Auryn simulator

This page is read only. You can view the source, but not change it. Ask your administrator if you think this is wrong.
====== sim_isp_orig ======

This simulation is the adapted from the network used to generate Figure 4 in [1]. It simulates a balanced network of 10,000 sparsely connected integrate-and-fire neurons[2] and features 
plastic inhibitory-exciatory synapses. 

{{:examples:isp_fig4.png?300|}}

[1] Vogels, T.P., Abbott, L.F., 2005. Signal propagation and logic gating in networks of integrate-and-fire neurons. J Neurosci 25, 10786.
[[http://www.ncbi.nlm.nih.gov/pubmed/16291952|PubMed]]

[2] Vogels, T.P., Sprekeler, H., Zenke, F., Clopath, C., Gerstner, W., 2011. Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks. Science 334, 1569 –1573.
[[http://www.ncbi.nlm.nih.gov/pubmed/22075724|PubMed]]

===== Running the program =====

To run the program after [[manual:start#first steps|compilation]] it suffices to call it as follows
<code shell>
./sim_isp_orig
</code>
this will trigger a 1000s simulation which creates the following output files
<code shell>
   4 -rw-r--r-- 1 zenke lcn1     954 Jan 20 10:52 out.0.log
5456 -rw-r--r-- 1 zenke lcn1 5555044 Jan 20 10:52 out.0_e.ras
1412 -rw-r--r-- 1 zenke lcn1 1433909 Jan 20 10:52 out.0_i.ras
 936 -rw-r--r-- 1 zenke lcn1  951025 Jan 20 10:52 out.0.volt
 888 -rw-r--r-- 1 zenke lcn1  901395 Jan 20 10:52 out.0.ampa
 928 -rw-r--r-- 1 zenke lcn1  942420 Jan 20 10:52 out.0.gaba
</code>

To see inhibitory plasticity in action the network can be initialized with small inithal I-E inhibitory weights. Try running the network with the following parameter
<code shell>
./sim_isp_orig --winh 0.01
</code>
if you then plot the spiking activity recorded in ''out.0_e.ras'' after a couple of seconds it will look highly synchronous and pathologic as in the following 

{{:examples:isp_orig_out1.png?300|}}

if you wait for roughly a minute this type of activity will give way to asynchronous irregular activity of the following type

{{:examples:isp_orig_out2.png?300|}}

==== Running in parallel ====

This version of the simulation can be run in parallel like any other Auryn simulation. See  [[manual:parallel_execution|Running simulations in parallel]] for details.


===== The important bits =====

The following snipped of code first initializes two [[manual:NeuronGroup|NeuronGroups]] of type [[manual:TIFGroup]] and then initializes the neuronal membrane potentials to random values that follow a Gaussian distribution centered at -60mV with a standard deviation of 5mV. It furthermore creates an array of Poisson neurons called [[manual:PoissonGroup]], before moving on to set up the inhibitory and excitatory connections. Note that the connections from the inhibitory to the excitatory population is of type [[manual:SymmetricSTDPConnection]] which implements inhibitory synaptic plasticity as specified in Vogels et al. 2011. 

<code c++>
 logger->msg("Setting up neuron groups ...",PROGRESS,true);
    TIFGroup * neurons_e = new TIFGroup(NE);
    TIFGroup * neurons_i = new TIFGroup(NI);
    neurons_e->random_mem(-60e-3,5e-3);
    neurons_i->random_mem(-60e-3,5e-3);
    PoissonGroup * poisson = new PoissonGroup(NP,poisson_rate);


    logger->msg("Setting up connections ...",PROGRESS,true);
    SparseConnection * con_ei = new SparseConnection(neurons_e,neurons_i,wei*w,sparseness,GLUT);
    SparseConnection * con_ii = new SparseConnection(neurons_i,neurons_i,gamma*w,sparseness,GABA);

    SparseConnection * con_exte = new SparseConnection(poisson,neurons_e,0,sparseness_afferents,GLUT);

    SparseConnection * con_ee = new SparseConnection(neurons_e,neurons_e,w,sparseness,GLUT);
    SymmetricSTDPConnection * con_ie = new SymmetricSTDPConnection(neurons_i,neurons_e,
            gamma*w,sparseness,
            gamma*eta,kappa,tau_stdp,wmax,
            GABA);
</code>

Note also that in the above code the [[manual:PoissonGroup]] is connected with zero weights only. Certain of the these random sparse zero weights can be changed to non-zero values later in the simulation thus providing external input to the network. By default external input is given by external input currents and the [[manual:PoissonGroup]] is simply dragged along (it would indeed be more efficient to not define it at all in this case). The actual setting up of a stimulus happens here
<code c++>
// stimulus
    if (!stimfile.empty()) {
        char ch;
        NeuronID counter = 0;
        ifstream fin(stimfile.c_str());
        while (!fin.eof() && counter<NE) {
            ch = fin.get();
            if (ch == '1') {
                if (poisson_stim==true) {
                    for (int i = 0 ; i < NP ; ++i)
                        con_exte->set(i,counter,w_ext);
                } else {
                    neurons_e->set_bg_current(counter,chi*bg_current);
                }
            }
            counter++;
        }
        fin.close();
    }
</code>

To run the network and keep activity alive even in the case of highly synchronized activity all cells in the network receive depolarizing external currents. This is set up here
<code c++>
for ( int j = 0; j < NE ; j++ ) {
  neurons_e->set_bg_current(j,bg_current);
}
 
for ( int j = 0; j < NI ; j++ ) {
  neurons_i->set_bg_current(j,bg_current);
}
</code>
note however that ''set_bg_current'' is not a generic function of [[manual:NeuronGroup]], but a pecularity of the neuron model implemented as [[manual:TIFGroup]]. To give currents to arbitrary [[manual:NeuronGroup|NeuronGroups]] a [[manual:CurrentInjector]] has to be used.


===== The full program =====
<code c++>
/* 
* Copyright 2014 Friedemann Zenke
*
* This file is part of Auryn, a simulation package for plastic
* spiking neural networks.
* 
* Auryn is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
* 
* Auryn is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
* GNU General Public License for more details.
* 
* You should have received a copy of the GNU General Public License
* along with Auryn.  If not, see <http://www.gnu.org/licenses/>.
*/
#include <iomanip>
#include <stdlib.h>
#include <string>
#include <iterator>

#include <boost/program_options.hpp>

#include "auryn_global.h"
#include "auryn_definitions.h"
#include "System.h"
#include "Logger.h"

#include "NeuronGroup.h"
#include "TIFGroup.h"
#include "PoissonGroup.h"
#include "SparseConnection.h"
#include "SymmetricSTDPConnection.h"
#include "StateMonitor.h"
#include "SpikeMonitor.h"
#include "RateChecker.h"

#define NE 8000
#define NI 2000
#define NP 1000
#define NSTIM 20

using namespace std;
namespace po = boost::program_options;

int main(int ac, char* av[]) 
{
	double w = 0.3 ;
	double w_ext = w ;
	double gamma = 10. ;
	double wmax = 10*gamma*w;

	double sparseness = 0.02 ;

	double eta = 1e-4 ;
	double kappa = 3. ;
	double tau_stdp = 20e-3 ;
	bool stdp_active = true;
	bool poisson_stim = false;
	double winh = -1;
	double wei = 1;
	double chi = 10.;
	double bg_current = 2e-2;

	double poisson_rate = 100.;
	double sparseness_afferents = 0.05;

	bool quiet = false;
	double simtime = 1000. ;
	NeuronID record_neuron = 30;
	// handle command line options
	
	string infilename = "";
	string outputfile = "out";
	string stimfile = "";
	string strbuf ;

	int errcode = 0;

    try {

        po::options_description desc("Allowed options");
        desc.add_options()
            ("help", "produce help message")
            ("quiet", "quiet mode")
            ("load", po::value<string>(), "input weight matrix")
            ("out", po::value<string>(), "output filename")
            ("stimfile", po::value<string>(), "stimulus file")
            ("eta", po::value<double>(), "learning rate")
            ("kappa", po::value<double>(), "target rate")
            ("simtime", po::value<double>(), "simulation time")
            ("active", po::value<bool>(), "toggle learning")
            ("poisson", po::value<bool>(), "toggle poisson stimulus")
            ("winh", po::value<double>(), "inhibitory weight multiplier")
            ("wei", po::value<double>(), "ei weight multiplier")
            ("chi", po::value<double>(), "chi current multiplier")
        ;

        po::variables_map vm;        
        po::store(po::parse_command_line(ac, av, desc), vm);
        po::notify(vm);    

        if (vm.count("help")) {
            cout << desc << "\n";
            return 1;
        }

        if (vm.count("quiet")) {
			quiet = true;
        } 

        if (vm.count("load")) {
            cout << "input weight matrix " 
                 << vm["load"].as<string>() << ".\n";
			infilename = vm["load"].as<string>();
        } 

        if (vm.count("out")) {
            cout << "output filename " 
                 << vm["out"].as<string>() << ".\n";
			outputfile = vm["out"].as<string>();
        } 

        if (vm.count("stimfile")) {
            cout << "stimfile filename " 
                 << vm["stimfile"].as<string>() << ".\n";
			stimfile = vm["stimfile"].as<string>();
        } 

        if (vm.count("eta")) {
            cout << "eta set to " 
                 << vm["eta"].as<double>() << ".\n";
			eta = vm["eta"].as<double>();
        } 

        if (vm.count("kappa")) {
            cout << "kappa set to " 
                 << vm["kappa"].as<double>() << ".\n";
			kappa = vm["kappa"].as<double>();
        } 

        if (vm.count("simtime")) {
            cout << "simtime set to " 
                 << vm["simtime"].as<double>() << ".\n";
			simtime = vm["simtime"].as<double>();
        } 

        if (vm.count("active")) {
            cout << "stdp active : " 
                 << vm["active"].as<bool>() << ".\n";
			stdp_active = vm["active"].as<bool>();
        } 

        if (vm.count("poisson")) {
            cout << "poisson active : " 
                 << vm["poisson"].as<bool>() << ".\n";
			poisson_stim = vm["poisson"].as<bool>();
        } 


        if (vm.count("winh")) {
            cout << "inhib weight multiplier : " 
                 << vm["winh"].as<double>() << ".\n";
			winh = vm["winh"].as<double>();
        } 

        if (vm.count("wei")) {
            cout << "ei weight multiplier : " 
                 << vm["wei"].as<double>() << ".\n";
			wei = vm["wei"].as<double>();
        } 

        if (vm.count("chi")) {
            cout << "chi multiplier : " 
                 << vm["chi"].as<double>() << ".\n";
			chi = vm["chi"].as<double>();
        } 

    }
    catch(exception& e) {
        cerr << "error: " << e.what() << "\n";
        return 1;
    }
    catch(...) {
        cerr << "Exception of unknown type!\n";
    }

	// BEGIN Global definitions
	mpi::environment env(ac, av);
	mpi::communicator world;
	communicator = &world;

	stringstream oss;
	oss << outputfile << "." << world.rank();
	outputfile = oss.str();
	oss << ".log";
	string logfile = oss.str();
	logger = new Logger(logfile,world.rank());

	sys = new System(&world);
	// END Global definitions



	logger->msg("Setting up neuron groups ...",PROGRESS,true);
	TIFGroup * neurons_e = new TIFGroup(NE);
	TIFGroup * neurons_i = new TIFGroup(NI);
	neurons_e->random_mem(-60e-3,5e-3);
	neurons_i->random_mem(-60e-3,5e-3);
	PoissonGroup * poisson = new PoissonGroup(NP,poisson_rate);


	logger->msg("Setting up connections ...",PROGRESS,true);
	SparseConnection * con_ei = new SparseConnection(neurons_e,neurons_i,wei*w,sparseness,GLUT);
	SparseConnection * con_ii = new SparseConnection(neurons_i,neurons_i,gamma*w,sparseness,GABA);

	SparseConnection * con_exte = new SparseConnection(poisson,neurons_e,0,sparseness_afferents,GLUT);
	
	SparseConnection * con_ee = new SparseConnection(neurons_e,neurons_e,w,sparseness,GLUT);
	SymmetricSTDPConnection * con_ie = new SymmetricSTDPConnection(neurons_i,neurons_e,
			gamma*w,sparseness,
			gamma*eta,kappa,tau_stdp,wmax,
			GABA);

	if (!infilename.empty()) {
		sys->load_network_state(infilename);
	}

	if (winh>=0)
		con_ie->set_all(winh);

	logger->msg("Setting up monitors ...",PROGRESS,true);
	strbuf = outputfile;
	strbuf += "_e.ras";
	SpikeMonitor * smon_e = new SpikeMonitor( neurons_e , strbuf.c_str() );

	strbuf = outputfile;
	strbuf += "_i.ras";
	SpikeMonitor * smon_i = new SpikeMonitor( neurons_i, strbuf.c_str() );
	smon_i->set_offset(NE);

	strbuf = outputfile;
	strbuf += ".volt";
	StateMonitor * vmon = new StateMonitor( neurons_e, record_neuron, "mem", strbuf.c_str() );

	strbuf = outputfile;
	strbuf += ".ampa";
	StateMonitor * amon = new StateMonitor( neurons_e, record_neuron, "g_ampa", strbuf.c_str() );

	strbuf = outputfile;
	strbuf += ".gaba";
	StateMonitor * gmon = new StateMonitor( neurons_e, record_neuron, "g_gaba", strbuf.c_str() );

	RateChecker * chk = new RateChecker( neurons_e , 0.1 , 1000. , 100e-3);

	for ( int j = 0; j < NE ; j++ ) {
	  neurons_e->set_bg_current(j,bg_current);
	}

	for ( int j = 0; j < NI ; j++ ) {
	  neurons_i->set_bg_current(j,bg_current);
	}
	
	// stimulus
	if (!stimfile.empty()) {
		char ch;
		NeuronID counter = 0;
		ifstream fin(stimfile.c_str());
		while (!fin.eof() && counter<NE) { 
			ch = fin.get(); 
			if (ch == '1') {
				if (poisson_stim==true) {
					for (int i = 0 ; i < NP ; ++i)
						con_exte->set(i,counter,w_ext);
				} else {
					neurons_e->set_bg_current(counter,chi*bg_current);
				}
			}
			counter++;
		} 
		fin.close();
	}


	logger->msg("Simulating ...",PROGRESS,true);
	con_ie->stdp_active = stdp_active;

	sys->run(simtime);

	logger->msg("Saving network state ...",PROGRESS,true);
	sys->save_network_state(outputfile);

	logger->msg("Freeing ...",PROGRESS,true);
	delete sys;

	logger->msg("Exiting ...",PROGRESS,true);
	return errcode;
}
</code>