# Example: STDP

> Note

> You can launch an interactive, editable version of this example without installing any local files using the Binder service (although note that at some times this may be slow or fail to open):

Spike-timing dependent plasticity

Adapted from Song, Miller and Abbott (2000) and Song and Abbott (2001)

    from brian2 import *

    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
    '''

    poisson_input = PoissonGroup(N, rates=F)
    neurons = NeuronGroup(1, eqs_neurons, threshold='v>vt', reset='v = vr',
                          method='euler')
    S = Synapses(poisson_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(poisson_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()