examples-frompapers-computing with neural synchrony-duration selectivity_Fig4_duration_stdp.txt

.. currentmodule:: brian

.. index::
   pair: example usage; plot
   pair: example usage; run
   pair: example usage; xlim
   pair: example usage; show
   pair: example usage; shape
   pair: example usage; STDP
   pair: example usage; EventClock
   pair: example usage; Connection
   pair: example usage; exp
   pair: example usage; NeuronGroup
   pair: example usage; ylim

.. _example-frompapers-computing with neural synchrony-duration selectivity_Fig4_duration_stdp:

Example: Fig4_duration_stdp (frompapers/computing with neural synchrony/duration selectivity)
=============================================================================================

Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561
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Figure 4D. STDP in the duration model.
(very long simulation, default: 5000 stimuli)

Caption (Fig. 4D). Temporal evolution of the synaptic weights of the neuron corresponding to the blue curves in Fig. 4C.

The script runs the simulation with STDP for a long time, then displays the evolution of synaptic weights for one neuron.

::

    from brian import *
    from pylab import cm
    from numpy.random import seed
    from brian.experimental.connectionmonitor import *
    import numpy
    from params import *
    
    # Rebound neurons
    eqs='''
    dv/dt=(El-v+(gmax*gK+gmax2*gK2+ginh)*(EK-v))/tau : volt
    dgK/dt=(gKinf-gK)/tauK : 1 # IKLT
    dgK2/dt=-gK2/tauK2 : 1 # Delayed rectifier
    gKinf=1./(1+exp((Va-v)/ka)) : 1
    tauK : ms
    tau : ms
    gmax : 1
    ginh : 1
    '''
    
    uniform=lambda N:(rand(N)-.5)*2 #uniform between -1 and 1
    seed(31415) # Get the same neurons every time
    
    neurons=NeuronGroup(N,model=eqs,threshold='v>Vt',reset='v=Vr;gK2=1')
    neurons.v=Vr
    neurons.gK=1./(1+exp((Va-El)/ka))
    neurons.tauK=400*ms+uniform(N)*tauK_spread
    alpha=(El-Vt)/(Vt-EK)
    neurons.gmax=alpha*(minx+(maxx-minx)*rand(N))
    neurons.tau=30*ms+uniform(N)*tau_spread
    
    # Store the value of state variables at rest
    print "Calculate resting state"
    run(2*second)
    rest=zeros(neurons._S.shape)
    rest[:]=neurons._S
    
    # Postsynaptic neurons (noisy coincidence detectors)
    eqs_post='''
    dv/dt=(n-v)/tau_cd : 1
    dn/dt=-n/tau_n+sigma*(2/tau_n)**.5*xi : 1
    '''
    postneurons=NeuronGroup(Nout,model=eqs_post,threshold=1,reset=0,refractory=refractory)
    
    # Random connections between pre and post-synaptic neurons
    C=Connection(neurons,postneurons,'v',sparseness=Nsynapses*1./N,weight=w0)
    
    # STDP
    eqs_stdp='''
    dApre/dt=-Apre/tau_pre : 1
    Apost : 1
    '''
    pre='''
    Apre+=a_pre
    w+=0 #b_pre*w
    '''
    post='''
    Apost+=0
    w+=Apre+b_post*w
    '''
    stdp=STDP(C,eqs_stdp,pre=pre,post=post,wmax=Inf)
    
    # Record the evolution of synaptic weights
    MC=ConnectionMonitor(C,store=True,clock=EventClock(dt=record_period))
    
    print "Learning..."
    # Series of inhibitory pulses
    for i in range(Npulses):
        print "pulse",i+1
        duration=200*ms+rand()*300*ms # random stimulus duration
        neurons.ginh=ginh_max
        run(duration)
        C.W.alldata[:]=C.W.alldata+C.W.alldata*b_pre # homeostasis (synaptic scaling)
        neurons.ginh=0
        run(rest_time) # let neurons spike
        neurons._S[:]=rest # reset (to save time)
    
    # Figure (4D)
    neuron=0
    wsave=[(t,M.todense()) for (t,M) in MC.values]
    W=array(zip(*wsave)[1])
    weights=W[:,:,neuron]
    
    # Evolution of all synaptic weights for this neuron
    for i in range(weights.shape[1]):
        plot(arange(len(weights[:,i,]))*record_period,weights[:,i,],'k')
    xlim(0,weights.shape[0]*float(record_period))
    ylim(0,1)
    
    show()