[2*msiemens * cm **-2] #1
all_FSgRSg=[1* msiemens * cm **-2]
all_RSgFSg=[1*msiemens * cm **-2]
all_RSgRSg=[0.5*msiemens * cm **-2]
all_FSgFSg=[0.3* msiemens * cm **-2]
all_RSgRSs=[2*msiemens * cm **-2]
all_RSgFSs=[0.1*msiemens * cm **-2]
all_FSgRSs=[0.1* msiemens * cm **-2]
all_J_RSg=['15 * uA * cmeter ** -2']
all_J_FSg=['-5 * uA * cmeter ** -2']
all_thal=[10* msiemens * cm **-2]
thal=all_thal[0]
all_syn_cond=list(product(all_SIdFSg,all_FSgRSg,all_RSgFSg,all_RSgRSg,all_FSgFSg,all_RSgRSs,all_RSgFSs,all_FSgRSs))
all_J=list(product(all_J_RSg,all_J_FSg))
syn_cond=all_syn_cond[0]
J=all_J[0]
if theta_phase=='bad':
input_beta2_IB=False
input_beta2_RS=False
input_beta2_FS_SI=True
input_thalamus_gran=True
gFS=0* msiemens * cm **-2
ginp_SI=0* msiemens * cm **-2
ginpSIdeep=0* msiemens * cm **-2
thal_cond=2* msiemens * cm **-2
kainate='low'
if theta_phase=='good':
# input_beta2_IB=True
input_beta2_IB=False
ginp_IB=500* msiemens * cm **-2
ginpSIdeep=500* msiemens * cm **-2
input_beta2_RS=False
input_beta2_FS_SI=False
input_thalamus_gran=True
thal_cond=thal
kainate='low'
if theta_phase=='mixed':
input_mixed=True
ginp_IB=500* msiemens * cm **-2
ginpSIdeep=500* msiemens * cm **-2
input_beta2_IB=False
input_beta2_RS=False
input_beta2_RS=False
input_beta2_FS_SI=False
input_thalamus_gran=False
kainate='low'
print('Network setup')
net=Network()
all_neurons_FEF,all_synapses_FEF,all_monitors_FEF=create_FEF_full2(N_RS_vis,N_FS_vis,N_RS_mot,N_dSI_vm,N_RS_vm,N_gSI_vm,t_SI,theta_phase,target_on,runtime,target_time)
R8,R9,R10,V_RS,V_FS,V_SI,R11,R12,R13,R14,mon_RS=all_monitors_FEF
RSvm_FEF,SIvm_FEF,RSv_FEF,SIv_FEF,VIPv_FEF=all_neurons_FEF[1],all_neurons_FEF[2],all_neurons_FEF[6],all_neurons_FEF[9],all_neurons_FEF[8]
all_neurons_LIP, all_synapses_LIP, all_gap_junctions_LIP, all_monitors_LIP=make_full_network(syn_cond,J,thal,t_FS,theta_phase)
V1,V2,V3,R1,R2,R3,I1,I2,I3,V4,R4,I4s,I4a,I4ad,I4bd,R5,R6,R7,V5,V6,V7,inpmon,inpIBmon=all_monitors_LIP
RS_sup_LIP,IB_LIP,SI_deep_LIP=all_neurons_LIP[0],all_neurons_LIP[5],all_neurons_LIP[9]
RS_gran_LIP,FS_gran_LIP=all_neurons_LIP[7],all_neurons_LIP[8]
IB_LIP.ginp_IB=0* msiemens * cm **-2 #the input to RS_sup_LIP is provided with synapses from FEF
SI_deep_LIP.ginp_SI=0* msiemens * cm **-2
# RSvm_FEF.ginp_RS=0* msiemens * cm **-2
SIvm_FEF.ginp_SI=0* msiemens * cm **-2
RSv_FEF.ginp_RS=0* msiemens * cm **-2
SIv_FEF.ginp_SI=0* msiemens * cm **-2
VIPv_FEF.ginp_VIP_good=0* msiemens * cm **-2
VIPv_FEF.ginp_VIP_bad=0* msiemens * cm **-2
if theta_phase=='good':
VIP_FEF=all_neurons_FEF[0]
VIP_FEF.ginp_VIP_good=10* msiemens * cm **-2
RS_gran_LIP.ginp_RS_good=15* msiemens * cm **-2
FS_gran_LIP.ginp_FS_good=15* msiemens * cm **-2
VIP_FEF.ginp_VIP_bad=10* msiemens * cm **-2
RS_gran_LIP.ginp_RS_bad=15* msiemens * cm **-2
FS_gran_LIP.ginp_FS_bad=15* msiemens * cm **-2
if theta_phase=='mixed':
VIP_FEF=all_neurons_FEF[0]
VIP_FEF.ginp_VIP_good=10* msiemens * cm **-2
RS_gran_LIP.ginp_RS_good=5* msiemens * cm **-2
FS_gran_LIP.ginp_FS_good=5* msiemens * cm **-2
RS_gran_LIP.ginp_RS_bad=5* msiemens * cm **-2
FS_gran_LIP.ginp_FS_bad=5* msiemens * cm **-2
VIP_FEF.ginp_VIP_bad=10* msiemens * cm **-2
net.add(all_neurons_FEF)
net.add(all_synapses_FEF)
net.add(all_monitors_FEF)
net.add(all_neurons_LIP)
net.add(all_synapses_LIP)
net.add(all_gap_junctions_LIP)
net.add(all_monitors_LIP)
S_FEF_IB_LIP=generate_syn(RSvm_FEF,IB_LIP,'Isyn_FEF','',0*msiemens * cm **-2,0.125*ms,1*ms,0*mV)
S_FEF_SIdeep_LIP=generate_syn(RSvm_FEF,SI_deep_LIP,'Isyn_FEF','',0.05*msiemens * cm **-2,0.125*ms,1*ms,0*mV)
S_LIP_RS_FEF=generate_syn(RS_sup_LIP,RSvm_FEF,'Isyn_LIP','',0.009*msiemens * cm **-2,0.125*ms,1*ms,0*mV)
S_LIP_FS_FEF=generate_syn(RS_sup_LIP,SIvm_FEF,'Isyn_LIP','',0.009*msiemens * cm **-2,0.125*ms,1*ms,0*mV)
S_LIP_RSv_FEF=generate_syn(RS_sup_LIP,RSv_FEF,'Isyn_LIP','',0.025*msiemens * cm **-2,0.125*ms,1*ms,0*mV)
S_LIP_SIv_FEF=generate_syn(RS_sup_LIP,SIv_FEF,'Isyn_LIP','',0.025*msiemens * cm **-2,0.125*ms,1*ms,0*mV)
S_LIP_VIPv_FEF=generate_syn(RS_sup_LIP,VIPv_FEF,'Isyn_LIP','',0.005*msiemens * cm **-2,0.125*ms,1*ms,0*mV)
RSv_FEF.ginp_RS2=2.5* msiemens * cm **-2
SIv_FEF.ginp_SI2=2.5* msiemens * cm **-2
VIPv_FEF.ginp_VIP2=2.5* msiemens * cm **-2
net.add(S_FEF_IB_LIP)
net.add(S_FEF_SIdeep_LIP)
net.add(S_LIP_RS_FEF)
net.add(S_LIP_FS_FEF)
net.add(S_LIP_RSv_FEF)
net.add(S_LIP_SIv_FEF)
net.add(S_LIP_VIPv_FEF)
print('Compiling with cython')
prefs.codegen.target = 'cython' #cython=faster, numpy = default python
# defaultclock.dt = 0.01*ms
net.run(runtime,report='text',report_period=300*second)
save_raster('LIP RS',R1.i,R1.t,new_path)
save_raster('LIP FS',R2.i,R2.t,new_path)
save_raster('LIP SI',R3.i,R3.t,new_path)
save_raster('LIP IB',R4.i,R4.t,new_path)
save_raster('LIP RS gran',R5.i,R5.t,new_path)
save_raster('LIP FS gran',R6.i,R6.t,new_path)
save_raster('LIP SI deep',R7.i,R7.t,new_path)
save_raster('FEF RS vm',R8.i,R8.t,new_path)
save_raster('FEF SI2 vm',R9.i,R9.t,new_path)
save_raster('FEF SI1 vm',R10.i,R10.t,new_path)
save_raster('FEF RS v',R11.i,R11.t,new_path)
save_raster('FEF FS v',R12.i,R12.t,new_path)
save_raster('FEF VIP v',R13.i,R13.t,new_path)
save_raster('FEF SI v',R14.i,R14.t,new_path)
save_raster('FEF RS m',mon_RS.i,mon_RS.t,new_path)
# clear_cache('cython')
if __name__=='__main__':
path=""
if os.name == 'nt':
path=os.path.join(ntpath.dirname(os.path.abspath(__file__)),"results_"+str(datetime.datetime.now()).replace(':','-'))
else :
path="./results_"+str(datetime.datetime.now())
os.mkdir(path)
N=50
liste_target_time=[350*msecond,450*msecond,550*msecond,650*msecond,750*msecond,850*msecond,950*msecond,1050*msecond,1150*msecond,1250*msecond,1350*msecond,1450*msecond,1550*msecond,1650*msecond]
liste_simus=[]
for t in liste_target_time:
liste_simus+=[t]*N
liste_simus=[[liste_simus[i],i+750] for i in range(len(liste_simus))]
simus_pas_faites=list(range(700))
order=[50*i for i in range(len(liste_target_time))]
for ind in range(1,50):
order+=[50*i+ind for i in range(len(liste_target_time))]
liste_simus=[liste_simus[i] for i in order if i in simus_pas_faites]
liste_simus.reverse()
print(liste_simus)
print('Number of simulations: '+str(len(liste_simus)))
for simu in liste_simus:
FEF_and_LIP(simu,path)
clear_cache('cython')
# clear_cache('cython')