int main(int ac, char* av[])
{
double w = 0.16;
double w_ext = w;
double wmax = 1.0;
double w_ee = w;
double w_ei = w;
double gamma = 1.0;
double w_ie = gamma;
double w_ii = gamma;
NeuronID ne = 20000;
NeuronID ni = ne/4;
double sparseness = 0.05;
double kappa = 3.;
bool quiet = false;
bool scaling = false;
bool wmatdump = false;
bool loadbalance = false;
bool wall = false;
double tau_chk = 100e-3;
double simtime = 3600.;
double stimtime = simtime;
double wmat_interval = 600.;
double ampa_nmda_ratio = 1.0;
double wstim = 0.1;
NeuronID psize = 200;
NeuronID hsize = 100;
NeuronID offset = 0;
string patfile = "";
string prefile = "";
string currentfile = "";
double stimfreq = 10;
bool corr = false;
int plen = 3;
int hlen = 3;
double ampl = 1.0;
bool recall = false;
bool decay = false;
bool adapt = false;
bool noisyweights = false;
bool switchweights = false;
bool ei_plastic = false;
double bg_rate = 2;
bool fast = false;
AurynWeight wdecay = w;
double tau_decay = 3600.;
double tau_hom = 50.;
double eta = 1;
double onperiod = 2;
double offperiod = 30;
double scale = 1;
int n_strengthen = 0;
vector<Connection*> corr_connections;
string dir = ".";
string stimfile = "";
string label = "";
string infilename = "";
const char * file_prefix = "bg2";
char strbuf [255];
string msg;
int errcode = 0;
try {
po::options_description desc("Allowed options");
desc.add_options()
("help", "produce help message")
("quiet", "quiet mode")
("scaling", "scaling mode")
("balance", "activate load balance")
("load", po::value<string>(), "input weight matrix")
("patfile", po::value<string>(), "pattern file for StimulusGroup")
("prefile", po::value<string>(), "pattern file for loading patterns")
("onperiod", po::value<double>(), "mean stimulus on period")
("offperiod", po::value<double>(), "mean stimulus off period")
("wmat", "wmat dump mode")
("eta", po::value<double>(), "learning rate")
("bgrate", po::value<double>(), "PoissonGroup external firing rate")
("sparseness", po::value<double>(), "overall network sparseness")
("scale", po::value<double>(), "learning rate")
("tau_hom", po::value<double>(), "homeostatic time constant")
("kappa", po::value<double>(), "target rate")
("simtime", po::value<double>(), "simulation time")
("dir", po::value<string>(), "output dir")
("label", po::value<string>(), "output label")
("wee", po::value<double>(), "wee")
("wei", po::value<double>(), "wei")
("wie", po::value<double>(), "wie")
("wii", po::value<double>(), "wii")
("wmax", po::value<double>(), "wmax")
("ampa", po::value<double>(), "ampa nmda ratio")
("strengthen", po::value<int>(), "connections to strengthen by 10")
("ne", po::value<int>(), "no of exc units")
("stimfile", po::value<string>(), "stimulus ras file")
("wstim", po::value<double>(), "weight of stimulus connections")
("stimtime", po::value<double>(), "time of stimulus on")
("decay", "decay triplet connections")
("corr", "add correlated inputs")
("ampl", po::value<double>(), "recall stim amplitude")
("psize", po::value<int>(), "block size for correlated inputs")
("plen", po::value<int>(), "number of blocks feed-forward")
("hsize", po::value<int>(), "block size for correlated inputs (hebbian assembly)")
("hlen", po::value<int>(), "number of blocks Hebbian")
("offset", po::value<int>(), " offset for corr stim")
("recall", "add correlated inputs to first patterns")
("stimfreq", po::value<double>(), "CorrelatedPoissonGroup frequency default = 100")
("dconstant", po::value<double>(), "decay time constant for decaying triplet connections")
("wdecay", po::value<double>(), "wdecay for decay triplet connections")
("chk", po::value<double>(), "checker time constant")
("adapt", "adapting excitatory neurons")
("wall", "enable monitoring of wall clock time")
("noisyweights", "enables noisyweights for mean field checks")
("switchweights", "switches first weights in each weight matrix")
("fast", "turn off some of the monitors to run faster")
("eiplastic", "make EI connection plastic")
;
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("scaling")) {
scaling = true;
}
if (vm.count("balance")) {
cout << "load balancing active" << endl;
loadbalance = true;
}
if (vm.count("load")) {
cout << "load from matrix "
<< vm["load"].as<string>() << ".\n";
infilename = vm["load"].as<string>();
}
if (vm.count("patfile")) {
cout << "PatternFile is "
<< vm["patfile"].as<string>() << ".\n";
patfile = vm["patfile"].as<string>();
}
if (vm.count("prefile")) {
cout << "Preload patternfile is "
<< vm["prefile"].as<string>() << ".\n";
prefile = vm["prefile"].as<string>();
}
if (vm.count("wmat")) {
wmatdump = true;
cout << "wmat dump mode" << endl;
}
if (vm.count("onperiod")) {
cout << "onperiod set to "
<< vm["onperiod"].as<double>() << ".\n";
onperiod = vm["onperiod"].as<double>();
}
if (vm.count("offperiod")) {
cout << "offperiod set to "
<< vm["offperiod"].as<double>() << ".\n";
offperiod = vm["offperiod"].as<double>();
}
if (vm.count("eta")) {
cout << "eta set to "
<< vm["eta"].as<double>() << ".\n";
eta = vm["eta"].as<double>();
}
if (vm.count("bgrate")) {
cout << "bgrate set to "
<< vm["bgrate"].as<double>() << ".\n";
bg_rate = vm["bgrate"].as<double>();
}
if (vm.count("sparseness")) {
cout << "sparseness set to "
<< vm["sparseness"].as<double>() << ".\n";
sparseness = vm["sparseness"].as<double>();
}
if (vm.count("scale")) {
cout << "scale set to "
<< vm["scale"].as<double>() << ".\n";
scale = vm["scale"].as<double>();
}
if (vm.count("tau_hom")) {
cout << "tau_hom set to "
<< vm["tau_hom"].as<double>() << ".\n";
tau_hom = vm["tau_hom"].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>();
stimtime = simtime;
}
if (vm.count("dir")) {
cout << "dir set to "
<< vm["dir"].as<string>() << ".\n";
dir = vm["dir"].as<string>();
}
if (vm.count("label")) {
cout << "label set to "
<< vm["label"].as<string>() << ".\n";
label = vm["label"].as<string>();
}
if (vm.count("wee")) {
cout << "wee set to "
<< vm["wee"].as<double>() << ".\n";
w_ee = vm["wee"].as<double>();
}
if (vm.count("wei")) {
cout << "wei set to "
<< vm["wei"].as<double>() << ".\n";
w_ei = vm["wei"].as<double>();
}
if (vm.count("wie")) {
cout << "wie set to "
<< vm["wie"].as<double>() << ".\n";
w_ie = vm["wie"].as<double>();
}
if (vm.count("wii")) {
cout << "wii set to "
<< vm["wii"].as<double>() << ".\n";
w_ii = vm["wii"].as<double>();
}
if (vm.count("wmax")) {
cout << "wmax set to "
<< vm["wmax"].as<double>() << ".\n";
wmax = vm["wmax"].as<double>();
}
if (vm.count("ampa")) {
cout << "ampa set to "
<< vm["ampa"].as<double>() << ".\n";
ampa_nmda_ratio = vm["ampa"].as<double>();
}
if (vm.count("strengthen")) {
cout << "strengthen set to "
<< vm["strengthen"].as<int>() << ".\n";
n_strengthen = vm["strengthen"].as<int>();
}
if (vm.count("ne")) {
cout << "ne set to "
<< vm["ne"].as<int>() << ".\n";
ne = vm["ne"].as<int>();
ni = ne/4;
}
if (vm.count("stimfile")) {
cout << "stimfile set to "
<< vm["stimfile"].as<string>() << ".\n";
stimfile = vm["stimfile"].as<string>();
}
if (vm.count("wstim")) {
cout << "wstim set to "
<< vm["wstim"].as<double>() << ".\n";
wstim = vm["wstim"].as<double>();
}
if (vm.count("stimtime")) {
cout << "stimtime set to "
<< vm["stimtime"].as<double>() << ".\n";
stimtime = vm["stimtime"].as<double>();
}
if (vm.count("corr")) {
cout << "enabling corr " << endl;
corr = true;
}
if (vm.count("ampl")) {
cout << "ampl set to "
<< vm["ampl"].as<double>() << ".\n";
ampl = vm["ampl"].as<double>();
}
if (vm.count("psize")) {
cout << "psize set to "
<< vm["psize"].as<int>() << ".\n";
psize = vm["psize"].as<int>();
}
if (vm.count("plen")) {
cout << "plen set to "
<< vm["plen"].as<int>() << ".\n";
plen = vm["plen"].as<int>();
}
if (vm.count("hsize")) {
cout << "hsize set to "
<< vm["hsize"].as<int>() << ".\n";
hsize = vm["hsize"].as<int>();
}
if (vm.count("hlen")) {
cout << "hlen set to "
<< vm["hlen"].as<int>() << ".\n";
hlen = vm["hlen"].as<int>();
}
if (vm.count("offset")) {
cout << "offset set to "
<< vm["offset"].as<int>() << ".\n";
offset = vm["offset"].as<int>();
}
if (vm.count("recall")) {
cout << "enabling recall " << endl;
recall = true;
}
if (vm.count("stimfreq")) {
cout << "stimfreq set to "
<< vm["stimfreq"].as<double>() << ".\n";
stimfreq = vm["stimfreq"].as<double>();
}
if (vm.count("decay")) {
cout << "weight decay on " << endl;
decay = true;
}
if (vm.count("dconstant")) {
cout << "dconstant set to "
<< vm["dconstant"].as<double>() << ".\n";
tau_decay = vm["dconstant"].as<double>();
}
if (vm.count("wdecay")) {
cout << "wdecay set to "
<< vm["wdecay"].as<double>() << ".\n";
wdecay = vm["wdecay"].as<double>();
}
if (vm.count("chk")) {
cout << "chk set to "
<< vm["chk"].as<double>() << ".\n";
tau_chk = vm["chk"].as<double>();
}
if (vm.count("adapt")) {
cout << "adaptation on " << endl;
adapt = true;
}
if (vm.count("wall")) {
cout << "real time wall clock monitoring enabled " << endl;
wall = true;
}
if (vm.count("noisyweights")) {
cout << "noisyweights on " << endl;
noisyweights = true;
}
if (vm.count("switchweights")) {
cout << "switchweights on " << endl;
switchweights = true;
}
if (vm.count("fast")) {
cout << "fast on " << endl;
fast = true;
}
if (vm.count("eiplastic")) {
cout << "eiplastic on " << endl;
ei_plastic = true;
}
}
catch(exception& e) {
cerr << "error: " << e.what() << "\n";
return 1;
}
catch(...) {
cerr << "Exception of unknown type!\n";
}
if (scale != 1.) {
eta *= scale;
tau_hom /= scale;
simtime /= scale;
cout << "scaled by " << scale << endl;
}
double primetime = 3*tau_hom;
// BEGIN Global stuff
mpi::environment env(ac, av);
mpi::communicator world;
communicator = &world;
sprintf(strbuf, "%s/%s_e%.2et%.2f%s.%d.log", dir.c_str(), file_prefix, eta, tau_hom, label.c_str(), world.rank());
string logfile = strbuf;
logger = new Logger(logfile,world.rank(),PROGRESS,EVERYTHING);
sys = new System(&world);
// boost::filesystem::path p = av[0];
// string binaryname = p.stem().string();
// sys->set_simulation_name(binaryname);
// END Global stuff
if (!infilename.empty()) {
stringstream iss;
iss << infilename << "." << world.rank();
infilename = iss.str();
}
logger->msg("Setting up neuron groups ...",PROGRESS,true);
NeuronGroup * neurons_e;
if ( adapt ) {
neurons_e = new AIFGroup(ne);
((AIFGroup*)neurons_e)->set_ampa_nmda_ratio(ampa_nmda_ratio);
((AIFGroup*)neurons_e)->dg_adapt1=1.0;
} else {
if ( loadbalance )
neurons_e = new IFGroup(ne,1.2,(1.2*ne+ni));
else
neurons_e = new IFGroup(ne);
((IFGroup*)neurons_e)->set_ampa_nmda_ratio(ampa_nmda_ratio);
}
IFGroup * neurons_i = new IFGroup(ni);
// initialize membranes
neurons_i->set_tau_mem(10e-3);
neurons_e->random_mem(-60e-3,10e-3);
neurons_i->random_mem(-60e-3,10e-3);
((IFGroup*)neurons_i)->set_ampa_nmda_ratio(ampa_nmda_ratio);
SpikingGroup * poisson = new PoissonGroup(2500,bg_rate);
SparseConnection * con_exte = new SparseConnection(poisson, neurons_e, w_ext, sparseness, GLUT);
msg = "Setting up I connections ...";
logger->msg(msg,PROGRESS,true);
SparseConnection * con_ie = new SparseConnection(neurons_i,neurons_e,
w_ie,sparseness,GABA);
SparseConnection * con_ii = new SparseConnection(neurons_i,neurons_i,
w_ii,sparseness,GABA);
msg = "Setting up E connections ...";
logger->msg(msg,PROGRESS,true);
SparseConnection * con_ei;
if ( !ei_plastic ) {
con_ei = new SparseConnection(neurons_e,neurons_i,
w_ei, sparseness,GLUT);
} else {
if (infilename.empty()) {
con_ei = new TripletConnection(neurons_e,neurons_i,
w_ei, sparseness,
tau_hom, eta, kappa, wmax, GLUT);
} else {
string str;
str = infilename;
str += ".ei.wmat";
stringstream oss;
oss << "Loading weight matrix from " << str;
logger->msg(oss.str(),PROGRESS,true);
con_ei = new TripletConnection(neurons_e,neurons_i,
str.c_str(),
tau_hom, eta, kappa, wmax, GLUT);
}
}
TripletConnection * con_ee;
if (infilename.empty()) {
if (decay)
con_ee = new TripletDecayConnection(neurons_e,neurons_e,
w_ee, sparseness,
tau_hom, eta, tau_decay,
kappa, wdecay, wmax, GLUT);
else
con_ee = new TripletConnection(neurons_e,neurons_e,
w_ee, sparseness,
tau_hom, eta, kappa, wmax, GLUT);
// con_ee->set_min_weight(wmin);
if ( noisyweights )
con_ee->random_data(w_ee,w_ee/4);
for ( int i = 0 ; i < n_strengthen ; ++i ) {
con_ee->set_data(i,i*(wmax/n_strengthen));
}
}
else {
string str;
str = infilename;
str += ".wmat";
stringstream oss;
oss << "Loading weight matrix from " << str;
logger->msg(oss.str(),PROGRESS,true);
if (decay)
con_ee = new TripletDecayConnection(neurons_e,neurons_e,
str.c_str(),tau_hom,eta,tau_decay,kappa,wdecay,wmax,GLUT);
else
con_ee = new TripletConnection(neurons_e,neurons_e,
str.c_str(),tau_hom,eta,kappa,wmax,GLUT);
sprintf(strbuf, "%s.e.nstate", infilename.c_str());
neurons_e->load_from_file(strbuf);
sprintf(strbuf, "%s.i.nstate", infilename.c_str());
neurons_i->load_from_file(strbuf);
// primetime = 0;
}
// SparseConnection * con_exti = new SparseConnection(sys, poisson, neurons_i, w_ext, sparseness, GLUT);
msg = "Initializing traces ...";
logger->msg(msg,PROGRESS,true);
con_ee->set_hom_trace(kappa);
con_ee->seed(23);
// TODO
// con_ee->w->set_col(0,2*w_ee);
msg = "Setting up monitors ...";
logger->msg(msg,PROGRESS,true);
if (wmatdump) {
sprintf(strbuf, "%s/%s_e%.2et%.2f%s.%d.weight", dir.c_str(), file_prefix, eta, tau_hom, label.c_str(), world.rank());
WeightMatrixMonitor * wmatmon = new WeightMatrixMonitor( con_ee, strbuf , wmat_interval );
}
if ( !fast ) {
sprintf(strbuf, "%s/%s_e%.2et%.2f%s.%d.syn", dir.c_str(), file_prefix, eta, tau_hom, label.c_str(), world.rank());
WeightMonitor * wmon = new WeightMonitor( con_ee, strbuf, 10 );
for ( int i = 0 ; i < 5 ; ++i ) {
for ( int j = 0 ; j < 5 ; ++j ) {
vector<neuron_pair> sublist = con_ee->get_block(i*psize,(i+1)*psize,j*psize,(j+1)*psize);
sublist.resize(50); // only record a maximum of 50 connections from each block
wmon->add_to_list(sublist);
}
}
sprintf(strbuf, "%s/%s_e%.2et%.2f%s.%d.%c.ras", dir.c_str(), file_prefix, eta, tau_hom, label.c_str(), world.rank(), 'e');
SpikeMonitor * smon_e = new SpikeMonitor( neurons_e, strbuf , 2500);
// sprintf(strbuf, "%s/%s_e%.2et%.2f%s.%d.%c.ras", dir.c_str(), file_prefix, eta, tau_hom, label.c_str(), world.rank(), 'p');
// SpikeMonitor * smon_p = new SpikeMonitor( poisson, strbuf , 2500);
}
sprintf(strbuf, "%s/%s_e%.2et%.2f%s.%d.%c.prate", dir.c_str(), file_prefix, eta, tau_hom, label.c_str(), world.rank(), 'e');
PopulationRateMonitor * pmon_e = new PopulationRateMonitor( neurons_e, strbuf, 1.0 );
if ( wall ) {
sprintf(strbuf, "%s/%s_e%.2et%.2f%s.%d.rt", dir.c_str(), file_prefix, eta, tau_hom, label.c_str(), world.rank());
RealTimeMonitor * rtmon = new RealTimeMonitor( strbuf );
}
// sprintf(strbuf, "%s/%s_e%.2et%.2f%s.%d.%c.prate", dir.c_str(), file_prefix, eta, tau_hom, label.c_str(), world.rank(), 'i');
// PopulationRateMonitor * pmon_i = new PopulationRateMonitor( neurons_i, strbuf, 1.0 );
// sprintf(strbuf, "%s/%s_e%.2et%.2f%s.%d.%c.mem", dir.c_str(), file_prefix, eta, tau_hom, label.c_str(), world.rank(), 'e');
// VoltageMonitor * vmon_e = new VoltageMonitor( neurons_e, 33, strbuf );
// sprintf(strbuf, "%s/%s_e%.2et%.2f%s.%d.%c.ras", dir.c_str(), file_prefix, eta, tau_hom, label.c_str(), world.rank(), 'i');
// SpikeMonitor * smon_i = new SpikeMonitor( neurons_i, strbuf , 500);
RateChecker * chk = new RateChecker( neurons_e , 0.1 , 20.*kappa , tau_chk);
// Use the same time constant for the online rate estimate in the progress bar
sys->set_online_rate_monitor_id(0);
sys->set_online_rate_monitor_tau(tau_chk);
if ( scaling && (errcode==0) ) {
stringstream oss;
oss << "Changing cell input ... ";
logger->msg(oss.str(),PROGRESS,true);
sprintf(strbuf, "%s/%s_e%.2et%.2f%s.%d.pat", dir.c_str(), file_prefix, eta, tau_hom, label.c_str(), world.rank());
PatternMonitor * patmon = new PatternMonitor(neurons_e,strbuf,patfile.c_str(), 10, 1.);
PatternStimulator * patstim = new PatternStimulator(neurons_e,currentfile.c_str(),patfile.c_str());
sprintf(strbuf, "%s/%s_e%.2et%.2f%s.%d.scal", dir.c_str(), file_prefix, eta, tau_hom, label.c_str(), world.rank());
WeightMonitor * wmon_scal = new WeightMonitor( con_ee, 0, 0, strbuf, 1, ELEMENTLIST);
wmon_scal->add_to_list( con_ee->get_pre_partners(0) );
wmon_scal->add_to_list( con_ee->get_pre_partners(10) );
}
// if ( corr ) {
// sprintf(strbuf, "%s/%s_e%.2et%.2f%s.%d.%c.pat", dir.c_str(), file_prefix, eta, tau_hom, label.c_str(), world.rank(), 'c');
// PatternMonitor * pmon_c = new PatternMonitor( neurons_e, strbuf,corr_pat_file.c_str() );
// }
const double amplitude_fwd = 9.0*ampl;
const double amplitude_heb = 3.0*ampl;
CorrelatedPoissonGroup * corr_e;
CorrelatedPoissonGroup * corr_e2;
NeuronID extsize = 500;
double strength = 0.2;
if ( corr ) {
stringstream oss;
oss << "Activating correlated input ... ";
logger->msg(oss.str(),PROGRESS,true);
if (eta > 0)
con_ee->stdp_active = true;
if ( psize*plen ) {
corr_e = new CorrelatedPoissonGroup(extsize*plen,stimfreq,extsize,10e-3);
corr_e->set_amplitude(amplitude_fwd);
corr_e->set_timescale(5e-3);
// IdentityConnection * con_corr_e = new IdentityConnection(corr_e,neurons_e,w);
SparseConnection * con_corr_e = new SparseConnection(corr_e,neurons_e,GLUT);
con_corr_e->allocate_manually( plen*extsize*psize*sparseness*1.05 );
con_corr_e->w->clear();
for ( int i = 0 ; i < plen ; ++i )
con_corr_e->connect_block_random(w,sparseness,(i)*extsize,offset+(i+1)*extsize,(i)*psize,offset+(i+1)*psize);
con_corr_e->finalize();
corr_connections.push_back(con_corr_e);
SparseConnection * con_corr_i = new SparseConnection(corr_e,neurons_i,w*strength,sparseness*plen*psize/ni,GLUT);
// set up Weight monitor
sprintf(strbuf, "%s/%s_e%.2et%.2f%s.%d.%c.ras", dir.c_str(), file_prefix, eta, tau_hom, label.c_str(), world.rank(), 'c');
SpikeMonitor * smon_c = new SpikeMonitor( corr_e, strbuf , psize );
}
if ( hsize*hlen ) {
corr_e2 = new CorrelatedPoissonGroup(extsize*hlen,stimfreq,extsize,10);
corr_e2->set_amplitude(amplitude_heb);
corr_e2->set_timescale(100e-3);
SparseConnection * con_corr_e2 = new SparseConnection(corr_e2,neurons_e,GLUT);
con_corr_e2->allocate_manually( hlen*extsize*hsize*sparseness*1.05 );
con_corr_e2->w->clear();
for ( int i = 0 ; i < hlen ; ++i )
con_corr_e2->connect_block_random(w,sparseness,(i)*extsize,(i+1)*extsize,offset+(i)*hsize+plen*psize,offset+(i+1)*hsize+plen*psize);
con_corr_e2->finalize();
corr_connections.push_back(con_corr_e2);
SparseConnection * con_corr_i2 = new SparseConnection(corr_e2,neurons_i,w*strength,sparseness*hlen*hsize/ni,GLUT);
}
// disabling external random input
con_exte->set_block(0,2500,0,plen*psize+hlen*hsize,0.0);
} else {
if ( recall ) {
stringstream oss;
oss << "Activating recall input ... ";
logger->msg(oss.str(),PROGRESS,true);
if ( psize*plen ) {
corr_e = new CorrelatedPoissonGroup(extsize*plen,stimfreq,extsize,10e-3);
corr_e->set_amplitude(amplitude_fwd);
corr_e->set_timescale(5e-3);
// IdentityConnection * con_corr_e = new IdentityConnection(corr_e,neurons_e,w);
SparseConnection * con_corr_e = new SparseConnection(corr_e,neurons_e,GLUT);
con_corr_e->allocate_manually( plen*extsize*psize*sparseness*1.05 );
con_corr_e->w->clear();
for ( int i = 0 ; i < plen ; ++i )
con_corr_e->connect_block_random(w,sparseness,(i)*extsize,offset+(i+1)*extsize,(i)*psize,offset+(i+1)*psize);
con_corr_e->finalize();
corr_connections.push_back(con_corr_e);
// SparseConnection * con_corr_i = new SparseConnection(corr_e,neurons_i,w*strength,sparseness*plen*psize/ni,GLUT);
// set up Weight monitor
sprintf(strbuf, "%s/%s_e%.2et%.2f%s.%d.%c.ras", dir.c_str(), file_prefix, eta, tau_hom, label.c_str(), world.rank(), 'c');
SpikeMonitor * smon_c = new SpikeMonitor( corr_e, strbuf , psize );
}
if ( hsize*hlen ) {
corr_e2 = new CorrelatedPoissonGroup(extsize*hlen,stimfreq,extsize,10);
corr_e2->set_amplitude(amplitude_heb);
corr_e2->set_timescale(100e-3);
SparseConnection * con_corr_e2 = new SparseConnection(corr_e2,neurons_e,GLUT);
con_corr_e2->allocate_manually( hlen*extsize*hsize*sparseness*1.05 );
con_corr_e2->w->clear();
for ( int i = 0 ; i < hlen ; ++i )
con_corr_e2->connect_block_random(w,sparseness,(i)*extsize,(i+1)*extsize,offset+(i)*hsize+plen*psize,offset+(i+1)*hsize+plen*psize);
con_corr_e2->finalize();
corr_connections.push_back(con_corr_e2);
// SparseConnection * con_corr_i2 = new SparseConnection(corr_e2,neurons_i,w*strength,sparseness*hlen*hsize/ni,GLUT);
}
// disabling external random input
// con_exte->set_block(0,2500,psize,2*psize,0.0);
}
}
// preload patterns
if ( prefile != "" ) {
logger->msg("Preloading patterns ...",PROGRESS,true);
// con_ee->random_data(w_ee,w_ee);
con_ee->load_patterns(prefile, wmax, true, false);
// con_ee->sparse_set_data(0.9,0);
}
// adding patterns
if ( patfile != "" ) {
logger->msg("Preparing stimulus ...",PROGRESS,true);
sprintf(strbuf, "%s/%s_e%.2et%.2f%s.%d.stim", dir.c_str(), file_prefix, eta, tau_hom, label.c_str(), world.rank());
StimulusGroup * stimgroup = new StimulusGroup(ne,patfile,strbuf);
stimgroup->set_mean_on_period(onperiod);
stimgroup->set_mean_off_period(offperiod);
IdentityConnection * con_stim = new IdentityConnection(stimgroup,neurons_e,w_ext);
}
if (primetime>0) {
msg = "Priming ...";
logger->msg(msg,PROGRESS,true);
con_ee->stdp_active = false;
sys->run(primetime,true);
}
if ( corr || recall ) {
if ( psize*plen )
corr_e->set_target_amplitude(amplitude_fwd);
if ( hsize*hlen )
corr_e2->set_target_amplitude(amplitude_heb);
}
logger->msg("Simulating ...",PROGRESS,true);
if (eta > 0) {
con_ee->stdp_active = true;
}
if (!sys->run(simtime,true))
errcode = 1;
if (!fast) {
logger->msg("Saving neurons state ...",PROGRESS,true);
sprintf(strbuf, "%s/%s_e%.2et%.2f%s.%d.e.nstate", dir.c_str(), file_prefix, eta, tau_hom, label.c_str(), world.rank());
neurons_e->write_to_file(strbuf);
sprintf(strbuf, "%s/%s_e%.2et%.2f%s.%d.i.nstate", dir.c_str(), file_prefix, eta, tau_hom, label.c_str(), world.rank());
neurons_i->write_to_file(strbuf);
logger->msg("Saving weight matrix ...",PROGRESS,true);
sprintf(strbuf, "%s/%s_e%.2et%.2f%s.%d.wmat", dir.c_str(), file_prefix, eta, tau_hom, label.c_str(), world.rank());
con_ee->write_to_file(strbuf);
sprintf(strbuf, "%s/%s_e%.2et%.2f%s.%d.ei.wmat", dir.c_str(), file_prefix, eta, tau_hom, label.c_str(), world.rank());
con_ei->write_to_file(strbuf);
for ( int i = 0 ; i < corr_connections.size() ; ++i ) {
sprintf(strbuf, "%s/%s_e%.2et%.2f%s.%d.%d.wmat", dir.c_str(), file_prefix, eta, tau_hom, label.c_str(), i, world.rank());
corr_connections[i]->write_to_file(strbuf);
}
}
// save lifetime
sprintf(strbuf, "%s/%s_e%.2et%.2f%s.%d.lifetime", dir.c_str(), file_prefix, eta, tau_hom, label.c_str(), world.rank());
ofstream killfile;
killfile.open(strbuf);
killfile << sys->get_time()-primetime << endl;
killfile.close();
logger->msg("Freeing ...",PROGRESS,true);
delete sys;
if (errcode) {
env.abort(errcode);
}
return errcode;
}
int main(int ac, char* av[])
{
string dir = "/lcncluster/zenke/reset/";
string file_prefix = "rc";
string infilename = "";
char strbuf [255];
string msg;
bool save = false;
bool chain = false;
bool prime = false;
bool consolidation = true;
bool isp_active = true;
bool inh_input = false;
bool noisy_initial_weights = false;
bool consolidate_initial_weights = false;
NeuronID stimsize = 1000;
NeuronID size = 4000;
NeuronID seed = 1;
double alpha = 3;
double kappa = 3;
double tauf = 200e-3;
double ujump = 0.2;
double taud = 200e-3;
double eta = 1e-3;
double beta = 5.0e-2;
double delta = 2.0e-2;
double weight_a = 0.1;
double weight_c = 0.5;
double adapt = 0.0;
double strong_weights = 0.0; // determines the fraction of weights initialized at weight_c -- FIXME not implemented
double pot_strength = 0.1;
double ontime = 1.0;
double offtime = 5.0;
double scale = 35;
double wmax = 5.0;
double wmin = 0.0;
double wmaxi = 5.0;
double bgrate = 10.0;
int preferred = -1;
string stimfile = ""; // stimulus patterns file
string prefile = ""; // preload patters file
string recfile = ""; // file with receptive fields
string monfile = ""; // patternsto monitor file
AurynWeight wee = 0.1;
AurynWeight wei = 0.2;
AurynWeight wie = 0.2;
AurynWeight wii = 0.2;
AurynWeight chi = 1.0;
AurynWeight xi = 0.5;
double sparseness = 0.1;
double sparseness_ext = 0.05;
double wext = 0.2;
double simtime = 3600.;
int errcode = 0;
try {
po::options_description desc("Allowed options");
desc.add_options()
("help", "produce help message")
("load", po::value<string>(), "input weight matrix")
("prefix", po::value<string>(), "set file prefix")
("save", "save network state at end of sim")
("chain", "chain mode for pattern loader")
("prime", "prime network with a burn-in phase")
("noconsolidation", "switches off consolidation")
("noisp", "switches off isp")
("noisyweights", "switches noisy initial weights on")
("consolidateweights", "initialize weights as consolidated")
("inhinput", "switches external input to inh on")
("alpha", po::value<double>(), "exc input rate")
("taud", po::value<double>(), "time constant of synaptic depression")
("tauf", po::value<double>(), "time constant of synaptic facilitation")
("ujump", po::value<double>(), "u jump STP constant")
("chi", po::value<double>(), "chi factor - pattern preload strength")
("xi", po::value<double>(), "xi factor - stimulation strength")
("wext", po::value<double>(), "recurrent weight (ext)")
("wee", po::value<double>(), "recurrent weight (wee)")
("wei", po::value<double>(), "recurrent weight (wei)")
("wii", po::value<double>(), "recurrent weight (wii)")
("wie", po::value<double>(), "recurrent weight (wie)")
("extsparse", po::value<double>(), "external sparseness")
("intsparse", po::value<double>(), "external sparseness")
("simtime", po::value<double>(), "simulation time")
("ontime", po::value<double>(), "simulation time")
("offtime", po::value<double>(), "simulation time")
("dir", po::value<string>(), "output dir")
("eta", po::value<double>(), "the learning rate")
("beta", po::value<double>(), "decay parameter")
("potstrength", po::value<double>(), "potential strength parameter")
("delta", po::value<double>(), "growth parameter")
("weight_a", po::value<double>(), "weight_a")
("weight_c", po::value<double>(), "weight_c")
("strongw", po::value<double>(), "fraction of initially strong weights")
("size", po::value<int>(), "simulation size")
("seed", po::value<int>(), "random seed ")
("stimfile", po::value<string>(), "stimulus file")
("prefile", po::value<string>(), "preload file")
("recfile", po::value<string>(), "receptive field file")
("scale", po::value<double>(), "stimulus strength")
("adapt", po::value<double>(), "adaptation jump size for long time constant")
("bgrate", po::value<double>(), "background rate of input")
("preferred", po::value<int>(), "num of preferred stim")
("monfile", po::value<string>(), "monitor file")
;
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("load")) {
infilename = vm["load"].as<string>();
}
if (vm.count("prefix")) {
cout << "simulation prefix "
<< vm["prefix"].as<string>() << ".\n";
file_prefix = vm["prefix"].as<string>();
}
if (vm.count("save")) {
save = true;
}
if (vm.count("chain")) {
chain = true;
}
if (vm.count("prime")) {
prime = true;
}
if (vm.count("noconsolidation")) {
consolidation = false;
}
if (vm.count("noisp")) {
isp_active = false;
}
if (vm.count("noisyweights")) {
noisy_initial_weights = true;
}
if (vm.count("consolidateweights")) {
consolidate_initial_weights = true;
}
if (vm.count("inhinput")) {
inh_input = true;
}
if (vm.count("alpha")) {
alpha = vm["alpha"].as<double>();
}
if (vm.count("taud")) {
taud = vm["taud"].as<double>();
}
if (vm.count("tauf")) {
tauf = vm["tauf"].as<double>();
}
if (vm.count("ujump")) {
ujump = vm["ujump"].as<double>();
}
if (vm.count("simtime")) {
simtime = vm["simtime"].as<double>();
}
if (vm.count("ontime")) {
ontime = vm["ontime"].as<double>();
}
if (vm.count("offtime")) {
offtime = vm["offtime"].as<double>();
}
if (vm.count("dir")) {
dir = vm["dir"].as<string>();
}
if (vm.count("chi")) {
chi = vm["chi"].as<double>();
}
if (vm.count("xi")) {
xi = vm["xi"].as<double>();
}
if (vm.count("wext")) {
wext = vm["wext"].as<double>();
}
if (vm.count("wee")) {
wee = vm["wee"].as<double>();
}
if (vm.count("wei")) {
wei = vm["wei"].as<double>();
}
if (vm.count("wii")) {
wii = vm["wii"].as<double>();
}
if (vm.count("wie")) {
wie = vm["wie"].as<double>();
}
if (vm.count("extsparse")) {
sparseness_ext = vm["extsparse"].as<double>();
}
if (vm.count("intsparse")) {
sparseness = vm["intsparse"].as<double>();
}
if (vm.count("eta")) {
eta = vm["eta"].as<double>();
}
if (vm.count("beta")) {
beta = vm["beta"].as<double>();
}
if (vm.count("potstrength")) {
pot_strength = vm["potstrength"].as<double>();
}
if (vm.count("delta")) {
delta = vm["delta"].as<double>();
}
if (vm.count("weight_a")) {
weight_a = vm["weight_a"].as<double>();
}
if (vm.count("weight_c")) {
weight_c = vm["weight_c"].as<double>();
}
if (vm.count("strongw")) {
strong_weights = vm["strongw"].as<double>();
}
if (vm.count("size")) {
size = vm["size"].as<int>();
}
if (vm.count("stimfile")) {
stimfile = vm["stimfile"].as<string>();
monfile = stimfile;
}
if (vm.count("prefile")) {
prefile = vm["prefile"].as<string>();
}
if (vm.count("recfile")) {
recfile = vm["recfile"].as<string>();
}
if (vm.count("scale")) {
scale = vm["scale"].as<double>();
}
if (vm.count("adapt")) {
adapt = vm["adapt"].as<double>();
}
if (vm.count("bgrate")) {
bgrate = vm["bgrate"].as<double>();
}
if (vm.count("preferred")) {
preferred = vm["preferred"].as<int>();
}
if (vm.count("monfile")) {
monfile = vm["monfile"].as<string>();
}
if (vm.count("seed")) {
seed = vm["seed"].as<int>();
}
}
catch(exception& e) {
cerr << "error: " << e.what() << "\n";
return 1;
}
catch(...) {
cerr << "Exception of unknown type!\n";
}
// BEGIN Global stuff
mpi::environment env(ac, av);
mpi::communicator world;
communicator = &world;
sprintf(strbuf, "%s/%s.%d.log", dir.c_str(), file_prefix.c_str(), world.rank());
string logfile = strbuf;
logger = new Logger(logfile,world.rank());
sys = new System(&world);
// END Global stuff
//log params
logger->parameter("alpha",alpha);
logger->parameter("beta",beta);
logger->parameter("delta",delta);
logger->parameter("eta",eta);
logger->parameter("wee",wee);
logger->parameter("wext",wext);
logger->parameter("chi",chi);
logger->parameter("xi",xi);
logger->parameter("stimfile",stimfile);
logger->parameter("monfile",monfile);
logger->parameter("offtime",offtime);
logger->parameter("ontime",ontime);
logger->parameter("taud",taud);
logger->parameter("tauf",tauf);
logger->parameter("ujump",ujump);
AIF2Group * neurons_e = new AIF2Group(size);
// AIFSclGroup * neurons_e = new AIFSclGroup(size);
// neurons_e->randomize_state_vector_gauss("scaling_weight",1.0,0.2,2351);
// neurons_e->set_eta(eta); // exc scaling
// neurons_e->set_kappa(alpha); // exc scaling
neurons_e->dg_adapt1 = 0.1;
neurons_e->dg_adapt2 = adapt;
// AIFGroup * neurons_e = new AIFGroup(size);
// neurons_e->dg_adapt1 = 0.1;
// neurons_e->random_adapt(1,1);
// IFGroup * neurons_e = new IFGroup(size);
// IF2Group * neurons_e = new IF2Group(size);
neurons_e->set_tau_ampa(5e-3);
neurons_e->set_tau_gaba(10e-3);
neurons_e->set_tau_nmda(100e-3);
neurons_e->set_ampa_nmda_ratio(0.2);
IFGroup * neurons_i2 = new IFGroup(size/4);
neurons_i2->set_tau_ampa(5e-3);
neurons_i2->set_tau_gaba(10e-3);
neurons_i2->set_tau_nmda(100e-3);
neurons_i2->set_ampa_nmda_ratio(0.3);
StimulusGroup * stimgroup;
sprintf(strbuf, "%s/%s.%d.stimtimes", dir.c_str(), file_prefix.c_str(), world.rank() );
string stimtimefile = strbuf;
// stimgroup is initialized here. If stimfile is empty no patterns are loaded
// and it acts simply as PoissonGroup
stimgroup = new StimulusGroup(size,stimtimefile);
stimgroup->set_mean_on_period(ontime);
stimgroup->set_mean_off_period(offtime);
stimgroup->binary_patterns = true;
stimgroup->scale = scale;
stimgroup->background_rate = bgrate;
stimgroup->background_during_stimulus = true;
// stimgroup->randomintensities = true;
if (seed!=1) stimgroup->seed(seed);
double raw_delta = delta*eta/1e-3;
P10Connection * con_ee;
con_ee = new P10Connection(neurons_e,neurons_e,
wee,sparseness,
eta,
kappa,
wmax
);
con_ee->set_transmitter(AMPA);
con_ee->set_name("EE");
con_ee->set_weight_a(weight_a);
con_ee->set_weight_c(weight_c);
con_ee->consolidation_active = consolidation;
double wtmax = 1.0/4*(weight_c-weight_a);
double normalization_factor = (wtmax-weight_a)*(wtmax-(weight_a+weight_c)/2)*(wtmax-weight_c);
con_ee->pot_strength = pot_strength/normalization_factor;
logger->parameter("normalized pot_strength",con_ee->pot_strength);
if ( noisy_initial_weights )
con_ee->random_data(wee,wee);
if ( consolidate_initial_weights )
con_ee->consolidate();
// STP parameters
con_ee->set_tau_d(taud);
con_ee->set_tau_f(tauf);
con_ee->set_ujump(ujump);
con_ee->set_urest(ujump);
con_ee->set_beta(beta);
con_ee->delta = raw_delta*eta;
con_ee->set_min_weight(wmin);
// con_ee->w_min = 0.01;
// SparseConnection * con_ei2 = new SparseConnection(neurons_e,neurons_i2,wei,sparseness,GLUT);
// SparseConnection * con_ei2 = new SparseConnection(neurons_e,neurons_i2,wei,sparseness,GLUT);
STPConnection * con_ei2 = new STPConnection(neurons_e,neurons_i2,3*wei,sparseness,GLUT);
con_ei2->set_tau_d(taud);
con_ei2->set_tau_f(0.6);
con_ei2->set_ujump(0.2);
con_ei2->set_urest(0.2);
// P10Connection * con_ei2 = NULL;
// con_ei2 = new P10Connection( neurons_e, neurons_i2,
// 3*wei,sparseness,
// eta,
// kappa, // supposedly deprecated
// wmax,
// GLUT
// );
// con_ei2->set_weight_a(weight_a);
// con_ei2->set_weight_c(weight_c);
// con_ei2->set_tau_d(taud);
// con_ei2->set_tau_f(tauf);
// con_ei2->set_ujump(ujump);
// con_ei2->set_urest(ujump);
// con_ei2->set_beta(beta);
// con_ei2->A2_minus = eta;
// con_ei2->delta = raw_delta*eta;
// con_ei2->set_min_weight(wmin);
// con_ei2->set_name("E->I");
// P10Connection * con_ei2 = new P10Connection(neurons_e,neurons_i2,
// wei,sparseness,
// eta,
// alpha,
// wmax
// );
// con_ei2->set_name("EI2");
// con_ei2->set_tau_d(taud);
// con_ei2->set_tau_f(tauf);
// con_ei2->set_ujump(0.2);
// con_ei2->set_urest(0.2);
// con_ei2->set_beta(beta);
// con_ei2->weight_a = weight_a;
double geta = -eta*1e-4;
SparseConnection * con_i2i2 = new SparseConnection(neurons_i2,neurons_i2,wii,sparseness,GABA);
// RateModulatedConnection * con_i2i2;
// con_i2i2 = new RateModulatedConnection(neurons_i2,neurons_i2,
// wii,sparseness,
// GABA
// );
// con_i2i2->eta = geta;
// con_i2i2->rate_target = 3*alpha;
// con_i2i2->rate_estimate = 3*alpha;
// con_i2i2->random_data(wii,wii);
con_i2i2->set_name("I2->I2");
GlobalPFConnection * con_i2e;
con_i2e = new GlobalPFConnection(neurons_i2,neurons_e,
wie,sparseness,
10.0,
eta/50,
alpha,
wmaxi,
GABA
);
con_i2e->set_name("I2E");
// con_i2e->set_eta(geta);
// con_i2e->rate_target = alpha;
// con_i2e->rate_estimate = alpha;
// con_i2e->random_data(wie,wie);
if ( inh_input ) {
STPConnection * con_stim_i
= new STPConnection( stimgroup,
neurons_i2,
wext,
sparseness_ext,
GLUT);
con_stim_i->set_tau_d(taud);
con_stim_i->set_tau_f(0.6);
con_stim_i->set_ujump(0.2);
con_stim_i->set_urest(0.2);
}
// External input
P10Connection * con_stim_e = NULL;
con_stim_e = new P10Connection( stimgroup, neurons_e,
wext,sparseness_ext,
eta,
kappa, // supposedly deprecated
wmax,
GLUT
);
con_stim_e->set_weight_a(weight_a);
con_stim_e->set_weight_c(weight_c);
con_stim_e->set_tau_d(taud);
con_stim_e->set_tau_f(tauf);
con_stim_e->set_ujump(ujump);
con_stim_e->set_urest(ujump);
con_stim_e->set_beta(beta);
con_stim_e->delta = raw_delta*eta;
con_stim_e->set_min_weight(wmin);
// con_stim_e->random_data(wext,wext);
if ( noisy_initial_weights )
con_stim_e->random_data(wext,wext);
con_stim_e->set_name("Stim->E");
con_stim_e->consolidation_active = consolidation;
con_stim_e->pot_strength = pot_strength/normalization_factor;
if ( consolidate_initial_weights )
con_stim_e->consolidate();
// con_stim_e->sparse_set_data(strong_weights,weight_c);
// External input
// P10Connection * con_stim_i = NULL;
// con_stim_i = new P10Connection( stimgroup, neurons_i2,
// wext,sparseness_ext,
// eta,
// kappa, // supposedly deprecated
// wmax,
// GLUT
// );
// con_stim_i->set_weight_a(weight_a);
// con_stim_i->set_weight_c(weight_c);
// con_stim_i->random_data(wext,wext);
// // con_stim_i->sparse_set_data(0.1,1.0);
// con_stim_i->set_tau_d(taud);
// con_stim_i->set_tau_f(tauf);
// con_stim_i->set_ujump(ujump);
// con_stim_i->set_urest(ujump);
// con_stim_i->set_beta(beta);
// con_stim_i->delta = raw_delta*eta;
// con_stim_i->set_min_weight(wmin);
// // con_stim_e->random_data(wext,wext);
// con_stim_i->set_name("Stim->E");
// // con_stim_e->random_data(wext,wext);
if (!stimfile.empty()) {
logger->msg("Setting up stimulus ...",PROGRESS,true);
stimgroup->load_patterns(stimfile.c_str());
stimgroup->set_next_action_time(50); // let network settle for some time
sprintf(strbuf, "%s/%s.%d.s.ras", dir.c_str(), file_prefix.c_str(), world.rank() );
SpikeMonitor * smon_s = new SpikeMonitor( stimgroup, string(strbuf), size );
// gives the first 3 patterns half of the probability
if ( preferred > 0 ) {
vector<double> dist = stimgroup->get_distribution();
int r = preferred;
double prob = 0.8;
for ( int i = 0 ; i < dist.size() ; ++i ) {
if ( i == r )
dist[i] = 1.0;
else
dist[i] = 1.0/dist.size();
}
stimgroup->set_distribution(dist);
}
sprintf(strbuf, "%s/%s.%d.wse", dir.c_str(), file_prefix.c_str(), world.rank() );
new WeightStatsMonitor( con_stim_e, string(strbuf) );
}
// load if necessary
if (!infilename.empty()) {
logger->msg("Loading from file ...",PROGRESS,true);
sys->load_network_state(infilename.c_str());
// auryn_vector_float * foo = neurons_i2->get_state_vector("g_nmda");
// auryn_vector_float_set_all( foo, 5.0 );
}
if ( !prefile.empty() && chi > 0.0 ) {
con_ee->patterns_ignore_gamma = true;
con_ee->load_patterns(prefile,chi,false);
con_ee->consolidate();
}
if ( !prefile.empty() && xi > 0.0 ) {
con_stim_e->patterns_ignore_gamma = true;
con_stim_e->load_patterns(prefile,xi,false);
if ( consolidate_initial_weights )
con_stim_e->consolidate();
}
if ( !recfile.empty() && xi > 0.0 ) {
con_stim_e->load_fragile_matrix(recfile); // TODO
con_stim_e->scale_all(xi);
if ( consolidate_initial_weights )
con_stim_e->consolidate();
}
sprintf(strbuf, "%s/%s.%d.sse", dir.c_str(), file_prefix.c_str(), world.rank() );
WeightMonitor * wmon_s = new WeightMonitor( con_stim_e, string(strbuf), 1.0 );
wmon_s->add_equally_spaced(50);
if ( !monfile.empty() ) {
sprintf(strbuf, "%s/%s.%d.pact", dir.c_str(), file_prefix.c_str(), world.rank() );
PatternMonitor * patmon = new PatternMonitor( neurons_e, string(strbuf) , monfile.c_str(), 100);
if ( !stimfile.empty() ) //
wmon_s->load_pattern_connections(stimfile,monfile,20,20,ASSEMBLIES_ONLY); // true for assemblies only
else
wmon_s->load_pattern_connections(monfile,20,20,ASSEMBLIES_ONLY); // true for assemblies only
}
// sprintf(strbuf, "%s/%s.%d.sei", dir.c_str(), file_prefix.c_str(), world.rank() );
// WeightMonitor * wmon_ei = new WeightMonitor( con_ei2, 0, 100, strbuf, 1.0, DATARANGE);
sprintf(strbuf, "%s/%s.%d.see", dir.c_str(), file_prefix.c_str(), world.rank() );
WeightMonitor * wmon = new WeightMonitor( con_ee, string(strbuf), 1.0);
wmon->add_equally_spaced(50);
if ( !monfile.empty() )
wmon->load_pattern_connections(monfile,10,10,ASSEMBLIES_ONLY); // true for assemblies only
// sprintf(strbuf, "%s/%s.%d.scl", dir.c_str(), file_prefix.c_str(), world.rank() );
// StateMonitor * stmon_scl = new StateMonitor( neurons_e, 5, "scaling_weight", string(strbuf), 1 );
// sprintf(strbuf, "%s/%s.%d.scl3k", dir.c_str(), file_prefix.c_str(), world.rank() );
// StateMonitor * stmon_scl2 = new StateMonitor( neurons_e, 3000, "scaling_weight", string(strbuf), 1 );
sprintf(strbuf, "%s/%s.%d.mem", dir.c_str(), file_prefix.c_str(), world.rank() );
VoltageMonitor * stmon_mem = new VoltageMonitor( neurons_e, 3, string(strbuf) );
stmon_mem->tStop = 10/dt; // stops recording after 10s
sprintf(strbuf, "%s/%s.%d.imem", dir.c_str(), file_prefix.c_str(), world.rank() );
VoltageMonitor * stmon_imem = new VoltageMonitor( neurons_i2, 3, string(strbuf) );
stmon_imem->tStop = 10/dt; // stops recording after 10s
// sprintf(strbuf, "%s/%s.%d.si1e", dir.c_str(), file_prefix.c_str(), world.rank() );
// WeightMonitor * wmon_i1e = new WeightMonitor( con_i1e, string(strbuf) );
// wmon_i1e->add_equally_spaced(50);
sprintf(strbuf, "%s/%s.%d.si2e", dir.c_str(), file_prefix.c_str(), world.rank() );
WeightMonitor * wmon_i2e = new WeightMonitor( con_i2e, string(strbuf) );
wmon_i2e->add_equally_spaced(50);
// sprintf(strbuf, "%s/%s.%d.ipe", dir.c_str(), file_prefix.c_str(), world.rank() );
// WeightMonitor * wmon_ise = new WeightMonitor( con_se_inh, 0, 100, strbuf, 1.0, DATARANGE);
// sprintf(strbuf, "%s/%s.%d.sii", dir.c_str(), file_prefix.c_str(), world.rank() );
// WeightMonitor * wmon_ii = new WeightMonitor( con_ii, 0, 100, strbuf, 1.0, DATARANGE);
sprintf(strbuf, "%s/%s.%d.wee", dir.c_str(), file_prefix.c_str(), world.rank() );
new WeightStatsMonitor( con_ee, string(strbuf) );
sprintf(strbuf, "%s/%s.%d.wi2e", dir.c_str(), file_prefix.c_str(), world.rank() );
new WeightStatsMonitor( con_i2e, string(strbuf) );
if ( !monfile.empty() ) {
sprintf(strbuf, "%s/%s.%d.wprec", dir.c_str(), file_prefix.c_str(), world.rank() );
WeightPatternMonitor * wpmon = new WeightPatternMonitor( con_ee, string(strbuf), 60 );
wpmon->load_patterns(monfile);
}
if ( !stimfile.empty() && !monfile.empty() ) {
sprintf(strbuf, "%s/%s.%d.wpin", dir.c_str(), file_prefix.c_str(), world.rank() );
WeightPatternMonitor * wpmon = new WeightPatternMonitor( con_stim_e, string(strbuf), 60 );
wpmon->load_pre_patterns(stimfile);
wpmon->load_post_patterns(monfile);
}
// sprintf(strbuf, "%s/%s.%d.wi1e", dir.c_str(), file_prefix.c_str(), world.rank() );
// new WeightStatsMonitor( con_i1e, string(strbuf) );
// sprintf(strbuf, "%s/%s.%d.wpe", dir.c_str(), file_prefix.c_str(), world.rank() );
// new WeightStatsMonitor( con_se, string(strbuf) );
sprintf(strbuf, "%s/%s.%d.e.ras", dir.c_str(), file_prefix.c_str(), world.rank() );
SpikeMonitor * smon_e = new SpikeMonitor( neurons_e, string(strbuf), size );
sprintf(strbuf, "%s/%s.%d.i2.ras", dir.c_str(), file_prefix.c_str(), world.rank() );
SpikeMonitor * smon_i2 = new SpikeMonitor( neurons_i2, string(strbuf), size );
sprintf(strbuf, "%s/%s.%d.e.prate", dir.c_str(), file_prefix.c_str(), world.rank() );
PopulationRateMonitor * pmon_e = new PopulationRateMonitor( neurons_e, string(strbuf), 0.1 );
sprintf(strbuf, "%s/%s.%d.i2.prate", dir.c_str(), file_prefix.c_str(), world.rank() );
PopulationRateMonitor * pmon_i2 = new PopulationRateMonitor( neurons_i2, string(strbuf), 0.1 );
RateChecker * chk = new RateChecker( neurons_e , -1 , 20. , 0.1);
// prime
if ( prime ) {
// neurons_e->set_eta(1e-2); // exc scaling
con_ee->stdp_active = false;
con_stim_e->stdp_active = false;
if (!sys->run(100.0,false))
errcode = 1;
// con_stim_e->stdp_active = true;
// con_ee->stdp_active = true;
// neurons_e->set_eta(eta); // exc scaling
}
// neurons_e->set_eta(0);
if ( eta > 0 ) {
con_ee->stdp_active = true;
con_stim_e->stdp_active = true;
con_i2e->stdp_active = true;
} else {
con_ee->stdp_active = false;
con_stim_e->stdp_active = false;
con_i2e->stdp_active = false;
}
con_i2e->stdp_active = isp_active;
if (!sys->run(simtime,false))
errcode = 1;
if ( save ) {
sprintf(strbuf, "%s/%s", dir.c_str(), file_prefix.c_str() );
sys->save_network_state(string(strbuf));
}
logger->msg("Freeing ...",PROGRESS,true);
delete sys;
if (errcode)
env.abort(errcode);
return errcode;
}
