# Example: synapses_STDPΒΆ

#!/usr/bin/env python
'''
Spike-timing dependent plasticity
Adapted from Song, Miller and Abbott (2000) and Song and Abbott (2001)

This simulation takes a long time!
'''
from time import time

from brian2 import *

# brian_prefs.codegen.target = 'weave'

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-vr)+El-v)/taum : volt   # the synaptic current is linearized
dge/dt=-ge/taue : 1
'''

input = PoissonGroup(N, rates=F)
neurons = NeuronGroup(1, eqs_neurons, threshold='v>vt', reset='v=vr')
S = Synapses(input, neurons,
'''w:1
dApre/dt=-Apre/taupre : 1 (event-driven)
dApost/dt=-Apost/taupost : 1 (event-driven)''',
pre='''ge+=w
Apre+=dApre
w=clip(w+Apost,0,gmax)''',
post='''Apost+=dApost
w=clip(w+Apre,0,gmax)''',
connect=True,
)
S.w='rand()*gmax'
start_time = time()
run(100 * second, report='text')
print "Simulation time:", time() - start_time

subplot(211)
plot(S.w[:] / gmax, '.')
subplot(212)
hist(S.w[:] / gmax, 20)
show()