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FEF_contrast_detection_3.py
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FEF_contrast_detection_3.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 29 08:58:41 2020
@author: amelie
"""
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_vis import *
def generate_visual_neurons(theta_phase,N_FS,N_RS,runtime,target_on,target_time):
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=10* 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=10* 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
RS=NeuronGroup(N_RS,eq_RS_FEF,threshold='V>-20*mvolt',refractory=3*ms,method='rk4')
RS.V = '-70*mvolt+10*rand()*mvolt'
RS.h = '0+0.05*rand()'
RS.m = '0+0.05*rand()'
RS.mAR = '0.035+0.025*rand()'
# RS.J='50 * uA * cmeter ** -2' #article SI=25, code=1
RS.J='55 * uA * cmeter ** -2' #article SI=25, code=1
#0
# RS.J='20 * uA * cmeter ** -2' #article SI=25, code=1
# FS=NeuronGroup(N_FS,eq_VIP_vis,threshold='V>-20*mvolt',refractory=3*ms,method='rk4')
FS=NeuronGroup(N_FS,eq_FS_FEF,threshold='V>-20*mvolt',refractory=3*ms,method='rk4')
FS.V = '-70*mvolt+10*rand()*mvolt'
FS.h = '0+0.05*rand()'
FS.m = '0+0.05*rand()'
# FS.J='6 * uA * cmeter ** -2' #article=code=35
# FS.J='7 * uA * cmeter ** -2' #article=code=35
# FS.J='5 * uA * cmeter ** -2' #article=code=35
FS.J='10 * uA * cmeter ** -2' #article=code=35
# #-30
# FS.Iapp='0 * uA * cmeter ** -2' #article=code=35
SI=NeuronGroup(N_FS,eq_SI_FEF,threshold='V>-20*mvolt',refractory=3*ms,method='rk4')
SI.V = '-70*mvolt+10*rand()*mvolt'
SI.h = '0+0.05*rand()'
SI.m = '0+0.05*rand()'
SI.J='40 * uA * cmeter ** -2' #article=code=35
VIP=NeuronGroup(N_FS,eq_VIP_vis,threshold='V>-20*mvolt',refractory=3*ms,method='rk4')
VIP.V = '-90*mvolt+10*rand()*mvolt'
VIP.Iapp='5 * uA * cmeter ** -2' #article=code=35
##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(condition=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_RSFS=generate_syn(RS,FS,'IsynRS_FEF_VM','i//10==j//10',0.2* msiemens * cm **-2,0.125*ms,1*ms,0*mV)
S_RSRS=generate_syn(RS,RS,'IsynRS_FEF_VM','i//10==j//10',0.2* msiemens * cm **-2,0.125*ms,1*ms,0*mV)
S_FSRS=generate_syn(FS,RS,'IsynSI_FEF_VM','i//10==j//10',0.2* msiemens * cm **-2,0.25*ms,5*ms,-80*mV)
S_FSFS=generate_syn(FS,FS,'IsynSI_FEF_VM','i//10==j//10',0.2* msiemens * cm **-2,0.25*ms,5*ms,-80*mV)
# S_VIPSI=generate_syn(VIP,SI,'IsynSI_FEF_VM','i//10==j//10',0.7* msiemens * cm **-2,0.25*ms,20*ms,-80*mV)
S_VIPSI=generate_syn(VIP,SI,'IsynSI_FEF_VM','i//10==j//10',0.7* msiemens * cm **-2,0.25*ms,20*ms,-80*mV)
S_SIVIP=generate_syn(SI,VIP,'IsynSI_FEF_VM','',0.01* msiemens * cm **-2,0.25*ms,20*ms,-80*mV)
S_SIRS=generate_syn(SI,RS,'IsynSI2_FEF_VM','i//10==j//10',1.5* msiemens * cm **-2,0.25*ms,20*ms,-80*mV)
# print(S_SIVIP)
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*rand())/f #0.01
while next_spike<end_time:
list_time.append((next_spike,i))
next_spike=list_time[-1][0]+(1+0*rand())/f
list_time_and_i+=list_time
return array(list_time_and_i)
if input_thalamus_gran:
# FS.ginp_VIP_good=thal_cond*1
# FS.ginp_VIP_bad=thal_cond*1
FS.ginp_FS=thal_cond*0
RS.ginp_RS=7.5* msiemens * cm **-2
SI.ginp_SI=7.5* msiemens * cm **-2
# RS.ginp_RS=7* msiemens * cm **-2
# SI.ginp_SI=7* msiemens * cm **-2
# VIP.ginp_VIP_good=2.5* msiemens * cm **-2
# VIP.ginp_VIP_bad=2.5* msiemens * cm **-2
VIP.ginp_VIP_good=3* msiemens * cm **-2
VIP.ginp_VIP_bad=3* msiemens * cm **-2
if theta_phase=='good':
fLIP=50*Hz
# fLIP=25*Hz
else :
# fLIP=50*Hz
fLIP=13*Hz
# print(fLIP)
gamma_background=generate_spike_timing(N_FS,fLIP,0*ms,end_time=3000*ms)
if theta_phase=='mixed':
t0=0*ms
t1=125*ms
gamma_background=generate_spike_timing(N_FS,fLIP,t0,end_time=t1)
while t0+125*ms<runtime:
fLIP=50*Hz*int(fLIP==13*Hz)+13*Hz*int(fLIP==50*Hz)
t0,t1=t0+125*ms,t1+125*ms
gamma_background=vstack((gamma_background,generate_spike_timing(N_FS,fLIP,t0,end_time=t1)))
# gamma_background=generate_spike_timing(N_FS,fLIP,0*ms,end_time=2100*ms)
# gamma_target=generate_spike_timing(20,50*Hz,600*ms,end_time=700*ms)
# gamma_target=generate_spike_timing(20,50*Hz,500*ms,end_time=600*ms)
gamma_target=generate_spike_timing(10,50*Hz,target_time,end_time=target_time+100*ms)
# gamma_target=generate_spike_timing(10,50*Hz,500*ms,end_time=600*ms)
Poisson_background = SpikeGeneratorGroup(N_FS, gamma_background[:,1], gamma_background[:,0]*second)
if target_on :
# Poisson_target = SpikeGeneratorGroup(20, gamma_target[:,1], gamma_target[:,0]*second)
Poisson_target = SpikeGeneratorGroup(10, gamma_target[:,1], gamma_target[:,0]*second)
else :
# Poisson_target = SpikeGeneratorGroup(20, [], []*second)
Poisson_target = SpikeGeneratorGroup(10, [], []*second)
# S_in_bg_FS=Synapses(Poisson_background,FS,on_pre='Vinp=Vhigh')
# S_in_bg_FS.connect(j='i')
S_in_bg_RS=Synapses(Poisson_background,RS,on_pre='Vinp=Vhigh')
S_in_bg_RS.connect(j='i')
S_in_bg_SI=Synapses(Poisson_background,SI,on_pre='Vinp=Vhigh')
S_in_bg_SI.connect(j='i')
S_in_bg_VIP=Synapses(Poisson_background,VIP,on_pre='Vinp=Vhigh')
S_in_bg_VIP.connect(j='i')
# S_in_target_FS=Synapses(Poisson_target,FS,on_pre='Vinp2=Vhigh')
# S_in_target_FS.connect(j='i')
# S_in_target_RS=Synapses(Poisson_target,RS,on_pre='Vinp2=Vhigh')
# S_in_target_RS.connect(j='i')
S_in_target_VIP=Synapses(Poisson_target,VIP,on_pre='Vinp2=Vhigh')
S_in_target_VIP.connect(j='i')
S_in_target_SI=Synapses(Poisson_target,SI,on_pre='Vinp2=Vhigh')
S_in_target_SI.connect(j='i')
SI.ginp_SI2=2.5* msiemens * cm **-2
VIP.ginp_VIP2=2.5* msiemens * cm **-2
## SI.ginp_SI2=3* msiemens * cm **-2
## VIP.ginp_VIP2=3* msiemens * cm **-2
RS.ginp_RS2=2.5* msiemens * cm **-2
# SI.ginp_SI2=7.5* msiemens * cm **-2
# VIP.ginp_VIP2=7.5* msiemens * cm **-2
# SI.ginp_SI2=3* msiemens * cm **-2
# VIP.ginp_VIP2=3* msiemens * cm **-2
# RS.ginp_RS2=7.5* msiemens * cm **-2
#Define monitors and run network :
R5=SpikeMonitor(RS,record=True)
R6=SpikeMonitor(FS,record=True)
R7=SpikeMonitor(VIP,record=True)
R8=SpikeMonitor(SI,record=True)
all_neurons=RS,FS,VIP,SI,Poisson_background,Poisson_target
all_synapses=S_FSRS,S_RSFS,S_RSRS,S_FSFS,S_VIPSI,S_SIVIP,S_SIRS,S_in_bg_RS,S_in_bg_SI,S_in_bg_VIP,S_in_target_VIP,S_in_target_SI
# all_synapses=S_FSRS,S_RSFS,S_in_bg_RS,S_in_target_RS
all_monitors=R5,R6,R7,R8
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
target_on=True
target_time=600*msecond
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_FS,N_RS=20,20
all_neurons,all_synapses,all_monitors=generate_visual_neurons(theta_phase,N_FS,N_RS,runtime,target_on,target_time)
net=Network()
net.add(all_neurons)
net.add(all_synapses)
net.add(all_monitors)
# RS=all_neurons[0]
# M=StateMonitor(RS,['Iinp1','Iinp2'],record=[0])
# net.add(M)
# VIP=all_neurons[2]
# M=StateMonitor(VIP,['V'],record=[0])
# net.add(M)
taurinp=0.1*ms
taudinp=0.5*ms
tauinp=taudinp
taurinp2=2*ms
taudinp2=10*ms
tauinp2=taudinp2
prefs.codegen.target = 'cython' #cython=faster, numpy = default python
net.run(runtime,report='text',report_period=300*second)
R5,R6,R7,R8=all_monitors
figure(figsize=(4,5))
plot(R5.t,R5.i+0,'r.',label='RS')
plot(R6.t,R6.i+20,'b.',label='FS')
plot(R7.t,R7.i+40,'k.',label='VIP')
plot(R8.t,R8.i+60,'g.',label='SOM')
xlim(0.2,runtime/second)
xlabel('Time (s)')
ylabel('Neuron index')
# legend(loc='upper left')
# figure()
# plot(R5.t,R5.i+0,'r.',label='RS')
# plot(R6.t,R6.i+20,'b.',label='FS')
# plot(R7.t,R7.i+40,'k.',label='VIP')
# plot(R8.t,R8.i+60,'g.',label='SI')
# xlim(0,runtime/second)
# legend(loc='upper left')
# xlabel('Time (s)')
# ylabel('Neuron index')
# figure()
# plot(M.t,M.Iinp1[0])
# plot(M.t,M.Iinp2[0])
# figure()
# plot(M.t,M.V[0])
clear_cache('cython')