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Copy pathFEF_visuomotor_altd.py
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FEF_visuomotor_altd.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Dec 2 14:28:44 2019
@author: aaussel
"""
from brian2 import *
from scipy import signal
from cells.RS_FEF import *
from cells.FS_FEF import *
from cells.SI_FEF import *
from cells.VIP_FEF_altd import *
def generate_deepSI_and_gran_layers(theta_phase,N_SI,N_RS_gran,N_SI_gran,runtime):
if theta_phase=='bad':
ginp_IB=0* msiemens * cm **-2
input_beta2_RS=False
input_beta2_FS_SI=True
input_thalamus_gran=True
gFS=0* msiemens * cm **-2
thal_cond=3* msiemens * cm **-2
# thal_cond=10* msiemens * cm **-2
kainate='low'
if theta_phase=='good' or theta_phase=='mixed':
ginp_IB=10* msiemens * cm **-2
input_beta2_RS=False
input_beta2_FS_SI=False
input_thalamus_gran=True
thal_cond=3* msiemens * cm **-2
# thal_cond=10* msiemens * cm **-2
kainate='low'
prefs.codegen.target = 'numpy'
defaultclock.dt = 0.01*ms
#Single column network
##Define neuron groups
E_gran=NeuronGroup(N_RS_gran,eq_RS_FEF,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()'
# E_gran.J='30 * uA * cmeter ** -2' #article SI=25, code=1
# E_gran.J='20 * uA * cmeter ** -2' #article SI=25, code=1
E_gran.J='10 * uA * cmeter ** -2' #article SI=25, code=1
#0
SI_gran=NeuronGroup(N_SI_gran,eq_SI_FEF,threshold='V>-20*mvolt',refractory=3*ms,method='rk4')
SI_gran.V = '-110*mvolt+10*rand()*mvolt'
SI_gran.h = '0+0.05*rand()'
SI_gran.m = '0+0.05*rand()'
SI_gran.J='5 * uA * cmeter ** -2' #article=code=35
SI_gran.J='0 * uA * cmeter ** -2' #article=code=35
#-30
# SI_deep=NeuronGroup(N_SI,eq_SIdeep,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()'
# SI_deep.J='35* uA * cmeter ** -2' #article SI=50, code=35, Mark = 45
SI_deep=NeuronGroup(N_SI,eq_VIP,threshold='V>-20*mvolt',refractory=3*ms,method='rk4')
SI_deep.V = '-63*mvolt'
SI_deep.Iapp='0 * uA * cmeter ** -2'
##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_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,'IsynEgran','',1/160*msiemens * cm **-2,0.125*ms,1*ms,0*mV)
S_EgranEgran=generate_syn(E_gran,E_gran,'IsynRS_FEF_VM','',0.6*msiemens * cm **-2,0.125*ms,1*ms,0*mV) #0.4
#S_EgranFSgran=generate_syn(E_gran,SI_gran,'IsynEgran','',0.2*usiemens * cm **-2*FLee,0.125*ms,1*ms,0*mV)
S_EgranFSgran=generate_syn(E_gran,SI_gran,'IsynRS_FEF_VM','',0.5*msiemens * cm **-2,0.125*ms,1*ms,0*mV) #0.6
#S_EgranRS=generate_syn(E_gran,RS,'IsynEgran','',0.2*usiemens * cm **-2*FLee,0.125*ms,1*ms,0*mV)
#From FS (granular layer) cells
#S_FSgranEgran=generate_syn(SI_gran,E_gran,'IsynFSgran','',1* usiemens * cm **-2*FLee,0.25*ms,5*ms,-80*mV)
#S_FSgranEgran=generate_syn(SI_gran,E_gran,'IsynSI_FEF_VM','',0.6* msiemens * cm **-2,0.25*ms,20*ms,-80*mV)
S_FSgranEgran=generate_syn(SI_gran,E_gran,'IsynSI_FEF_VM','',0.5*msiemens * cm **-2,0.25*ms,20*ms,-80*mV) #0.35
#S_FSgranFSgran=generate_syn(SI_gran,SI_gran,'IsynFSgran','',0.1* usiemens * cm **-2*FLee,0.25*ms,5*ms,-75*mV)
S_FSgranFSgran=generate_syn(SI_gran,SI_gran,'IsynSI_FEF_VM','',0.2* msiemens * cm **-2,0.25*ms,20*ms,-75*mV) #1
#From deep SI cells
#S_SIdeepFSgran=generate_syn(SI_deep,SI_gran,'IsynSIdeep','',0.4* usiemens * cm **-2*FLee,0.25*ms,20*ms,-80*mV)
# S_SIdeepFSgran=generate_syn(SI_deep,SI_gran,'IsynSI2_FEF_VM','',1*msiemens * cm **-2,0.25*ms,20*ms,-80*mV)
S_SIdeepFSgran=generate_syn(SI_deep,SI_gran,'IsynSI2_FEF_VM','',1*msiemens * cm **-2,0.25*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+0.01*rand())/f
while next_spike<end_time:
list_time.append((next_spike,i))
next_spike=list_time[-1][0]+(1+0.01*rand())/f
list_time_and_i+=list_time
return array(list_time_and_i)
if theta_phase=='good':
SI_deep.ginp_VIP_good=ginp_IB
SI_deep.ginp_VIP_bad=ginp_IB
elif theta_phase=='mixed':
SI_deep.ginp_VIP_good='ginp_IB*(0.5+i*0.05)'
SI_deep.ginp_VIP_bad='ginp_IB*(0.5+i*0.05)'
fIB=13*Hz
inputs_topdown3=generate_spike_timing(N_SI,fIB,0*ms,end_time=3000*ms)
# if theta_phase=='mixed':
# t0=0*ms
# t1=125*ms
# inputs_topdown3=generate_spike_timing(N_SI,fIB,t0,end_time=t1)
# while t0+250*ms<runtime:
# t0,t1=t0+250*ms,t1+250*ms
# inputs_topdown3=vstack((inputs_topdown3,generate_spike_timing(N_SI,fIB,t0,end_time=t1)))
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=4* msiemens * cm **-2
# inputs_topdown2=generate_spike_timing(N_RS,25*Hz,0*ms,end_time=2100*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=gFS
# inputs_lateral=generate_spike_timing(N_FS,25*Hz,0*ms,end_time=2100*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=2100*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:
SI_gran.ginp_SI=thal_cond
E_gran.ginp_RS=thal_cond
# SI_gran.ginp_FS=thal_cond
# Poisson_input = PoissonGroup(N_SI_gran,100*Hz)
# bottomup_in = Synapses(Poisson_input,SI_gran, on_pre='Vinp=Vhigh')
# bottomup_in.connect(j='i')
#
# Poisson_input2 = PoissonGroup(N_RS_gran,100*Hz)
# bottomup_in2 = Synapses(Poisson_input2,E_gran, on_pre='Vinp=Vhigh')
# bottomup_in2.connect(j='i')
# print(bottomup_in,bottomup_in2)
if theta_phase=='good':
fLIP=50*Hz
# fLIP=13*Hz #test, if LIP hasn't switched to its good phase activity
else :
fLIP=13*Hz
# print(fLIP)
bottomup=generate_spike_timing(N_SI_gran,fLIP,0*ms,end_time=2100*ms)
if theta_phase=='mixed':
t0=0*ms
t1=125*ms
fLIP=50*Hz
bottomup=generate_spike_timing(N_SI_gran,fLIP,t0,end_time=t1)
while t0+250*ms<runtime:
t0,t1=t0+250*ms,t1+250*ms
fLIP=50*Hz*int(fLIP==13*Hz)+13*Hz*int(fLIP==50*Hz)
bottomup=vstack((bottomup,generate_spike_timing(N_SI_gran,fLIP,t0,end_time=t1)))
Poisson_input = SpikeGeneratorGroup(N_SI_gran, bottomup[:,1], bottomup[:,0]*second)
bottomup_in=Synapses(Poisson_input,SI_gran,on_pre='Vinp=Vhigh')
bottomup_in.connect(j='i')
Poisson_input2 = SpikeGeneratorGroup(N_RS_gran, bottomup[:,1], bottomup[:,0]*second)
bottomup_in2=Synapses(Poisson_input2,E_gran,on_pre='Vinp=Vhigh')
bottomup_in2.connect(j='i')
#Define monitors and run network :
R5=SpikeMonitor(E_gran,record=True)
R6=SpikeMonitor(SI_gran,record=True)
R7=SpikeMonitor(SI_deep,record=True)
#inpmon=StateMonitor(E_gran,'Iinp1',record=True)
#graninpmon=StateMonitor(FS,'IsynEgran',record=[0])
#inpIBmon=StateMonitor(IB_bd,'Iapp',record=[0])
V_RS=StateMonitor(E_gran,'V',record=True)
V_FS=StateMonitor(SI_gran,'V',record=True)
V_SI=StateMonitor(SI_deep,'V',record=True)
all_neurons=SI_deep,E_gran,SI_gran,G_topdown3,Poisson_input,Poisson_input2
all_synapses=S_EgranEgran,S_EgranFSgran,S_FSgranEgran,S_FSgranFSgran,S_SIdeepFSgran,topdown_in3,bottomup_in,bottomup_in2
all_monitors=R5,R6,R7,V_RS,V_FS,V_SI
return all_neurons,all_synapses,all_monitors
if __name__=='__main__':
close('all')
start_scope()
prefs.codegen.target = 'numpy'
defaultclock.dt = 0.01*ms
FLee=(0.05*mS/cm**2)/(0.4*uS/cm**2)*0.5
theta_phase='mixed' #'good' or 'bad' or 'mixed'
runtime=1*second
Vrev_inp=0*mV
taurinp=0.1*ms
taudinp=0.5*ms
tauinp=taudinp
Vhigh=0*mV
Vlow=-80*mV
ginp=0* msiemens * cm **-2
N_SI,N_RS_gran,N_SI_gran=20,20,20
all_neurons,all_synapses,all_monitors=generate_deepSI_and_gran_layers(theta_phase,N_SI,N_RS_gran,N_SI_gran,runtime)
net=Network()
net.add(all_neurons)
net.add(all_synapses)
net.add(all_monitors)
prefs.codegen.target = 'cython' #cython=faster, numpy = default python
net.run(runtime,report='text',report_period=300*second)
R5,R6,R7,V_RS,V_FS,V_SI=all_monitors
figure()
plot(R7.t,R7.i+0,'b.',label='deep SI cells')
plot(R5.t,R5.i+20,'r.',label='gran RS')
plot(R6.t,R6.i+40,'k.',label='gran SI')
xlim(0,runtime/second)
legend(loc='upper left')
figure()
plot(V_RS.t,V_RS.V[0])
min_t=int(50*ms*100000*Hz)
LFP_V_RS=1/20*sum(V_RS.V,axis=0)[min_t:]
LFP_V_FS=1/20*sum(V_FS.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')
figure()
subplot(221)
plot((V_RS.t/second)[min_t:],LFP_V_RS)
ylabel('LFP')
title('gran RS cell')
subplot(223)
plot((V_FS.t/second)[min_t:],LFP_V_FS)
ylabel('LFP')
title('gran FS cell')
subplot(222)
plot(f,Spectrum_LFP_V_RS)
ylabel('Spectrum')
yticks([],[])
xlim(0,100)
title('gran RS cell')
subplot(224)
plot(f,Spectrum_LFP_V_FS)
ylabel('Spectrum')
yticks([],[])
xlim(0,100)
title('gran FS cell')
clear_cache('cython')