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Copy pathFEF_full3_alternatec.py
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FEF_full3_alternatec.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Feb 5 11:24:49 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 import *
from FEF_visuomotor_alternatec import *
from FEF_contrast_detection_3 import *
runtime=3*second
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(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
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)
def create_FEF_full2(N_RS_vis,N_FS_vis,N_RS_mot,N_dSI_vm,N_RS_vm,N_gSI_vm,f_mdPul,theta_phase,target_on,runtime,target_time):
#create each functional group of neurons individually
all_neurons_vm,all_synapses_vm,all_monitors_vm=generate_deepSI_and_gran_layers(theta_phase,N_dSI_vm,N_RS_vm,N_gSI_vm,f_mdPul,runtime)
RS_vm=all_neurons_vm[1]
all_neurons_v,all_synapses_v,all_monitors_v=generate_visual_neurons(theta_phase,N_FS_vis,N_RS_vis,runtime,target_on,target_time)
RS_vis=all_neurons_v[0]
# print(all_neurons_v)
RS_mot=NeuronGroup(N_RS_mot,eq_RS_FEF,threshold='V>-20*mvolt',refractory=3*ms,method='rk4')
RS_mot.V = '-70*mvolt+10*rand()*mvolt'
RS_mot.h = '0+0.05*rand()'
RS_mot.m = '0+0.05*rand()'
RS_mot.mAR = '0.035+0.025*rand()'
# RS_mot.J='50 * uA * cmeter ** -2'
RS_mot.J='50 * uA * cmeter ** -2'
#From visual to visual-motor
# S_RSvRSm=generate_syn(RS_vis,RS_mot,'IsynRS_FEF_V','',0.15*msiemens * cm **-2,12.5*ms,125*ms,0*mV)
# S_RSvRSm_AMPA=generate_syn(RS_vis,RS_mot,'IsynRS_FEF_V','',0.04*msiemens * cm **-2,0.125*ms,1*ms,0*mV) #AMPA 0.125*ms,1*ms NMDA 12.5*ms,125*ms
S_RSvRSm_AMPA=generate_syn(RS_vis,RS_mot,'IsynRS_FEF_V','i<10',0.08*msiemens * cm **-2,0.125*ms,1*ms,0*mV) #AMPA 0.125*ms,1*ms NMDA 12.5*ms,125*ms
#0.06
S_RSvmRSm_AMPA=generate_syn(RS_vm,RS_mot,'IsynFS_FEF_V','',0*msiemens * cm **-2,0.125*ms,1*ms,0*mV)
S_RSvRSm_NMDA=generate_syn(RS_vis,RS_mot,'IsynSI_FEF_VM','',0.01*msiemens * cm **-2,12.5*ms,125*ms,0*mV) #AMPA 0.125*ms,1*ms NMDA 12.5*ms,125*ms
S_RSvmRSm_NMDA=generate_syn(RS_vm,RS_mot,'IsynSI2_FEF_VM','',0.1*msiemens * cm **-2,12.5*ms,125*ms,0*mV)
eq_gap='''_post=g_i*(V_post-V_pre) : amp * meter ** -2 (summed)
g_i : siemens * meter**-2
'''
gapRSmot=Synapses(RS_mot,RS_mot,model='Igap'+eq_gap,method='exact')
gapRSmot.connect(j='i')
gapRSmot.g_i=0* msiemens * cm **-2
mon_RS=SpikeMonitor(RS_mot,record=True)
all_monitors=all_monitors_vm+all_monitors_v+(mon_RS,)
all_neurons=all_neurons_vm+all_neurons_v+(RS_mot,)
all_synapses=all_synapses_vm+all_synapses_v+(S_RSvRSm_AMPA,S_RSvmRSm_AMPA,S_RSvRSm_NMDA,S_RSvmRSm_NMDA,gapRSmot)
return all_neurons,all_synapses,all_monitors
if __name__=='__main__':
close('all')
prefs.codegen.target = 'numpy'
defaultclock.dt = 0.01*ms
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
ginp_SI=0* msiemens * cm **-2
f_mdPul=13*Hz
print('Creating the network')
N_RS_vis,N_FS_vis,N_RS_mot,N_dSI_vm,N_RS_vm,N_gSI_vm=[20]*6
theta_phase='bad'
target_on=True
runtime=1*second
target_time=500*msecond
net=Network()
# net,all_monitors=create_network(N_RS_vis,N_FS_vis,N_RS_mot,N_SI_mot,N_dSI_vm,N_RS_vm,N_gSI_vm,theta_phase,target_on,runtime)
all_neurons,all_synapses,all_monitors=create_FEF_full2(N_RS_vis,N_FS_vis,N_RS_mot,N_dSI_vm,N_RS_vm,N_gSI_vm,f_mdPul,theta_phase,target_on,runtime,target_time)
net.add(all_neurons)
net.add(all_synapses)
net.add(all_monitors)
print('Compiling with cython')
prefs.codegen.target = 'cython'
net.run(runtime,report='text',report_period=300*second)
# R1,R2,R3,V1,V2,V3,R4,R5,V4,V5,R6,R7,V6,V7,R8,mon_RS=all_monitors
R1,R2,R3,V1,V2,V3,R4,R5,R6,R7,mon_RS=all_monitors
figure(figsize=(10,4))
subplot(131)
title('Visual Neurons')
plot(R4.t,R4.i+20,'r.',label='RS')
plot(R5.t,R5.i+0,'k.',label='FS')
plot(R6.t,R6.i+40,'b.',label='VIP')
plot(R7.t,R7.i+60,'g.',label='SI')
xlim(0,runtime/second)
legend(loc='upper left')
subplot(132)
title('Visual-Motor Neurons')
plot(R3.t,R3.i+0,'c.',label='SI 1')
plot(R1.t,R1.i+60,'r.',label='RS')
plot(R2.t,R2.i+40,'b.',label='SI 2')
xlim(0,runtime/second)
legend(loc='upper left')
subplot(133)
title('Motor Neurons')
plot(mon_RS.t,mon_RS.i+0,'r.',label='RS')
xlim(0,runtime/second)
legend(loc='upper left')
# min_t=int(50*ms*100000*Hz)
# LFP_V1=1/20*sum(V1.V,axis=0)[min_t:]
# LFP_V2=1/20*sum(V2.V,axis=0)[min_t:]
# LFP_V3=1/20*sum(V3.V,axis=0)[min_t:]
# LFP_V4=1/20*sum(V4.V,axis=0)[min_t:]
# LFP_V5=1/20*sum(V5.V,axis=0)[min_t:]
## LFP_V6=1/20*sum(V6.V,axis=0)[min_t:]
## LFP_V7=1/20*sum(V7.V,axis=0)[min_t:]
#
# f,Spectrum_LFP_V1=signal.periodogram(LFP_V1, 100000,'flattop', scaling='spectrum')
# f,Spectrum_LFP_V2=signal.periodogram(LFP_V2, 100000,'flattop', scaling='spectrum')
# f,Spectrum_LFP_V3=signal.periodogram(LFP_V3, 100000,'flattop', scaling='spectrum')
# f,Spectrum_LFP_V4=signal.periodogram(LFP_V4, 100000,'flattop', scaling='spectrum')
# f,Spectrum_LFP_V5=signal.periodogram(LFP_V5, 100000,'flattop', scaling='spectrum')
## f,Spectrum_LFP_V6=signal.periodogram(LFP_V6, 100000,'flattop', scaling='spectrum')
## f,Spectrum_LFP_V7=signal.periodogram(LFP_V7, 100000,'flattop', scaling='spectrum')
#
# figure(figsize=(10,4))
# subplot(331)
# plot(f,Spectrum_LFP_V4)
# ylabel('Spectrum')
# yticks([],[])
# xlim(0,100)
# title('visual RS')
# subplot(334)
# plot(f,Spectrum_LFP_V5)
# ylabel('Spectrum')
# yticks([],[])
# xlim(0,100)
# title('visual FS')
#
# subplot(332)
# plot(f,Spectrum_LFP_V1)
# ylabel('Spectrum')
# yticks([],[])
# xlim(0,100)
# title('visual-motor gran RS')
# subplot(335)
# plot(f,Spectrum_LFP_V2)
# ylabel('Spectrum')
# yticks([],[])
# xlim(0,100)
# title('visual-motor gran SI')
# subplot(338)
# plot(f,Spectrum_LFP_V3)
# ylabel('Spectrum')
# yticks([],[])
# xlim(0,100)
# title('visual-motor deep SI')
# subplot(333)
# plot(f,Spectrum_LFP_V6)
# ylabel('Spectrum')
# yticks([],[])
# xlim(0,100)
# title('motor RS')
# subplot(336)
# plot(f,Spectrum_LFP_V7)
# ylabel('Spectrum')
# yticks([],[])
# xlim(0,100)
# title('motor SI')
# tight_layout()
# figure()
# plot(mon_RS.t,mon_RS.Isyn[0])
# plot(mon_RS.t,mon_RS.Isyn[10])
clear_cache('cython')