examples-twister_MicheleGiugliano.txt

.. currentmodule:: brian

.. index::
   pair: example usage; subplot
   pair: example usage; plot
   pair: example usage; run
   pair: example usage; PopulationRateMonitor
   pair: example usage; trace
   pair: example usage; show
   pair: example usage; raster_plot
   pair: example usage; SimpleCustomRefractoriness
   pair: example usage; Connection
   pair: example usage; SpikeMonitor
   pair: example usage; NeuronGroup
   pair: example usage; ylim
   pair: example usage; StateMonitor

.. _example-twister_MicheleGiugliano:

Example: MicheleGiugliano (twister)
===================================

Michele Giugliano's entry for the 2012 Brian twister.

::

    #
    # Figure5B - from Giugliano et al., 2004
    # Journal of Neurophysiology 92(2):977-96
    #
    # implemented by Eleni Vasilaki <e.vasilaki@sheffield.ac.uk> and
    # Michele Giugliano <michele.giugliano@ua.ac.be>
    #
    # A sparsely connected network of excitatory neurons, interacting
    # via current-based synaptic interactions, and incorporating 
    # spike-frequency adaptation, is simulated.
    #
    # Its overall emerging firing rate activity replicates some of the features of
    # spontaneous patterned electrical activity, observed experimentally in cultured 
    # networks of neurons dissociated from the neocortex.
    #
    from brian import *
    
    # Parameters of the simulation
    T    = 30000 *ms     # life time of the simulation
    N    = 100           # total number of (excitatory) integrate-and-fire model neurons in the network
    
    # Parameters of each model neuron, voltage dynamics
    C    = 67.95 *pF      # Membrane capacitance of single model neurons
    tau  = 22.25  *ms     # Membrane time-constant of single model neurons
    H    = 2.39 *mV       # Reset voltage, mimicking hyperpolarization potential following a spike
    theta= 20 *mV         # Threshold voltage for spike initiation
    tauarp=7.76 *ms       # Absolute refractory period
    
    # Parameters of each model neuron, spike-frequency adaptation dynamics
    taua = 2100 *ms       # Adaptation time constant
    a    = 0.75 *pA       # Adaptation scaling factor - NO ADAPTATION
    D    = 1*ms           # Unit consistency factor        
    temp = 1. *ms**(-.5)  # Unit consistency factor         
    
    # Parameters of network connectivity
    Cee  = 0.38           # Sparseness of all-to-all random connectivity
    taue = 5 *ms          # Decay time constant of excitatory EPSPs
    delta= 1.5 * ms       # Conduction+synaptic propagation delay
    J    = 14.5* pA       # Strenght of synaptic coupling, up to 18 *pA
    
    # Parameters of background synaptic activity, modelled as a identical and independent noisy extra-input to each model neuron
    m0   = 25.1 *pA       # Mean background input current
    s0   = 92 *pA         # Std dev of the (noisy) background input current
    
    # Each model neuron is described as a leaky integrate-and-fire with adaptation and current-driven synapses
    eqs = """
    dv/dt  = - v / tau - a/C * x  + Ie/C + (m0 + s0 * xi / temp)/C  : mV
    dx/dt  = -x/taua   : 1
    dIe/dt = -Ie/taue  : pA
    """
    
    # Custom refractory mechanisms are employed here, to allow the membrane potential to be clamped to the reset value H
    def myresetfunc(P, spikes):
     P.v[spikes] = H   #reset voltage 
     P.x[spikes] += 1  #low pass filter of spikes (adaptation mechanism)
    
    SCR = SimpleCustomRefractoriness(myresetfunc, tauarp, state='v')
     
    # The population of identical N model neuon is defined now
    P = NeuronGroup(N, model=eqs, threshold=theta, reset=SCR)
    
    # The interneuronal connectivity is defined now
    Ce = Connection(P, P, 'Ie', weight=J, sparseness=Cee, delay=delta)
    
    # Initialization of the state variables, for each model neuron
    P.v    = rand(len(P)) * 20 * mV  #membrane potential
    P.x    = rand(len(P)) * 2        #low pass filter of spikes
    P.Ie   = 0 *pA                   #excitatory synaptic input
    
    # Definition of tools for plotting and visualization of single neuron and population quantities
    R      = PopulationRateMonitor(P)
    M      = SpikeMonitor(P)
    trace  = StateMonitor(P, 'v', record=0)
    tracex = StateMonitor(P, 'x', record=0)
    
    print "Simulation running... (long-lasting simulation: be patient)"
    run(T)
    
    print "Simulation completed! If you did not see any firing rate population burst (lower panel), then slightly increase J!"
    
    # Plot nice spikes - adapted from Brette's code
    vm       = trace[0]
    spikes0  = [t for i,t in M.spikes if i==0]
    for i in range(0,len(spikes0)):
        k = int(spikes0[i] / defaultclock.dt)
        vm[k] = 80 * mV
        
    subplot(311) #membrane potential of neuron 0
    plot(trace.times / ms, vm / mV - 60)
    
    subplot(312) #raster plot
    raster_plot(M) 
    
    subplot(313) #smoothed population rate 
    plot(R.times / ms, R.smooth_rate(5*ms) / Hz, tracex.times / ms, tracex[0] * 10)
    ylim(0, 120)
    
    show()