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LIP_full.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Dec 2 14:28:44 2019
@author: aaussel
"""
#import matplotlib
#matplotlib.use('Agg')
from brian2 import *
start_scope()
from scipy import signal
from cells.RS_LIP import *
from cells.FS_LIP import *
from cells.SI_LIP import *
from cells.IB_soma_LIP import *
from cells.IB_axon_LIP import *
from cells.IB_apical_dendrite_LIP import *
from cells.IB_basal_dendrite_LIP import *
from LIP_superficial_layer import *
from LIP_beta1 import *
#from joblib import Parallel, delayed
#from joblib import parallel_backend
#import multiprocessing
import os
os.environ['MKL_NUM_THREADS'] = '1'
os.environ['OMP_NUM_THREADS'] = '1'
os.environ['MKL_DYNAMIC'] = 'FALSE'
import time
from itertools import *
##Custom
#input_beta2_IB=False
#input_beta2_RS=False
#input_beta2_FS_SI=False
#input_thalamus_gran=False
#thal_cond=8* msiemens * cm **-2
#kainate='low'
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 make_full_network(syn_cond,J,thal,theta_phase):
# print(syn_cond,J,thal,theta_phase)
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
gSIdFSg,gFSgRSg,gRSgFSg,gRSgRSg,gFSgFSg,gRSgRSs,gRSgFSs,gFSgRSs=syn_cond
J_RSg,J_FSg=J
FLee=(0.05*mS/cm**2)/(0.4*uS/cm**2)*0.5
version = 'Alex'
runtime=3*second
kainate='low'
all_neurons, all_synapses, all_gap_junctions, all_monitors=create_Mark_Alex_network(kainate,version,Nf=NN)
V1,V2,V3,R1,R2,R3,I1,I2,I3,V4,R4,I4s,I4a,I4ad,I4bd=all_monitors
RS, FS, SI, IB_soma,IB_axon,IB_bd,IB_ad =all_neurons
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
SI.ginp_SI=0* msiemens * cm **-2
thal_cond=2* msiemens * cm **-2
input_mixed=False
if theta_phase=='good':
input_beta2_IB=True
IB_bd.ginp_IB=500* msiemens * cm **-2
input_beta2_RS=False
input_beta2_FS_SI=False
input_thalamus_gran=True
thal_cond=thal
# thal_cond=thal#*2754.660086037123/12782.0904181147
# thal_cond=thal*2754.660086037123/139.46773954954165
input_mixed=False
if theta_phase=='mixed':
input_mixed=True
IB_bd.ginp_IB=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
thal_cond=thal
# print(input_mixed,input_beta2_IB,input_beta2_RS,input_beta2_FS_SI,input_thalamus_gran)
prefs.codegen.target = 'numpy'
defaultclock.dt = 0.01*ms
#Single column network
##Define neuron groups
E_gran=NeuronGroup(N_FS,eq_RS_LIP,threshold='V>-20*mvolt',refractory=3*ms,method='rk4')
E_gran.V = '-70*mvolt+10*rand()*mvolt'
E_gran.h = '0+0.05*rand()'
E_gran.m = '0+0.05*rand()'
E_gran.mAR = '0.035+0.025*rand()'
if kainate=='low':
# E_gran.J='1 * uA * cmeter ** -2' #article SI=25, code=1
E_gran.J=J_RSg
elif kainate=='high':
E_gran.J='-10 * uA * cmeter ** -2' #article SI=25, code=1
FS_gran=NeuronGroup(N_FS,eq_FS_LIP,threshold='V>-20*mvolt',refractory=3*ms,method='rk4')
FS_gran.V = '-110*mvolt+10*rand()*mvolt'
FS_gran.h = '0+0.05*rand()'
FS_gran.m = '0+0.05*rand()'
if kainate=='low':
# FS_gran.J='5 * uA * cmeter ** -2' #article=code=35
FS_gran.J=J_FSg
elif kainate=='high':
FS_gran.J='16 * uA * cmeter ** -2'
SI_deep=NeuronGroup(N_SI,eq_SI_LIP,threshold='V>-20*mvolt',refractory=3*ms,method='rk4')
SI_deep.V = '-100*mvolt+10*rand()*mvolt'
SI_deep.h = '0+0.05*rand()'
SI_deep.m = '0+0.05*rand()'
SI_deep.mAR = '0.02+0.04*rand()'
if version == 'Alex':
if kainate=='low':
SI_deep.J='35* uA * cmeter ** -2' #article SI=50, code=35, Mark = 45
elif kainate=='high':
SI_deep.J='30* uA * cmeter ** -2' #article SI=50, code=35, Mark = 45
elif version=='Mark':
if kainate=='low':
SI_deep.J='45* uA * cmeter ** -2' #article SI=50, code=35, Mark = 45
elif kainate=='high':
SI_deep.J='40* uA * cmeter ** -2' #article SI=50, code=35, Mark = 45
if theta_phase=='good' or theta_phase=='mixed':
# SI_deep.ginp_SI=50* msiemens * cm **-2
SI_deep.ginp_SI=5* msiemens * cm **-2
# SI_deep.ginp_SI=0* msiemens * cm **-2
Vlow=-80*mV
SI_deep.Vinp=Vlow
##Synapses
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
'''
def generate_syn(source,target,syntype,connection_pattern,g_i,taur_i,taud_i,V_i):
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
#From E (granular layer) cells
#S_EgranFS=generate_syn(E_gran,FS,'IsynEgran','',0.2*usiemens * cm **-2*FLee,0.125*ms,1*ms,0*mV)
#S_EgranFS=generate_syn(E_gran,FS,'IsynEgran','',0.1*msiemens * cm **-2,0.125*ms,1*ms,0*mV)
S_EgranFS=generate_syn(E_gran,FS,'IsynRS_LIP_gran','',gRSgFSs,0.125*ms,1*ms,0*mV)
#S_EgranEgran=generate_syn(E_gran,E_gran,'IsynEgran','',0.4*usiemens * cm **-2*FLee,0.125*ms,1*ms,0*mV)
S_EgranEgran=generate_syn(E_gran,E_gran,'IsynRS_LIP_gran','',gRSgRSg,0.125*ms,1*ms,0*mV)
#S_EgranFSgran=generate_syn(E_gran,FS_gran,'IsynEgran','',0.2*usiemens * cm **-2*FLee,0.125*ms,1*ms,0*mV)
S_EgranFSgran=generate_syn(E_gran,FS_gran,'IsynRS_LIP_gran','',gRSgFSg,0.125*ms,1*ms,0*mV)
#S_EgranRS=generate_syn(E_gran,RS,'IsynEgran','',0.2*usiemens * cm **-2*FLee,0.125*ms,1*ms,0*mV)
#S_EgranRS=generate_syn(E_gran,RS,'IsynEgran','',1*msiemens * cm **-2,0.125*ms,1*ms,0*mV)
S_EgranRS=generate_syn(E_gran,RS,'IsynRS_LIP_gran','',gRSgRSs,0.125*ms,1*ms,0*mV)
S_EgranIB=generate_syn(E_gran,IB_ad,'IsynRS_LIP_gran','',0.212*usiemens * cm **-2*FLee,0.125*ms,1*ms,0*mV)
#From FS (granular layer) cells, normal timescale
#S_FSgranEgran=generate_syn(FS_gran,E_gran,'IsynFSgran','',1* usiemens * cm **-2*FLee,0.25*ms,5*ms,-80*mV)
S_FSgranEgran=generate_syn(FS_gran,E_gran,'IsynFS_LIP_gran','',gFSgRSg,0.25*ms,5*ms,-80*mV)
S_FSgranFSgran=generate_syn(FS_gran,FS_gran,'IsynFS_LIP_gran','',gFSgFSg,0.25*ms,5*ms,-75*mV)
#S_FSgranRS=generate_syn(FS_gran,RS,'IsynFSgran','',0.02* usiemens * cm **-2*FLee,0.25*ms,5*ms,-80*mV)
S_FSgranRS=generate_syn(FS_gran,RS,'IsynFS_LIP_gran','',gFSgRSs,0.25*ms,5*ms,-80*mV)
#S_FSgranRS=generate_syn(FS_gran,RS,'IsynFSgran','',0* msiemens * cm **-2,0.25*ms,5*ms,-80*mV)
# #From FS (granular layer) cells, beta timescale
# S_FSgranEgran=generate_syn(FS_gran,E_gran,'IsynFS_LIP_gran','',gFSgRSg,0.25*ms,20*ms,-80*mV)
# S_FSgranFSgran=generate_syn(FS_gran,FS_gran,'IsynFS_LIP_gran','',gFSgFSg,0.25*ms,20*ms,-75*mV)
# S_FSgranRS=generate_syn(FS_gran,RS,'IsynFS_LIP_gran','',gFSgRSs,0.25*ms,20*ms,-80*mV)
#From IB cells
#S_IBSIdeep=generate_syn(IB_axon,SI_deep,'IsynIB','',0.12* usiemens * cm **-2*FLee,0.125*ms,1*ms,0*mV)
# S_IBSIdeep=generate_syn(IB_axon,SI_deep,'IsynIB','',0.2* msiemens * cm **-2,0.125*ms,1*ms,0*mV)
S_IBSIdeep=generate_syn(IB_axon,SI_deep,'IsynIB_LIP','',0.01* msiemens * cm **-2,0.125*ms,1*ms,0*mV)
#From deep SI cells
S_SIdeepIB=generate_syn(SI_deep,IB_bd,'IsynSI_LIP_deep','',10* msiemens * cm **-2,0.25*ms,20*ms,-80*mV)
#S_SIdeepFSgran=generate_syn(SI_deep,FS_gran,'IsynSIdeep','',0.4* usiemens * cm **-2*FLee,0.25*ms,20*ms,-80*mV)
S_SIdeepFSgran=generate_syn(SI_deep,FS_gran,'IsynSI_LIP_deep','',gSIdFSg,0.25*ms,20*ms,-80*mV)
def generate_spike_timing(N,f,start_time,end_time=runtime):
list_time_and_i=[]
for i in range(N):
list_time=[(start_time,i)]
next_spike=list_time[-1][0]+(1+0.001*rand())/f
while next_spike<end_time:
list_time.append((next_spike,i))
next_spike=list_time[-1][0]+(1+0.001*rand())/f
list_time_and_i+=list_time
return array(list_time_and_i)
def generate_spike_timing_theta(N,f,start_time,end_time=runtime,f_theta=4*Hz):
list_time_and_i=[]
for i in range(N):
list_time=[(start_time,i)]
# next_spike=list_time[-1][0]+(1+0.1*rand())/f
next_spike=list_time[-1][0]+1/f
while next_spike<end_time:
if int(sin(2*pi*next_spike*f_theta)>0)==1:
list_time.append((next_spike,i))
next_spike=next_spike+1/f
list_time_and_i+=list_time
return array(list_time_and_i)
G_topdown,G_topdown2,G_topdown3,G_lateral,G_lateral2,Poisson_input,Poisson_input2=[None]*7
topdown_in,topdown_in2,topdown_in3,lateral_in,lateral_in2,bottomup_in,bottomup_in2=[None]*7
if input_beta2_IB:
# inputs_topdown=generate_spike_timing(N_IB,25*Hz,0*ms,end_time=3000*ms)
# G_topdown = SpikeGeneratorGroup(N_IB, inputs_topdown[:,1], inputs_topdown[:,0]*second)
# topdown_in=Synapses(G_topdown,IB_bd,on_pre='Vinp=Vhigh')
# topdown_in.connect(j='i')
# inputs_topdown3=generate_spike_timing(N_SI,50*Hz,0*ms,end_time=8000*ms)
inputs_topdown3=generate_spike_timing(N_SI,25*Hz,0*ms,end_time=10000*ms)
G_topdown3 = SpikeGeneratorGroup(N_SI, inputs_topdown3[:,1], inputs_topdown3[:,0]*second)
topdown_in3=Synapses(G_topdown3,SI_deep,on_pre='Vinp=Vhigh')
topdown_in3.connect(j='i')
if input_beta2_RS:
RS.ginp_RS_good=4* msiemens * cm **-2
RS.ginp_RS_bad=4* msiemens * cm **-2
inputs_topdown2=generate_spike_timing(N_RS,25*Hz,0*ms,end_time=10000*ms)
G_topdown2 = SpikeGeneratorGroup(N_RS, inputs_topdown2[:,1], inputs_topdown2[:,0]*second)
topdown_in2=Synapses(G_topdown2,RS,on_pre='Vinp=Vhigh')
topdown_in2.connect(j='i')
if input_beta2_FS_SI:
FS.ginp_FS_good=gFS
FS.ginp_FS_bad=gFS
inputs_lateral=generate_spike_timing(N_FS,25*Hz,0*ms,end_time=10000*ms)
G_lateral = SpikeGeneratorGroup(N_FS, inputs_lateral[:,1], inputs_lateral[:,0]*second)
lateral_in=Synapses(G_lateral,FS,on_pre='Vinp=Vhigh')
lateral_in.connect(j='i')
inputs_lateral2=generate_spike_timing(N_SI,25*Hz,0*ms,end_time=10000*ms)
G_lateral2 = SpikeGeneratorGroup(N_SI, inputs_lateral2[:,1], inputs_lateral2[:,0]*second)
lateral_in2=Synapses(G_lateral2,SI,on_pre='Vinp=Vhigh')
lateral_in2.connect(j='i')
if input_thalamus_gran:
E_gran.ginp_RS_good=thal_cond
FS_gran.ginp_FS_good=thal_cond
E_gran.ginp_RS_bad=thal_cond
FS_gran.ginp_FS_bad=thal_cond
# print(E_gran.ginp_RS_good,FS_gran.ginp_FS_good,E_gran.ginp_RS_bad,FS_gran.ginp_FS_bad)
# FS_gran.ginp_FS=thal_cond
inputs_mdpul=generate_spike_timing(N_FS,13*Hz,0*ms,end_time=10000*ms)
Poisson_input = SpikeGeneratorGroup(N_FS, inputs_mdpul[:,1], inputs_mdpul[:,0]*second)
# Poisson_input = PoissonGroup(N_FS,100*Hz)
bottomup_in = Synapses(Poisson_input,FS_gran, on_pre='Vinp=Vhigh')
bottomup_in.connect(j='i')
# E_gran.ginp_RS=thal_cond
Poisson_input2 = SpikeGeneratorGroup(N_FS, inputs_mdpul[:,1], inputs_mdpul[:,0]*second)
# Poisson_input2 = PoissonGroup(N_FS,100*Hz)
bottomup_in2 = Synapses(Poisson_input2,E_gran, on_pre='Vinp=Vhigh')
bottomup_in2.connect(j='i')
# print(bottomup_in,bottomup_in2)
if input_mixed:
E_gran.ginp_RS_good=thal_cond
FS_gran.ginp_FS_good=thal_cond
# E_gran.ginp_RS_bad=2* msiemens * cm **-2
# FS_gran.ginp_FS_bad=2* msiemens * cm **-2
E_gran.ginp_RS_bad=thal_cond
FS_gran.ginp_FS_bad=thal_cond
# FS_gran.ginp_FS='15* msiemens * cm **-2 * int(sin(2*pi*t*4*Hz)>0) + 2* msiemens * cm **-2 * int(sin(2*pi*t*4*Hz)<0)'
# inputs_mdpul=generate_spike_timing_theta(N_FS,13*Hz,0*ms,end_time=3000*ms)
#4Hz theta
t0=0*ms
t1=125*ms
inputs_mdpul=generate_spike_timing(N_SI,13*Hz,t0,end_time=t1)
while t0+250*ms<runtime:
t0,t1=t0+250*ms,t1+250*ms
inputs_mdpul=vstack((inputs_mdpul,generate_spike_timing(N_SI,13*Hz,t0,end_time=t1)))
# #8Hz theta
# t0=0*ms
# t1=62.5*ms
# inputs_mdpul=generate_spike_timing(N_SI,13*Hz,t0,end_time=t1)
# while t0+125*ms<runtime:
# t0,t1=t0+125*ms,t1+125*ms
# inputs_mdpul=vstack((inputs_mdpul,generate_spike_timing(N_SI,13*Hz,t0,end_time=t1)))
# #2Hz theta
# t0=0*ms
# t1=250*ms
# inputs_mdpul=generate_spike_timing(N_SI,13*Hz,t0,end_time=t1)
# while t0+500*ms<runtime:
# t0,t1=t0+500*ms,t1+500*ms
# inputs_mdpul=vstack((inputs_mdpul,generate_spike_timing(N_SI,13*Hz,t0,end_time=t1)))
# #16Hz theta
# t0=0*ms
# t1=31.25*ms
# inputs_mdpul=generate_spike_timing(N_SI,13*Hz,t0,end_time=t1)
# while t0+62.5*ms<runtime:
# t0,t1=t0+62.5*ms,t1+62.5*ms
# inputs_mdpul=vstack((inputs_mdpul,generate_spike_timing(N_SI,13*Hz,t0,end_time=t1)))
Poisson_input = SpikeGeneratorGroup(N_FS, inputs_mdpul[:,1], inputs_mdpul[:,0]*second)
# Poisson_input = PoissonGroup(N_FS,100*Hz)
bottomup_in = Synapses(Poisson_input,FS_gran, on_pre='Vinp=Vhigh')
bottomup_in.connect(j='i')
# E_gran.ginp_RS='15* msiemens * cm **-2 * int(sin(2*pi*t*4*Hz)>0) + 2* msiemens * cm **-2 * int(sin(2*pi*t*4*Hz)<0)'
Poisson_input2 = SpikeGeneratorGroup(N_FS, inputs_mdpul[:,1], inputs_mdpul[:,0]*second)
# Poisson_input2 = PoissonGroup(N_FS,100*Hz)
bottomup_in2 = Synapses(Poisson_input2,E_gran, on_pre='Vinp=Vhigh')
bottomup_in2.connect(j='i')
inputs_topdown=generate_spike_timing_theta(N_IB,25*Hz,0*ms,end_time=5100*ms)
G_topdown = SpikeGeneratorGroup(N_IB, inputs_topdown[:,1], inputs_topdown[:,0]*second)
topdown_in=Synapses(G_topdown,IB_bd,on_pre='Vinp=Vhigh')
topdown_in.connect(j='i')
#theta=4Hz
inputs_topdown3=generate_spike_timing_theta(N_SI,25*Hz,0*ms,end_time=5100*ms)
# #theta=8Hz
# inputs_topdown3=generate_spike_timing_theta(N_SI,25*Hz,0*ms,end_time=5100*ms,f_theta=8*Hz)
# #theta=2Hz
# inputs_topdown3=generate_spike_timing_theta(N_SI,25*Hz,0*ms,end_time=5100*ms,f_theta=2*Hz)
# #theta=16Hz
# inputs_topdown3=generate_spike_timing_theta(N_SI,25*Hz,0*ms,end_time=5100*ms,f_theta=16*Hz)
# inputs_topdown3=generate_spike_timing_theta(N_SI,50*Hz,0*ms,end_time=5100*ms)
G_topdown3 = SpikeGeneratorGroup(N_SI, inputs_topdown3[:,1], inputs_topdown3[:,0]*second)
topdown_in3=Synapses(G_topdown3,SI_deep,on_pre='Vinp=Vhigh')
topdown_in3.connect(j='i')
# g_inputs=[G_topdown,G_topdown2,G_topdown3,G_lateral,G_lateral2,Poisson_input,Poisson_input2]
# g_inputs=[y for y in g_inputs if y]
# syn_inputs=[topdown_in,topdown_in2,topdown_in3,lateral_in,lateral_in2,bottomup_in,bottomup_in2]
# syn_inputs=[y for y in syn_inputs if y]
numpy.set_printoptions(threshold=sys.maxsize)
# print(E_gran.ginp_RS_good)
# print(Poisson_input2)
g_inputs=[G_topdown2,G_topdown3,G_lateral,G_lateral2,Poisson_input,Poisson_input2]
g_inputs=[y for y in g_inputs if y]
syn_inputs=[topdown_in2,topdown_in3,lateral_in,lateral_in2,bottomup_in,bottomup_in2]
syn_inputs=[y for y in syn_inputs if y]
# print(len(g_inputs))
# print(len(syn_inputs))
# print(Poisson_input2 in g_inputs)
#Define monitors and run network :
R5=SpikeMonitor(E_gran,record=True)
R6=SpikeMonitor(FS_gran,record=True)
R7=SpikeMonitor(SI_deep,record=True)
V5=StateMonitor(E_gran,'V',record=True)
V6=StateMonitor(FS_gran,'V',record=True)
V7=StateMonitor(SI_deep,'V',record=True)
# inpmon=StateMonitor(E_gran,'Iinp1',record=True)
inpmon=StateMonitor(E_gran,'sinp',record=True)
#graninpmon=StateMonitor(FS,'IsynEgran',record=[0])
inpIBmon=StateMonitor(SI_deep,'Iapp',record=[0])
all_neurons=all_neurons+(E_gran,FS_gran,SI_deep)+tuple(g_inputs)
all_synapses=all_synapses+(S_EgranFS,S_EgranEgran,S_EgranFSgran,S_EgranRS,S_EgranIB,S_FSgranEgran,S_FSgranFSgran,S_FSgranRS,S_IBSIdeep,S_SIdeepIB,S_SIdeepFSgran)+tuple(syn_inputs)
all_monitors=all_monitors+(R5,R6,R7,V5,V6,V7,inpmon,inpIBmon)
return all_neurons, all_synapses, all_gap_junctions, all_monitors
def run_one_simulation(simu,path,index_var):
# print(simu,len(simu))
start_scope()
close('all')
runtime=1*second
# runtime=5*second
Vrev_inp=0*mV
taurinp=0.1*ms
taudinp=0.5*ms
tauinp=taudinp
Vhigh=0*mV
Vlow=-80*mV
ginp_IB=0* msiemens * cm **-2
ginp_SI=0* msiemens * cm **-2
ginp=0* msiemens * cm **-2
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
syn_cond,J,thal,theta_phase,index=simu
print('Simulation '+str(index))
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'
input_mixed=False
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'
input_mixed=False
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'
net = Network(collect())
print('Network setup')
all_neurons, all_synapses, all_gap_junctions, all_monitors=make_full_network(syn_cond,J,thal,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
net.add(all_neurons)
net.add(all_synapses)
net.add(all_gap_junctions)
# net.add(all_monitors)
net.add((V1,R1,R2,R3,R4,R5,R6,R7,inpmon,inpIBmon))
# taurinp=0.1*ms
# taudinp=0.5*ms
taurinp=2*ms
taudinp=10*ms
# taurinp=10*ms
# taudinp=50*ms
tauinp=taudinp
print('Compiling with cython')
prefs.codegen.target = 'cython' #cython=faster, numpy = default python
net.run(runtime,report='text',report_period=300*second)
figure()
plot(R1.t,R1.i+140,'r.',label='RS cells')
plot(R2.t,R2.i+120,'m.',label='FS cells')
plot(R3.t,R3.i+100,'y.',label='SI cells')
plot(R5.t,R5.i+70,'g.',label='Granular RS')
plot(R6.t,R6.i+50,'c.',label='Granular FS')
plot(R4.t,R4.i+20,'b.',label='IB cells')
plot(R7.t,R7.i,'k.',label='Deep SI')
xlim(0,runtime/second)
legend(loc='upper left')
figure()
# plot(inpmon.t,inpmon.Iinp1[0])
plot(inpmon.t,inpmon.sinp[0])
xlabel('Time (s)')
ylabel('Input synapse opening variable $s_{input}$')
tight_layout()
print(sum(inpmon.sinp[0]))
# print(inpmon.t)
# print(inpmon.Iinp1[0])
#
# print(inpIBmon.t)
# print(inpIBmon.Iapp[0])
figure()
plot(inpIBmon.t,inpIBmon.Iapp[0])
min_t=int(50*ms*100000*Hz)
# min_t=int(150*ms*100000*Hz)
LFP_V_RS=1/N_RS*sum(V1.V,axis=0)[min_t:]
# LFP_V_FS=1/N_FS*sum(V2.V,axis=0)[min_t:]
# LFP_V_SI=1/N_SI62.903225806451616*sum(V3.V,axis=0)[min_t:]
# LFP_V_IB=1/N_IB*sum(V4.V,axis=0)[min_t:]
# LFP_V_RSg=1/N_FS*sum(V5.V,axis=0)[min_t:]
# LFP_V_FSg=1/N_FS*sum(V6.V,axis=0)[min_t:]
# LFP_V_SId=1/N_SI*sum(V7.V,axis=0)[min_t:]
# f,Spectrum_LFP_V_RS=signal.periodogram(LFP_V_RS, 100000,'flattop', scaling='spectrum')
f,Spectrum_LFP_V_RS=signal.periodogram(LFP_V_RS, 100000,'flattop', scaling='density')
# f,Spectrum_LFP_V_FS=signal.periodogram(LFP_V_FS, 100000,'flattop', scaling='spectrum')
# f,Spectrum_LFP_V_SI=signal.periodogram(LFP_V_SI, 100000,'flattop', scaling='spectrum')
# f,Spectrum_LFP_V_IB=signal.periodogram(LFP_V_IB, 100000,'flattop', scaling='spectrum')
# f,Spectrum_LFP_V_RSg=signal.periodogram(LFP_V_RSg, 100000,'flattop', scaling='spectrum')
# f,Spectrum_LFP_V_FSg=signal.periodogram(LFP_V_FSg, 100000,'flattop', scaling='spectrum')
# f,Spectrum_LFP_V_SId=signal.periodogram(LFP_V_SId, 100000,'flattop', scaling='spectrum')
figure()
plot(LFP_V_RS)
# figure(figsize=(10,8))
# subplot(421)
# plot(f,Spectrum_LFP_V_RS)
# ylabel('Spectrum')
# yticks([],[])
# xlim(0,100)
# title('RS cell')
# subplot(422)
# plot(f,Spectrum_LFP_V_FS)
# yticks([],[])
# xlim(0,100)
# title('FS cell')
# subplot(423)
# plot(f,Spectrum_LFP_V_SI)
# ylabel('Spectrum')
# yticks([],[])
# xlim(0,100)
# title('SI cell')
# subplot(425)
# plot(f,Spectrum_LFP_V_RSg)
# ylabel('Spectrum')
# yticks([],[])
# xlim(0,100)
# title('gran RS cell')
# subplot(426)
# plot(f,Spectrum_LFP_V_FSg)
# yticks([],[])
# xlim(0,100)
# title('gran FS cell')
# subplot(427)
# plot(f,Spectrum_LFP_V_IB)
# xlabel('Frequency (Hz)')
# ylabel('Spectrum')
# yticks([],[])
# xlim(0,100)
# title('IB cell')
# subplot(428)
# plot(f,Spectrum_LFP_V_SId)
# yticks([],[])
# xlim(0,100)
# xlabel('Frequency (Hz)')
# title('deep SI cell')
# tight_layout()
figure()
plot(R1.t,R1.i+140,'r.',label='RS cells')
plot(R2.t,R2.i+120,'b.',label='FS cells')
plot(R3.t,R3.i+100,'g.',label='SI cells')
plot(R5.t,R5.i+70,'.',label='Granular RS',color='C1')
plot(R6.t,R6.i+50,'c.',label='Granular FS')
plot(R4.t,R4.i+20,'m.',label='IB cells')
plot(R7.t,R7.i,'.',label='Deep SI',color='lime')
xlim(0,runtime/second)
ylim(0,220)
legend(loc='upper left')
xlabel('Time (s)')
ylabel('Neuron index')
figure()
plot(f,Spectrum_LFP_V_RS)
ylabel('Spectrum')
xlabel('Frequency (Hz)')
xlim(0,100)
figure()
plot(f,Spectrum_LFP_V_RS)
ylabel('Spectrum')
xlabel('Frequency (Hz)')
xlim(0,50)
figure()
plot(R1.t,R1.i+140,'r.',label='RS cells')
plot(R2.t,R2.i+120,'b.',label='FS cells')
plot(R3.t,R3.i+100,'g.',label='SOM cells')
plot([0.2,runtime/second],[95,95],'k--')
plot(R5.t,R5.i+70,'r.')
plot(R6.t,R6.i+50,'b.')
plot([0.2,runtime/second],[45,45],'k--')
plot(R4.t,R4.i+20,'m.',label='IB cells')
plot(R7.t,R7.i,'g.')
xlim(0.2,runtime/second)
ylim(0,220)
# legend(loc='upper left')
xlabel('Time (s)')
ylabel('Neuron index')
# min_t=int(50*ms*100000*Hz)
# LFP_V_RS=1/N_RS*sum(V1.V,axis=0)[min_t:]
# LFP_V_FS=1/N_FS*sum(V2.V,axis=0)[min_t:]
# LFP_V_SI=1/N_SI*sum(V3.V,axis=0)[min_t:]
# LFP_V_IB=1/N_IB*sum(V4.V,axis=0)[min_t:]
#
# f,Spectrum_LFP_V_RS=signal.periodogram(LFP_V_RS, 100000,'flattop', scaling='spectrum')
# f,Spectrum_LFP_V_FS=signal.periodogram(LFP_V_FS, 100000,'flattop', scaling='spectrum')
# f,Spectrum_LFP_V_SI=signal.periodogram(LFP_V_SI, 100000,'flattop', scaling='spectrum')
# f,Spectrum_LFP_V_IB=signal.periodogram(LFP_V_IB, 100000,'flattop', scaling='spectrum')
#
# figure()
# subplot(421)
# plot((V1.t/second)[min_t:],LFP_V_RS)
# ylabel('LFP')
# title('RS cell')
# subplot(423)
# plot((V1.t/second)[min_t:],LFP_V_FS)
# ylabel('LFP')
# title('FS cell')
# subplot(425)
# plot((V1.t/second)[min_t:],LFP_V_SI)
# ylabel('LFP')
# title('SI cell')
# subplot(427)
# plot((V1.t/second)[min_t:],LFP_V_IB)
# xlabel('Time (s)')
# ylabel('LFP')
# title('IB cell')
#
# subplot(422)
# plot(f,Spectrum_LFP_V_RS)
# ylabel('Spectrum')
# yticks([],[])
# xlim(0,100)
# title('RS cell')
# subplot(424)
# plot(f,Spectrum_LFP_V_FS)
# ylabel('Spectrum')
# yticks([],[])
# xlim(0,100)
# title('FS cell')
# subplot(426)
# plot(f,Spectrum_LFP_V_SI)
# ylabel('Spectrum')
# yticks([],[])
# xlim(0,100)
# title('SI cell')
# subplot(428)
# plot(f,Spectrum_LFP_V_IB)
# xlabel('Frequency (Hz)')
# ylabel('Spectrum')
# yticks([],[])
# xlim(0,100)
# title('IB cell')
##save figures
new_path=path+"/results_"+str(index)
os.mkdir(new_path)
for n in get_fignums():
current_fig=figure(n)
current_fig.savefig(new_path+'/figure'+str(n)+'.png')
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_RS_gran',R5.i,R5.t,new_path)
save_raster('LIP_FS_gran',R6.i,R6.t,new_path)
save_raster('LIP_IB',R4.i,R4.t,new_path)
save_raster('LIP_SI_deep',R7.i,R7.t,new_path)
if __name__=='__main__':
Vlow=-80*mV
FLee=(0.05*mS/cm**2)/(0.4*uS/cm**2)*0.5
all_SIdFSg=[2*msiemens * cm **-2]
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=['10 * uA * cmeter ** -2']
all_J_FSg=['-5 * uA * cmeter ** -2']
## A
# all_J_RSg=['-5 * uA * cmeter ** -2'] #['20 * uA * cmeter ** -2']
# all_J_FSg=['-10 * uA * cmeter ** -2']
## B
# all_J_RSg=['-5 * uA * cmeter ** -2'] #['20 * uA * cmeter ** -2']
# all_J_FSg=['5 * uA * cmeter ** -2']
## C
# all_J_RSg=['20 * uA * cmeter ** -2'] #['20 * uA * cmeter ** -2']
# all_J_FSg=['-5 * uA * cmeter ** -2']
# all_J_RSg=['-10 * uA * cmeter ** -2','-5 * uA * cmeter ** -2','0 * uA * cmeter ** -2','5 * uA * cmeter ** -2','10 * uA * cmeter ** -2','15 * uA * cmeter ** -2','20 * uA * cmeter ** -2']
# all_J_FSg=['-9 * uA * cmeter ** -2','-8 * uA * cmeter ** -2','-7 * uA * cmeter ** -2','-6 * uA * cmeter ** -2','1 * uA * cmeter ** -2','2 * uA * cmeter ** -2','3 * uA * cmeter ** -2','4 * uA * cmeter ** -2']
# all_thal=[10* msiemens * cm **-2]
all_thal=[0* msiemens * cm **-2]
# all_theta=['mixed']
all_theta=['mixed','mixed','mixed','mixed','mixed']
#FLee=(0.05*mS/cm**2)/(0.4*uS/cm**2)*0.5
#all_SIdFSg=[1*msiemens * cm **-2]
#all_FSgRSg=[1* msiemens * cm **-2]
#all_RSgFSg=[0.5*msiemens * cm **-2]
#all_RSgRSg=[0.3*msiemens * cm **-2]
#all_FSgFSg=[0.3* msiemens * cm **-2]
#all_RSgRSs=[0.25*msiemens * cm **-2]
#all_RSgFSs=[0.01*msiemens * cm **-2]
#all_FSgRSs=[0.01* msiemens * cm **-2]
#all_J_RSg=['30 * uA * cmeter ** -2']
#all_J_FSg=['30 * uA * cmeter ** -2']
#all_thal=[15* msiemens * cm **-2]
#all_theta=['good','bad']
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))
path="./results_"+str(datetime.datetime.now())
os.mkdir(path)
all_sim=list(product(all_syn_cond,all_J,all_thal,all_theta))
index_var=[-1]
all_sim=[list(all_sim[i])+[i] for i in range(len(all_sim))]
#saving simulation parameters as a txt file
param_file=open(path+'/parameters.txt','w')
for simu in all_sim:
param_file.write(str(simu))
param_file.write('\n\n')
param_file.close()
#close('all')
#all_results=[]
#num_cores = multiprocessing.cpu_count()
#num_cores = 2
#with parallel_backend('multiprocessing'):
# Parallel(n_jobs=num_cores)(delayed(run_one_simulation)(simu,path,index_var) for simu in all_sim)
for simu in all_sim:
run_one_simulation(simu,path,index_var)
clear_cache('cython')