bool SpikingGroup::write_to_file(const char * filename)
{
if ( !evolve_locally() ) return true;
ofstream outfile;
outfile.open(filename,ios::out);
if (!outfile) {
cerr << "Can't open output file " << filename << endl;
throw AurynOpenFileException();
}
outfile << "# Auryn SpikingGroup state file for n="<< get_rank_size() <<" neurons (ver. " << VERSION << ")" << endl;
outfile << "# Field order: ";
for ( map<string,gsl_vector_float *>::const_iterator iter = state_vector.begin() ;
iter != state_vector.end() ;
++iter ) {
outfile << scientific << iter->first << " ";
}
outfile << "(plus traces)";
outfile << endl;
boost::archive::text_oarchive oa(outfile);
oa << *(delay);
outfile << endl;
for ( NeuronID i = 0 ; i < get_rank_size() ; ++i )
{
outfile << get_output_line(i);
}
outfile.close();
return true;
}
bool SpikingGroup::localrank(NeuronID i) {
bool t = ( (i+locked_rank)%locked_range==communicator->rank() )
&& (int) communicator->rank() >= locked_rank
&& (int) communicator->rank() < (locked_rank+locked_range)
&& i/locked_range < get_rank_size();
return t;
}
void TIFGroup::evolve()
{
for (NeuronID i = 0 ; i < get_rank_size() ; ++i ) {
if (t_ref[i]==0) {
const AurynFloat dg_mem = ( (e_rest-t_mem[i])
- t_g_ampa[i] * t_mem[i]
- t_g_gaba[i] * (t_mem[i]-e_rev)
+ t_bg_cur[i] );
t_mem[i] += dg_mem*scale_mem;
if (t_mem[i]>thr) {
push_spike(i);
t_mem[i] = e_rest ;
t_ref[i] += refractory_time ;
}
} else {
t_ref[i]-- ;
t_mem[i] = e_rest ;
}
}
auryn_vector_float_scale(scale_ampa,g_ampa);
auryn_vector_float_scale(scale_gaba,g_gaba);
}
bool SpikingGroup::load_from_file(const char * filename)
{
if ( !evolve_locally() ) return true;
stringstream oss;
oss << "Loading SpikingGroup from " << filename;
logger->msg(oss.str(),NOTIFICATION);
ifstream infile (filename);
if (!infile) {
stringstream oes;
oes << "Can't open input file " << filename;
logger->msg(oes.str(),ERROR);
throw AurynOpenFileException();
}
NeuronID count = 0;
char buffer[1024];
infile.getline (buffer,1024); // skipping header TODO once could make this logic a bit smarter
infile.getline (buffer,1024); // skipping header
boost::archive::text_iarchive ia(infile);
ia >> *delay;
infile.getline (buffer,1024); // jumpting to next line
while ( infile.getline (buffer,1024) )
{
load_input_line(count,buffer);
count++;
}
if ( get_rank_size() != count ) {
// issue warning
stringstream oes;
oes << "SpikingGroup:: NeuronState file corrupted. Read "
<< count << " entries, but "
<< get_rank_size() << " expected in " << filename;
logger->msg(oes.str(),WARNING);
}
infile.close();
return true;
}
void StimulusGroup::init(string filename, StimulusGroupModeType stimulusmode, string outputfile, AurynFloat baserate)
{
sys->register_spiking_group(this);
ttl = new AurynTime [get_rank_size()];
activity = new AurynFloat [get_rank_size()];
set_baserate(baserate);
poisson_gen.seed(162346*communicator->rank());
mean_off_period = 1.0 ;
mean_on_period = 0.2 ;
stimulus_order = stimulusmode ;
stimulus_active = false ;
set_all( 0.0 );
scale = 2.0;
randomintervals = true;
binary_patterns = false;
if ( !outputfile.empty() )
{
tiserfile.open(outputfile.c_str(),ios::out);
if (!tiserfile) {
stringstream oss;
oss << "StimulusGroup:: Can't open output file " << filename;
logger->msg(oss.str(),ERROR);
exit(1);
}
tiserfile.setf(ios::fixed);
// tiserfile.precision(5);
}
stringstream oss;
oss << "StimulusGroup:: In mode " << stimulus_order;
logger->msg(oss.str(),NOTIFICATION);
cur_stim_index = 0;
next_action_time = 0;
active = true;
off_pattern = -1;
load_patterns(filename);
}
void SpikingGroup::init(NeuronID n, double loadmultiplier, NeuronID total )
{
group_name = "SpikingGroup";
unique_id = unique_id_count++;
size = n;
effective_load_multiplier = loadmultiplier;
if ( total > 0 ) {
anticipated_total = total;
stringstream oss;
oss << get_name() << ":: Anticipating " << anticipated_total << " units in total." ;
logger->msg(oss.str(),NOTIFICATION);
}
// setting up default values
evolve_locally_bool = true;
locked_rank = 0;
locked_range = communicator->size();
rank_size = calculate_rank_size(); // set the rank size
double fraction = (double)calculate_rank_size(0)*effective_load_multiplier/DEFAULT_MINDISTRIBUTEDSIZE;
if ( anticipated_total > 0 )
fraction = (1.*size*effective_load_multiplier)/anticipated_total;
if ( fraction >= 0 && fraction < 1. ) {
lock_range( fraction );
} else { // ROUNDROBIN which is default
locked_rank = 0;
locked_range = communicator->size();
stringstream oss;
oss << get_name() << ":: Size " << get_rank_size() << " (ROUNDROBIN)";
logger->msg(oss.str(),NOTIFICATION);
}
stringstream oss;
oss << get_name() << ":: Registering delay (MINDELAY=" << MINDELAY << ")";
logger->msg(oss.str(),DEBUG);
delay = new SpikeDelay( );
set_delay(MINDELAY+1);
evolve_locally_bool = evolve_locally_bool && ( get_rank_size() > 0 );
}
void StimulusGroup::redraw()
{
boost::exponential_distribution<> dist(BASERATE);
boost::variate_generator<boost::mt19937&, boost::exponential_distribution<> > die(poisson_gen, dist);
for ( NeuronID i = 0 ; i < get_rank_size() ; ++i )
{
ttl[i] = sys->get_clock() + (AurynTime)((AurynFloat)die()/((activity[i]+1e-9)*dt));
}
}
void TIFGroup::clear()
{
clear_spikes();
for (NeuronID i = 0; i < get_rank_size(); i++) {
gsl_vector_float_set (mem, i, e_rest);
gsl_vector_ushort_set (ref, i, 0);
gsl_vector_float_set (g_ampa, i, 0.);
gsl_vector_float_set (g_gaba, i, 0.);
gsl_vector_float_set (bg_current, i, 0.);
}
}
void IF2Group::clear()
{
clear_spikes();
for (NeuronID i = 0; i < get_rank_size(); i++) {
auryn_vector_float_set (mem, i, e_rest);
auryn_vector_float_set (thr, i, 0.);
auryn_vector_float_set (g_ampa, i, 0.);
auryn_vector_float_set (g_gaba, i, 0.);
auryn_vector_float_set (g_nmda, i, 0.);
}
}
void StimulusGroup::redraw_softstart()
{
boost::exponential_distribution<> dist(BASERATE);
boost::variate_generator<boost::mt19937&, boost::exponential_distribution<> > die(poisson_gen, dist);
boost::uniform_real<> uniformdist(0, SOFTSTARTTIME );
boost::variate_generator<boost::mt19937&, boost::uniform_real<> > random(poisson_gen, uniformdist);
for ( NeuronID i = 0 ; i < get_rank_size() ; ++i )
{
ttl[i] = sys->get_clock() + (AurynTime)((AurynFloat)die()/((activity[i]+base_rate)*dt)+random()/dt);
}
}
void SpikingGroup::lock_range( double rank_fraction )
{
locked_rank = last_locked_rank%communicator->size(); // TODO might cause a bug with the block lock stuff
// TODO get the loads for the different ranks and try to minimize this
if ( rank_fraction == 0 ) { // this is the classical rank lock to one single rank
stringstream oss;
oss << get_name() << ":: Groups demands to run on single rank only (RANKLOCK).";
logger->msg(oss.str(),NOTIFICATION);
locked_range = 1;
} else { // this is for multiple rank ranges
unsigned int free_ranks = communicator->size()-last_locked_rank;
locked_range = rank_fraction*communicator->size()+0.5;
if ( locked_range == 0 ) { // needs at least one rank
locked_range = 1;
}
if ( locked_range > free_ranks ) {
stringstream oss;
// oss << "SpikingGroup:: Not enough free ranks to put SpikingGroup defaulting to ROUNDROBIN distribution.";
oss << get_name() << ":: Not enough free ranks for RANGELOCK. Starting to fill at zero again.";
logger->msg(oss.str(),NOTIFICATION);
locked_rank = 0;
free_ranks = communicator->size();
// return;
}
}
unsigned int rank = (unsigned int) communicator->rank();
evolve_locally_bool = ( rank >= locked_rank && rank < (locked_rank+locked_range) );
last_locked_rank = (locked_rank+locked_range)%communicator->size();
rank_size = calculate_rank_size(); // recalculate the rank size
// logging
if ( evolve_locally_bool ) {
stringstream oss;
oss << get_name() << ":: Size "<< get_rank_size() <<" (BLOCKLOCK: ["<< locked_rank
<< ":" << locked_rank+locked_range-1 << "] )";
logger->msg(oss.str(),NOTIFICATION);
} else {
stringstream oss;
oss << get_name() << ":: Passive on this rank (BLOCKLOCK: ["<< locked_rank
<< ":" << locked_rank+locked_range-1 << "] )";
logger->msg(oss.str(),DEBUG);
}
}
void AIFGroup::random_adapt(AurynState mean, AurynState sigma)
{
boost::mt19937 ng_gen(42); // produces same series every time
boost::normal_distribution<> dist((double)mean, (double)sigma);
boost::variate_generator<boost::mt19937&, boost::normal_distribution<> > die(ng_gen, dist);
AurynState rv;
for ( AurynLong i = 0 ; i<get_rank_size() ; ++i ) {
rv = die();
if ( rv>0 )
set_val (g_adapt1, i, rv );
}
init_state();
}
void IF2Group::check_thresholds()
{
auryn_vector_float_clip( mem, e_rev );
AurynState * thr_ptr = thr->data;
for ( AurynState * i = mem->data ; i != mem->data+get_rank_size() ; ++i ) { // it's important to use rank_size here otherwise there might be spikes from units that do not exist
if ( *i > ( thr_rest + *thr_ptr ) ) {
NeuronID unit = i-mem->data;
push_spike(unit);
set_val (mem, unit, e_rest); // reset
set_val (thr, unit, dthr); //refractory
}
thr_ptr++;
}
}
void SpikingGroup::randomize_state_vector_gauss(string state_vector_name, AurynState mean, AurynState sigma, int seed)
{
boost::mt19937 ng_gen(seed+communicator->rank()); // produces same series every time
boost::normal_distribution<> dist((double)mean, (double)sigma);
boost::variate_generator<boost::mt19937&, boost::normal_distribution<> > die(ng_gen, dist);
AurynState rv;
auryn_vector_float * vec = get_state_vector(state_vector_name);
for ( AurynLong i = 0 ; i<get_rank_size() ; ++i ) {
rv = die();
auryn_vector_float_set( vec, i, rv );
}
}
NeuronID SpikingGroup::get_vector_size()
{
return calculate_vector_size(get_rank_size());
}
void StimulusGroup::evolve()
{
if ( !active ) return;
// detect and push spikes
boost::exponential_distribution<> dist(BASERATE);
boost::variate_generator<boost::mt19937&, boost::exponential_distribution<> > die(poisson_gen, dist);
for ( NeuronID i = 0 ; i < get_rank_size() ; ++i )
{
if ( ttl[i] < sys->get_clock() && activity[i]>0.0 )
{
push_spike ( i );
ttl[i] = sys->get_clock() + (AurynTime)((AurynFloat)die()/((activity[i]+base_rate)*dt));
}
}
// update stimulus properties
if ( sys->get_clock() >= next_action_time ) {
write_sequence_file(dt*(sys->get_clock()));
if ( stimulus_active ) {
if ( off_pattern >= 0 ) {
set_active_pattern( off_pattern ); // turn on "off-stimulus"
cur_stim_index = off_pattern;
} else
set_all( 0.0 ); // turn off currently active stimulus
stimulus_active = false ;
if ( randomintervals ) {
boost::exponential_distribution<> dist(1./mean_off_period);
boost::variate_generator<boost::mt19937&, boost::exponential_distribution<> > die(order_gen, dist);
next_action_time = sys->get_clock() + (AurynTime)(max(0.0,die())/dt);
} else {
next_action_time = sys->get_clock() + (AurynTime)(mean_off_period/dt);
}
} else {
if ( active ) {
// choose stimulus
switch ( stimulus_order ) {
case MANUAL:
break;
case SEQUENTIAL:
cur_stim_index = (cur_stim_index+1)%stimuli.size();
break;
case SEQUENTIAL_REV:
--cur_stim_index;
if ( cur_stim_index <= 0 )
cur_stim_index = stimuli.size() - 1 ;
break;
case RANDOM:
default:
double draw = order_die();
double cummulative = 0; // TODO make this less greedy and do not compute this every draw
cur_stim_index = 0;
// cout.precision(5);
// cout << " draw " << draw << endl;
for ( unsigned int i = 0 ; i < probabilities.size() ; ++i ) {
cummulative += probabilities[i];
// cout << cummulative << endl;
if ( draw <= cummulative ) {
cur_stim_index = i;
break;
}
}
break;
}
set_active_pattern( cur_stim_index );
stimulus_active = true;
if ( randomintervals ) {
boost::normal_distribution<> dist(mean_on_period,mean_on_period/3);
boost::variate_generator<boost::mt19937&, boost::normal_distribution<> > die(order_gen, dist);
next_action_time = sys->get_clock() + (AurynTime)(max(0.0,die())/dt);
} else {
next_action_time = sys->get_clock() + (AurynTime)(mean_on_period/dt);
}
}
}
write_sequence_file(dt*(sys->get_clock()+1));
}
}
void StimulusGroup::set_all(AurynFloat val)
{
for ( unsigned int i = 0 ; i < get_rank_size() ; ++i )
activity[i] = val;
}
AurynDouble SpikingGroup::get_effective_load()
{
return get_rank_size()*effective_load_multiplier;
}
NeuronID SpikingGroup::get_post_size()
{
return get_rank_size();
}
NeuronID SpikingGroup::ranksize() {
return get_rank_size();
}
