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examples-plasticity_STDP1.txt
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examples-plasticity_STDP1.txt
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.. currentmodule:: brian
.. index::
pair: example usage; subplot
pair: example usage; plot
pair: example usage; run
pair: example usage; PopulationRateMonitor
pair: example usage; show
pair: example usage; Connection
pair: example usage; hist
pair: example usage; PoissonGroup
pair: example usage; NeuronGroup
pair: example usage; rate
pair: example usage; STDP
.. _example-plasticity_STDP1:
Example: STDP1 (plasticity)
===========================
Spike-timing dependent plasticity
Adapted from Song, Miller and Abbott (2000) and Song and Abbott (2001)
This simulation takes a long time!
::
from brian import *
from time import time
N = 1000
taum = 10 * ms
tau_pre = 20 * ms
tau_post = tau_pre
Ee = 0 * mV
vt = -54 * mV
vr = -60 * mV
El = -74 * mV
taue = 5 * ms
F = 15 * Hz
gmax = .01
dA_pre = .01
dA_post = -dA_pre * tau_pre / tau_post * 1.05
eqs_neurons = '''
dv/dt=(ge*(Ee-vr)+El-v)/taum : volt # the synaptic current is linearized
dge/dt=-ge/taue : 1
'''
input = PoissonGroup(N, rates=F)
neurons = NeuronGroup(1, model=eqs_neurons, threshold=vt, reset=vr)
synapses = Connection(input, neurons, 'ge', weight=rand(len(input), len(neurons)) * gmax)
neurons.v = vr
#stdp=ExponentialSTDP(synapses,tau_pre,tau_post,dA_pre,dA_post,wmax=gmax)
## Explicit STDP rule
eqs_stdp = '''
dA_pre/dt=-A_pre/tau_pre : 1
dA_post/dt=-A_post/tau_post : 1
'''
dA_post *= gmax
dA_pre *= gmax
stdp = STDP(synapses, eqs=eqs_stdp, pre='A_pre+=dA_pre;w+=A_post',
post='A_post+=dA_post;w+=A_pre', wmax=gmax)
rate = PopulationRateMonitor(neurons)
start_time = time()
run(100 * second, report='text')
print "Simulation time:", time() - start_time
subplot(311)
plot(rate.times / second, rate.smooth_rate(100 * ms))
subplot(312)
plot(synapses.W.todense() / gmax, '.')
subplot(313)
hist(synapses.W.todense() / gmax, 20)
show()