.. currentmodule:: brian2

.. STDP_standalone:

Example: STDP_standalone
========================


        .. only:: html

            .. |launchbinder| image:: file:///usr/share/doc/python-brian-doc/docs/badge.svg
            .. _launchbinder: https://mybinder.org/v2/gh/brian-team/brian2-binder/master?filepath=examples/standalone/STDP_standalone.ipynb

            .. 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): |launchbinder|_

        

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()
    

