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#!/usr/bin/env python
"""
Inhibitory synaptic plasticity in a recurrent network model
-----------------------------------------------------------
(F. Zenke, 2011) (from the 2012 Brian twister)
Adapted from:
Vogels, T. P., H. Sprekeler, F. Zenke, C. Clopath, and W. Gerstner.
Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks.
Science (November 10, 2011).
"""
from brian2 import *
# ###########################################
# Defining network model parameters
# ###########################################
NE = 8000 # Number of excitatory cells
NI = NE/4 # Number of inhibitory cells
tau_ampa = 5.0*ms # Glutamatergic synaptic time constant
tau_gaba = 10.0*ms # GABAergic synaptic time constant
epsilon = 0.02 # Sparseness of synaptic connections
tau_stdp = 20*ms # STDP time constant
simtime = 10*second # Simulation time
# ###########################################
# Neuron model
# ###########################################
gl = 10.0*nsiemens # Leak conductance
el = -60*mV # Resting potential
er = -80*mV # Inhibitory reversal potential
vt = -50.*mV # Spiking threshold
memc = 200.0*pfarad # Membrane capacitance
bgcurrent = 200*pA # External current
eqs_neurons='''
dv/dt=(-gl*(v-el)-(g_ampa*v+g_gaba*(v-er))+bgcurrent)/memc : volt (unless refractory)
dg_ampa/dt = -g_ampa/tau_ampa : siemens
dg_gaba/dt = -g_gaba/tau_gaba : siemens
'''
# ###########################################
# Initialize neuron group
# ###########################################
neurons = NeuronGroup(NE+NI, model=eqs_neurons, threshold='v > vt',
reset='v=el', refractory=5*ms, method='euler')
Pe = neurons[:NE]
Pi = neurons[NE:]
# ###########################################
# Connecting the network
# ###########################################
con_e = Synapses(Pe, neurons, on_pre='g_ampa += 0.3*nS')
con_e.connect(p=epsilon)
con_ii = Synapses(Pi, Pi, on_pre='g_gaba += 3*nS')
con_ii.connect(p=epsilon)
# ###########################################
# Inhibitory Plasticity
# ###########################################
eqs_stdp_inhib = '''
w : 1
dApre/dt=-Apre/tau_stdp : 1 (event-driven)
dApost/dt=-Apost/tau_stdp : 1 (event-driven)
'''
alpha = 3*Hz*tau_stdp*2 # Target rate parameter
gmax = 100 # Maximum inhibitory weight
con_ie = Synapses(Pi, Pe, model=eqs_stdp_inhib,
on_pre='''Apre += 1.
w = clip(w+(Apost-alpha)*eta, 0, gmax)
g_gaba += w*nS''',
on_post='''Apost += 1.
w = clip(w+Apre*eta, 0, gmax)
''')
con_ie.connect(p=epsilon)
con_ie.w = 1e-10
# ###########################################
# Setting up monitors
# ###########################################
sm = SpikeMonitor(Pe)
# ###########################################
# Run without plasticity
# ###########################################
eta = 0 # Learning rate
run(1*second)
# ###########################################
# Run with plasticity
# ###########################################
eta = 1e-2 # Learning rate
run(simtime-1*second, report='text')
# ###########################################
# Make plots
# ###########################################
i, t = sm.it
subplot(211)
plot(t/ms, i, 'k.', ms=0.25)
title("Before")
xlabel("")
yticks([])
xlim(0.8*1e3, 1*1e3)
subplot(212)
plot(t/ms, i, 'k.', ms=0.25)
xlabel("time (ms)")
yticks([])
title("After")
xlim((simtime-0.2*second)/ms, simtime/ms)
show()
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