FacetsPythonCourse2008: synfire_chains.py

'''
Synfire chains (from Diesmann et al, 1999)
'''
from brian import *
# Neuron model parameters
Vr = -70*mV
Vt = -55*mV
taum = 10*ms
taupsp = 0.325*ms
weight = 4.86 * mV
# Neuron model
eqs=Equations('''
dV/dt=(-(V-Vr)+x)*(1./taum) : volt
dx/dt=(-x+y)*(1./taupsp) : volt
dy/dt=-y*(1./taupsp)+25.27*mV/ms+\
    (39.24*mV/ms**0.5)*xi : volt
''')
# Neuron groups
P = NeuronGroup(N=1000, model=eqs,
    threshold=Vt,reset=Vr,refractory=1*ms)
Pinput = PulsePacket(t=50*ms,n=85,sigma=1*ms)
# The network structure
Pgp = [ P.subgroup(100) for i in range(10)]
C = Connection(P,P,'y')
for i in range(9):
    C.connect_full(Pgp[i],Pgp[i+1],weight)
Cinput = Connection(Pinput,P,'y')
Cinput.connect_full(Pinput,Pgp[0],weight)
# Record the spikes
Mgp = [SpikeMonitor(p,record=True) for p in Pgp]
Minput = SpikeMonitor(Pinput,record=True)
monitors = [Minput]+Mgp
# Setup the network, and run it
P.V = Vr + rand(len(P)) * (Vt-Vr)
run(100*ms)
# Plot result
raster_plot(showgrouplines=True,*monitors)
show()