sim_tutorial3.cpp File Reference

A minimal example for simulating a plastic balanced network model. More...

#include "auryn.h"

Include dependency graph for sim_tutorial3.cpp:

Functions
int	main (int ac, char *av[])

Detailed Description

A minimal example for simulating a plastic balanced network model.

This is the code for the Tutorial 3. The tutorials can be found at http://www.fzenke.net/auryn/doku.php?id=tutorials:start

Function Documentation

main()

int main	(	int	ac,
		char *	av[]
	)

{
        // Initialize Auryn kernel
        auryn_init( ac, av );

        // Set master random seed
        sys->set_master_seed(42);

        // Initialize excitatory and inhibitory network neurons
        int nb_exc_neurons = 20000;
        int nb_inh_neurons = nb_exc_neurons/4;
        IFGroup * neurons_exc = new IFGroup(nb_exc_neurons);
        neurons_exc->set_name("exc neurons");
        neurons_exc->get_state_vector("g_nmda")->set_random();
        IFGroup * neurons_inh = new IFGroup(nb_inh_neurons);
        neurons_inh->set_tau_mem(5e-3);
        neurons_inh->set_name("inh neurons");

        // Initialize Poisson input population (PoissonGroup)
        int nb_input_neurons = 5000;
        float poisson_rate = 2.0;
        PoissonGroup * poisson = new PoissonGroup(nb_input_neurons,poisson_rate);

        // Connect Poisson input to exc population
        float weight = 0.2; // conductance amplitude in units of leak conductance
        float sparseness = 0.05; // probability of connection
        SparseConnection * con_ext_exc = new SparseConnection(poisson,neurons_exc,weight,sparseness,GLUT);

        // Set up recurrent connections
        float gamma = 4.0;

        // plasticity parameters 
        float tau_hom = 10.0; // timescale of homeostatic sliding threshold
        float eta_rel = 0.1; // relative learning rate
        float kappa = 1.0;   // target rate
        TripletConnection * con_ee = new TripletConnection(neurons_exc,neurons_exc,weight,sparseness,tau_hom,eta_rel,kappa);
        con_ee->set_transmitter(GLUT);
        con_ee->stdp_active = false;
        SparseConnection * con_ei = new SparseConnection(neurons_exc,neurons_inh,weight,sparseness,GLUT);
        SparseConnection * con_ie = new SparseConnection(neurons_inh,neurons_exc,gamma*weight,sparseness,GABA);
        SparseConnection * con_ii = new SparseConnection(neurons_inh,neurons_inh,gamma*weight,sparseness,GABA);


        // Initialize monitors which record information to file
        
        // Set up to display the population firing rate in the progress bar
        sys->set_online_rate_monitor_id(neurons_exc->get_uid());
        
        // Record input and output spikes
        SpikeMonitor * exc_spike_mon = new SpikeMonitor( neurons_exc, sys->fn("exc","ras") );

        // Record membrane voltage of one of the neurons
        VoltageMonitor * voltage_mon = new VoltageMonitor( neurons_exc, 0, sys->fn("neuron","mem") );
        voltage_mon->record_for(2); // only record for 2 seconds

        // Add a population rate monitor
        PopulationRateMonitor * prate_mon_exc = new PopulationRateMonitor(neurons_exc, sys->fn("exc","prate"));
        PopulationRateMonitor * prate_mon_inh = new PopulationRateMonitor(neurons_inh, sys->fn("inh","prate"));

        // Run the simulation for 10 seconds
        sys->run(10);

        // Run the simulation for 100 seconds
        con_ee->stdp_active = true;
        sys->run(100);

        // Shut down Auryn kernel
        auryn_free();
}

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