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#!/usr/bin/env python
"""
Spike-timing dependent plasticity.
Adapted from Song, Miller and Abbott (2000) and Song and Abbott (2001).
This example is modified from ``synapses_STDP.py`` and writes a standalone
C++ project in the directory ``STDP_standalone``.
"""
from brian2 import *
set_device('cpp_standalone', directory='STDP_standalone')
N = 1000
taum = 10*ms
taupre = 20*ms
taupost = taupre
Ee = 0*mV
vt = -54*mV
vr = -60*mV
El = -74*mV
taue = 5*ms
F = 15*Hz
gmax = .01
dApre = .01
dApost = -dApre * taupre / taupost * 1.05
dApost *= gmax
dApre *= gmax
eqs_neurons = '''
dv/dt = (ge * (Ee-v) + El - v) / taum : volt
dge/dt = -ge / taue : 1
'''
input = PoissonGroup(N, rates=F)
neurons = NeuronGroup(1, eqs_neurons, threshold='v>vt', reset='v = vr',
method='euler')
S = Synapses(input, neurons,
'''w : 1
dApre/dt = -Apre / taupre : 1 (event-driven)
dApost/dt = -Apost / taupost : 1 (event-driven)''',
on_pre='''ge += w
Apre += dApre
w = clip(w + Apost, 0, gmax)''',
on_post='''Apost += dApost
w = clip(w + Apre, 0, gmax)''',
)
S.connect()
S.w = 'rand() * gmax'
mon = StateMonitor(S, 'w', record=[0, 1])
s_mon = SpikeMonitor(input)
run(100*second, report='text')
subplot(311)
plot(S.w / gmax, '.k')
ylabel('Weight / gmax')
xlabel('Synapse index')
subplot(312)
hist(S.w / gmax, 20)
xlabel('Weight / gmax')
subplot(313)
plot(mon.t/second, mon.w.T/gmax)
xlabel('Time (s)')
ylabel('Weight / gmax')
tight_layout()
show()
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