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prepostSTDP_savings.py
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prepostSTDP_savings.py
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#!/usr/bin/env python
'''
Long-term synaptic dynamics
Costa et al 2015
:: Figure 3 ::
:: Savings simulation (with two Guassian input profiles) ::
'''
import matplotlib as mpl
mpl.use('TkAgg')
from brian import *
from time import time
from numpy import *
import matplotlib.pyplot as plt
import matplotlib.pylab as plab
import os
exportOn = 0
plotOn = -1 #-1: none, 0: all, 1: only for input1, 2: only for input2
nruns = 1; #Number of runs
if os.path.isfile("spikespython.txt"):
os.remove("spikespython.txt")
if os.path.isfile("Neuron_I_python.txt"):
os.remove("Neuron_I_python.txt")
if os.path.isfile("array_A_python.txt"):
os.remove("array_A_python.txt")
for nrun in range(0,nruns):
close('all')
#Reset
#if 'neurons' in vars():
# reinit()
realtime = 0
stime = 0.1 * second
stime2 = 50 * second
extractFiringRatesOn = 0
resolution_export = 10; # every x ms
#neofytouchange apo 100 se 10
NxM = 0
MxM = 1
NxMxM = 0
N = 100
if MxM:
N = 0
M = 10
taum = 10 * ms
Ee = 0 * mV
taue = 2 * ms
Fon = 50 * Hz
Foff = 3 * Hz
#s = 55.0000e-10
if NxM:
s = 100.0000e-10
if MxM:
s = 500000
Amax = 2.
Amin = 0
Ainit = 0.1
Umax = 1.
Umin = 0
Uinit = 0.1
dFBn = 0
dFBp = 0
dFFp = 0
#Short-term plasticity params
tau_u = 50 * ms
tau_r = 200 * ms
#prepostSTDP params: AFBn tau_FBn AFBp tau_FBp AFFp tau_FFp
params = [0.1771, 0.0327, 0.1548, 0.2302, 0.0618, 0.0666];
AFBn = params[0]
tau_FBn = params[1]*1e3 * ms
AFBp = params[2]
tau_FBp = params[3]*1e3 * ms
AFFp = params[4]
tau_FFp = params[5]*1e3 * ms
#etaU = 0.35
etaU = 0.15
etaA = 0.15
#etaA = 0.35
defaultclock.dt = 1*ms
# Adex Parameters
C = 281 * pF
gL = 30 * nS
taum = C / gL
EL = -70.6 * mV
DeltaT = 2 * mV
vti = -50.4 * mV
#vtrest = vti + 5 * DeltaT
vtrest = -45 * mV
VTmax = 18 * mV
tauvt = 50 * ms
tauw, c, b, Vr = 144 * ms, 4 * nS, 0.0805 * nA, -70.6 * mV # Regular spiking (as in the paper)
eqs_neuron = """
dvm/dt=(gL*(EL-vm)+gL*DeltaT*exp((vm-vt)/DeltaT)+I-x)/C : volt
dvt/dt=-(vt-vtrest)/tauvt : volt
dx/dt=(c*(vm-EL)-x)/tauw : amp #In the standard formulation x is w
I : amp
"""
input1_pos = 25
input2_pos = 75
rad = 5
print "F_input1"
#Define input 1
F_input1 = ones(N)*Foff
#F_input1[input1_pos-rad:input1_pos+rad] = Fon
for i in range(0,N): #Define gaussian input
F_input1[i] = exp(-((((i+1)-input1_pos)**2)/(2.0*rad**2)))*(Fon-Foff)+Foff;
print F_input1
#Define input 2
F_input2 = ones(N)*Foff
#F_input2[input2_pos-rad:input2_pos+rad] = Fon
for i in range(0,N): #Define gaussian input
F_input2[i] = exp(-((((i+1)-input2_pos)**2)/(2.0*rad**2)))*(Fon-Foff)+Foff;
arrayN = ones(N)
for i in range(0, N):
arrayN[i] = i
F = 200 * Hz
#poissoninput = PoissonGroup(N, rates=F_input1)
#input2 = PoissonGroup(N, rates=F_input2)
#print 'PoissonGroup done'
neurons = NeuronGroup(M, model=eqs_neuron, threshold='vm>vt', reset="vm=Vr;x+=b;vt=VTmax", freeze = True)
neurons.vt = vtrest# - 26 * mV
if NxM:
neurons.vm = EL
if MxM:
neurons.vm = neurons.vt + 0.005
#for i in range(0,N):
# if (i%2 == 0):
# neurons.vm[i] = EL
# else:
# neurons.vm[i] = neurons.vt[i] + 0.005
#neurons.vm[0] = neurons.vt[0] + 0.005
#neurons.vm[3] = neurons.vt[3] + 0.005
#neurons.vm[8] = neurons.vt[8] + 0.005
print 'Vt'
print neurons.vt
print 'Vm'
print neurons.vm
neurons.I = 0 #0.0805 * nA
neurons.x = 0
print 'NeuronGroup done'
model='''w : 1
FFp : 1
FBp : 1
FBn : 1
R : 1
u : 1
U : 1
A : 1
dFFp/dt=-FFp/tau_FFp : 1 (event-driven)
dFBp/dt=-FBp/tau_FBp : 1 (event-driven)
dFBn/dt=-FBn/tau_FBn : 1 (event-driven)
dR/dt=(1-R)/tau_r : 1 (event-driven)
du/dt=(U-u)/tau_u : 1 (event-driven)
'''
#spiketimes = [([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 1 * ms), ([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 2 * ms), ([0, 1, 2, 3, 4, 5, 6, 7, 9], 3 * ms), ([0, 1, 2, 3, 4, 5, 6, 7, 9], 4 * ms), ([0, 1, 2, 3, 4, 5, 6, 7, 9], 5 * ms), ([0, 1, 2, 3, 4, 5, 6, 7, 9], 6 * ms), ([0, 1, 2, 3, 4, 5, 6, 7, 9], 7 * ms), ([0, 1, 2, 3, 4, 5, 6, 7, 9], 8 * ms), ([0, 1, 2, 3, 4, 5, 6, 7, 9], 9 * ms), ([0, 1, 2, 3, 4, 5, 6, 7, 9], 10 * ms),
#([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 11 * ms), ([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 12 * ms), ([0, 1, 2, 3, 4, 5, 6, 7, 9], 13 * ms), ([0, 1, 2, 3, 4, 5, 6, 7, 9], 14 * ms), ([0, 1, 2, 3, 4, 5, 6, 7, 9], 15 * ms), ([0, 1, 2, 3, 4, 5, 6, 7, 9], 16 * ms), ([0, 1, 2, 3, 4, 5, 6, 7, 9], 17 * ms), ([0, 1, 2, 3, 4, 5, 6, 7, 9], 18 * ms), ([0, 1, 2, 3, 4, 5, 6, 7, 9], 19 * ms), ([0, 1, 2, 3, 4, 5, 6, 7, 9], 20 * ms)]
#spiketimes = [(arrayN, 1 * ms), (arrayN, 4 * ms), (arrayN, 7 * ms), (arrayN, 10 * ms), (arrayN, 13 * ms), (arrayN, 16 * ms), (arrayN, 19 * ms)]
if NxM or NxMxM:
#spiketimes = [([25, 46], 0 * ms), (25, 1 * ms), ([24, 31], 2 * ms), (28, 5 * ms), (22, 6 * ms), (22, 7 * ms), ([23,26,35], 8 * ms), (19, 9 * ms), (68, 11 * ms), (32, 12 * ms), (31, 13 * ms), ([21,77], 14 * ms)]
spiketimes = [ (23, 4 * ms), ([20, 45], 5 * ms),([7, 27], 7 * ms), (25, 9 * ms), ([11, 27], 11 * ms), (27, 13 * ms), (29, 15 * ms), (32, 19 * ms), (27, 20 * ms), (41, 25 * ms), ([14, 27], 26 * ms), ([22, 27, 31], 28 * ms), (24, 29 * ms), (22, 30 * ms), (27, 31 * ms)]
my_input = SpikeGeneratorGroup(N, spiketimes)
if NxM or NxMxM:
syn = Synapses(my_input, neurons, model, pre='''I=s*A*R*u;
U=clip(U+etaU*(-AFBn*FBn*FBp + AFBp*FBp*FFp),Umin,Umax);
w=U*A;
FFp+=1; R-=R*u; u+=U*(1-u)''',
post='''A=A+etaA*(AFFp*FFp*FBn);
A=A-etaA*0.5*mean(AFFp*FFp*FBn);
A=clip(A,Amin,Amax);
w=U*A;
FBp+=1.;FBn+=1.''')
if MxM:
syn = Synapses(neurons, neurons, model, pre='''I=s*A*R*u;
U=clip(U+etaU*(-AFBn*FBn*FBp + AFBp*FBp*FFp),Umin,Umax);
w=U*A;
FFp+=1; R-=R*u; u+=U*(1-u)''',
post='''A=A+etaA*(AFFp*FFp*FBn);
A=A-etaA*0.5*mean(AFFp*FFp*FBn);
A=clip(A,Amin,Amax);
w=U*A;
FBp+=1.;FBn+=1.''')
#syn.connect_one_to_one(input, neurons)
print 'syn'
print syn
#neofytou testing connectivity
#syn[:,:] = True #all connected
#syn[50:,:] = True
syn[0,:]=True
#syn[10:20,:]=False
syn[2,:]=True
#syn[30:40,:]=False
syn[4,:]=True
#syn[50:60,:]=False
syn[6,:]=True
#syn[70:80,:]=False
syn[8,:]=True
#print syn[1,1], " ", syn[1,2], " ". syn[2,1], " ", syn[1,-1]
syn.FBp=0
syn.FBn=0
syn.R=1
print 'syn'
print syn
#syn.U='rand()*Uinit'
#syn.A='rand()*Ainit'
#syn.U[:]=Umin
#syn.U[:]=0.5
print 'size(syn.U[:])'
print size(syn.U[:])
# need to change the initialisations for U and A , for M > 1
#if NxM or NxMxM:
# for i in range(0,size(syn.U[:])/M): #Define gaussian input # for NxM
# for j in range(0,M): # for NxM
# syn.U[i*M+j] = exp(-((((i+1)-input1_pos)**2)/(2.0*(rad+0)**2)))*(Umax-Umin)+Umin; # for NxM
# syn.A[i*M+j] = exp(-((((i+1)-input1_pos)**2)/(2.0*(rad+3)**2)))*(Amax-Amin)+Amin; # for NxM
#if MxM:
for i in range(0,size(syn.U[:])): #Define gaussian input
syn.U[i] = exp(-((((i+1)-input1_pos)**2)/(2.0*(rad+0)**2)))*(Umax-Umin)+Umin; #for MxM
syn.A[i] = exp(-((((i+1)-input1_pos)**2)/(2.0*(rad+3)**2)))*(Amax-Amin)+Amin; #for MxM
if MxM:
syn.u[i] = 1
print 'syn.U[0]'
print syn.U[0]
# syn.U[input1_pos-rad:input1_pos+rad]=Umax
#syn.A[:]=Amin
#syn.A[input1_pos-rad-3:input1_pos+rad+3]=Amax
print 'Synapses done'
#Si = SpikeMonitor(input)
So = SpikeMonitor(neurons)
Mpost = MultiStateMonitor(neurons, record=True)
Mrate = PopulationRateMonitor(neurons,bin=100*ms)
Msyn = MultiStateMonitor(syn, record=True)
synU = StateMonitor(syn, 'U', record=True)
synA = StateMonitor(syn, 'A', record=True)
#Mstdp_post = MultiStateMonitor(stdp.post_group, record=True)
#Msyn = StateMonitor(synapses, 'W', record=True)
ref = 200
if(realtime):
ion()
subplot(411)
raster_plot(Si, refresh=ref*ms, showlast=stime2*3+stime)
subplot(412)
raster_plot(So, refresh=ref*ms, showlast=stime2*3+stime)
plab.ylim([-0.5,0.5])
subplot(413)
synU.plot([input1_pos, 50, input2_pos], refresh=ref*ms, showlast=stime2*3+stime)
plab.ylim([-0.05, 1.05])
subplot(414)
synA.plot([input1_pos, 50, input2_pos], refresh=ref*ms, showlast=stime2*3+stime)
plab.ylim([-0.05, 3.05])
show()
start_time = time()
run(stime)
#input1.rate = 0.1
#input.rate = F_input2
#run(stime2)
#input.rate = F_input1
#run(stime2)
#input.rate = F_input2
#run(stime2)
#os.system("cmp /home/ioannis/Desktop/Porting/array_A.txt /home/ioannis/Desktop/Diploma_Thesis/Tests/Debbuging_code/array_A_python.txt")
#os.system("cmp /home/ioannis/Desktop/Porting/Neurons_I.txt /home/ioannis/Desktop/Diploma_Thesis/Tests/Debbuging_code/Neuron_I_python.txt")
#os.system("cmp /home/ioannis/Desktop/Porting/Spikes.txt /home/ioannis/Desktop/Diploma_Thesis/Tests/Debbuging_code/spikespython.txt")
print "Simulation time:", time() - start_time
#print So.i[:]
#print So.t[:]
#G = NeuronGroup(...)
#spikemon = SpikeMonitor(G)
#statemon = StateMonitor(G, 'V', record=range(5))
#subplot(211)
#raster_plot(spikemon, refresh=10*ms, showlast=200*ms)
#subplot(212)
#statemon.plot(refresh=10*ms, showlast=200*ms)
#run(1*second)
if plotOn>=0:
plt.figure()
ion()
subplot(411)
raster_plot(Si)
subplot(412)
raster_plot(So)
subplot(413)
plot(syn.U[:], '.')
subplot(414)
plot(syn.A[:], '.')
show()
plt.figure()
i1 = 25
pFBn, = plt.plot(Msyn['FBn'].times, Msyn['FBn'][i1,:])
pFBp, = plt.plot(Msyn['FBp'].times, Msyn['FBp'][i1,:])
pPreLTD, = plt.plot(Msyn['FBn'].times, AFBn*Msyn['FBn'][i1,:]*Msyn['FBp'][i1,:])
pPreLTP, = plt.plot(Msyn['FBp'].times, AFBp*Msyn['FBp'][i1,:]*Msyn['FFp'][i1,:])
pFFp, = plt.plot(Msyn['FFp'].times, Msyn['FFp'][i1,:])
#pu, = plt.plot(Msyn['u'].times, Msyn['u'][i,:])
#pR, = plt.plot(Msyn['R'].times, Msyn['R'][i,:])
plt.legend([pFFp, pFBn, pFBp, pPreLTD, pPreLTP], ['FFp', 'FBn', 'FBp', 'preLTP', 'preLTD'])
ion()
show()
plt.figure()
i1 = input1_pos
i2 = input2_pos
pu1, = plt.plot(Msyn['U'].times, Msyn['U'][i1,:])
pA1, = plt.plot(Msyn['A'].times, Msyn['A'][i1,:])
pu2, = plt.plot(Msyn['U'].times, Msyn['U'][i2,:])
pA2, = plt.plot(Msyn['A'].times, Msyn['A'][i2,:])
plt.legend([pu1, pA1, pu2, pA2], ['U1', 'A1', 'U2', 'A2'])
ion()
show()
plt.figure()
pv, = plt.plot(Mpost['vm'].times, Mpost['vm'][0,:])
pvt, = plt.plot(Mpost['vt'].times, Mpost['vt'][0,:])
#pge, = plt.plot(Mpost['ge'].times, Mpost['ge'][0,:])
plt.legend([pv, pvt], ['Vm', 'vt'])
plt.show()
#Plot firing rate
rates = Mrate.smooth_rate(width=1000*ms,filter='gaussian')
if extractFiringRatesOn==0:
plt.figure()
pv, = plt.plot(Mrate.times, rates)
plt.show()
if exportOn:
#Export results to be plotted in matlab
import os as os
path = 'fromBrian/'
if os.path.exists(path + 'outParams.br'):
os.remove(path + 'outParams.br')
if os.path.exists(path + 'outU_run' + str(nrun) + '.br'):
os.remove(path + 'outU_run' + str(nrun) + '.br')
if os.path.exists(path + 'outA_run' + str(nrun) + '.br'):
os.remove(path + 'outA_run' + str(nrun) + '.br')
#f_handle = file(filename, 'a')
savetxt(path + 'outParams.br', [N, resolution_export, stime, stime2, input1_pos, input2_pos, rad, nruns, Amax], fmt='%f', newline='\n') # Number of postsynaptic neurons
savetxt(path + 'outU_run' + str(nrun) + '.br', Msyn['U'][:,::resolution_export], fmt='%f', newline='\n') # Save Us
savetxt(path + 'outA_run' + str(nrun) + '.br', Msyn['A'][:,::resolution_export], fmt='%f', newline='\n') # Save As
#savetxt(f_handle, Msyn['U'][:,0:2], fmt='%f', newline='\n') # Save Us
#savetxt(f_handle, Msyn['A'][:,0:2], fmt='%f', newline='\n') # Save As
#f_handle.close()
if extractFiringRatesOn:
#Extract postsynaptic firing rate for input1 and input2
Usim = Msyn['U'][:,:];
Asim = Msyn['A'][:,:];
post_nspikes1 = So.nspikes
forget(syn)
reinit()
neurons.vt = vtrest
neurons.vm = EL
neurons.I = 0
neurons.x = 0
modelAfter='''R : 1
u : 1
w : 1
U : 1
A : 1
dR/dt=(1-R)/tau_r : 1 (event-driven)
du/dt=(U-u)/tau_u : 1 (event-driven)
'''
synAfter=Synapses(input, neurons, modelAfter, pre='''I=s*A*R*u;
w=U*A;
R-=R*u; u+=U*(1-u)''',
post='''w=U*A''')
synAfter[:,:]=True
synAfter.R=1
synAfter.u=Uinit
synAfter.U=Umin
synAfter.U[input1_pos-rad:input1_pos+rad]=Umax
synAfter.A[:]=Amin
synAfter.A[input1_pos-rad:input1_pos+rad]=Amax
Uaux = synAfter.U[:]
Uaux[:] = Umin
Uaux[input2_pos-rad:input2_pos+rad]=Umax
Aaux = synAfter.A[:]
Aaux[:] = Amin
Aaux[input2_pos-rad:input2_pos+rad]=Amax
@network_operation
def loadUandA(clock):
#NEOFYTOU CHANGE
synAfter.U[:] = Usim[:,int(round(clock.t/clock.dt))]
synAfter.A[:] = Asim[:,int(round(clock.t/clock.dt))]
#synAfter.U[:] = Uaux[:]
#synAfter.A[:] = Aaux[:]
#INPUT 1
MsynAfter = MultiStateMonitor(synAfter, record=True)
Mpost = MultiStateMonitor(neurons, record=True)
start_time = time()
F_input1n = F_input1
F_input1n[F_input1n<(Fon/2)] = 0
input.rate = F_input1n
#run(stime+stime2)
run(stime+stime2*3)
rates_1 = Mrate.smooth_rate(width=1000*ms,filter='gaussian')
print "Simulation time (for input1 alone):", time() - start_time
#INPUT 2
post_nspikes2 = So.nspikes
reinit()
neurons.vt = vtrest
neurons.vm = EL
neurons.I = 0
neurons.x = 0
Mpost = MultiStateMonitor(neurons, record=True)
start_time = time()
F_input2n = F_input2
F_input2n[F_input2n<(Fon/2)] = 0
input.rate = F_input2
run(stime+stime2*3)
rates_2 = Mrate.smooth_rate(width=1000*ms,filter='gaussian')
print "Simulation time (for input2 alone):", time() - start_time
post_nspikes3 = So.nspikes
if plotOn>=1:
'''
plt.figure()
ion()
subplot(411)
raster_plot(Si)
subplot(412)
raster_plot(So)
subplot(413)
plot(synAfter.U[:], '.')
subplot(414)
plot(synAfter.A[:], '.')
show()
plt.figure()
i1 = 25
i2 = 75
pu1, = plt.plot(MsynAfter['U'].times, MsynAfter['U'][i1,:])
pA1, = plt.plot(MsynAfter['A'].times, MsynAfter['A'][i1,:])
pu2, = plt.plot(MsynAfter['U'].times, MsynAfter['U'][i2,:])
pA2, = plt.plot(MsynAfter['A'].times, MsynAfter['A'][i2,:])
plt.legend([pu1, pA1, pu2, pA2], ['U1', 'A1', 'U2', 'A2'])
ion()
show()
plt.figure()
pv, = plt.plot(Mpost['vm'].times, Mpost['vm'][0,:])
pvt, = plt.plot(Mpost['vt'].times, Mpost['vt'][0,:])
#pge, = plt.plot(Mpost['ge'].times, Mpost['ge'][0,:])
plt.legend([pv, pvt], ['Vm', 'vt'])
plt.show()
'''
#Plot firing rate
plt.figure()
prates, = plt.plot(Mrate.times, rates)
prates_1, = plt.plot(Mrate.times, rates_1)
prates_2, = plt.plot(Mrate.times, rates_2)
plt.legend([prates, prates_1, prates_2], ['Learning', 'Input_1', 'Input_2'])
plt.show()
print "Nspikes before: ", post_nspikes1, "| Nspikes after (Input1): ", post_nspikes2, "| Nspikes after (Input2): ", post_nspikes3
if exportOn:
if os.path.exists(path + 'rateInput1_run' + str(nrun) + '.br'):
os.remove(path + 'rateInput1_run' + str(nrun) + '.br')
if os.path.exists(path + 'rateInput2_run' + str(nrun) + '.br'):
os.remove(path + 'rateInput2_run' + str(nrun) + '.br')
savetxt(path + 'rateInput1_run' + str(nrun) + '.br', rates_1, fmt='%f', newline='\n') # Save post spike times
savetxt(path + 'rateInput2_run' + str(nrun) + '.br', rates_2, fmt='%f', newline='\n') # Save post spike times
clear()