void nest::iaf_psc_alpha_mod::handle(SpikeEvent & e)
{
assert(e.get_delay() > 0);
// port 0: spikes are normally handled.
if(e.get_rport() == 0 ){
if(e.get_weight() > 0.0)
B_.ex_spikes_.add_value(e.get_rel_delivery_steps(network()->get_slice_origin()),
e.get_weight() * e.get_multiplicity() );
else
B_.in_spikes_.add_value(e.get_rel_delivery_steps(network()->get_slice_origin()),
e.get_weight() * e.get_multiplicity() );
}
// port 1: spikes act as modulators for all spikes that arrive at....
if(e.get_rport() == 1 ){
B_.modspikes_.add_value(e.get_rel_delivery_steps(network()->get_slice_origin()),
e.get_weight() * e.get_multiplicity() );
}
// ....port 2. These weights get modulated.
if(e.get_rport() == 2 ){
if(e.get_weight() > 0.0){
double tmpweight = e.get_weight() * (1.0 + S_.ymod_) ;
tmpweight = (tmpweight > P_.max_weight_ ? P_.max_weight_ : tmpweight);
B_.ex_spikes_.add_value(e.get_rel_delivery_steps(network()->get_slice_origin()),
tmpweight * e.get_multiplicity() );
}
else
B_.in_spikes_.add_value(e.get_rel_delivery_steps(network()->get_slice_origin()),
e.get_weight() * e.get_multiplicity() );
}
}
void mynest::coronet_neuron::handle(SpikeEvent & e)
{
assert(e.get_delay() > 0);
assert(e.get_rport() < static_cast<int_t>(B_.spike_inputs_.size()));
B_.spike_inputs_[e.get_rport()].
add_value(e.get_rel_delivery_steps(network()->get_slice_origin()),
e.get_weight() * e.get_multiplicity() );
}
void
nest::ht_neuron::handle( SpikeEvent& e )
{
assert( e.get_delay() > 0 );
assert( e.get_rport() < static_cast< int_t >( B_.spike_inputs_.size() ) );
B_.spike_inputs_[ e.get_rport() ].add_value(
e.get_rel_delivery_steps( kernel().simulation_manager.get_slice_origin() ),
e.get_weight() * e.get_multiplicity() );
}
void
nest::iaf_cond_alpha_mc::handle( SpikeEvent& e )
{
assert( e.get_delay() > 0 );
assert( 0 <= e.get_rport() && e.get_rport() < 2 * NCOMP );
B_.spikes_[ e.get_rport() ].add_value(
e.get_rel_delivery_steps( kernel().simulation_manager.get_slice_origin() ),
e.get_weight() * e.get_multiplicity() );
}
void
gif_psc_exp_multisynapse::handle( SpikeEvent& e )
{
assert( e.get_delay() > 0 );
assert( ( e.get_rport() > 0 )
&& ( ( size_t ) e.get_rport() <= P_.n_receptors_() ) );
B_.spikes_[ e.get_rport() - 1 ].add_value(
e.get_rel_delivery_steps( kernel().simulation_manager.get_slice_origin() ),
e.get_weight() * e.get_multiplicity() );
}
void
iaf_psc_alpha_multisynapse::handle( SpikeEvent& e )
{
assert( e.get_delay_steps() > 0 );
B_.spikes_[ e.get_rport() - 1 ].add_value(
e.get_rel_delivery_steps( kernel().simulation_manager.get_slice_origin() ),
e.get_weight() * e.get_multiplicity() );
}
void
aeif_cond_alpha_multisynapse::handle( SpikeEvent& e )
{
assert( e.get_delay() > 0 );
assert( ( e.get_rport() > 0 ) && ( ( size_t ) e.get_rport() <= P_.num_of_receptors_ ) );
if ( e.get_weight() > 0.0 )
{
B_.spike_exc_[ e.get_rport() - 1 ].add_value(
e.get_rel_delivery_steps( network()->get_slice_origin() ),
e.get_weight() * e.get_multiplicity() );
}
else
{
B_.spike_inh_[ e.get_rport() - 1 ].add_value(
e.get_rel_delivery_steps( network()->get_slice_origin() ),
-e.get_weight() * e.get_multiplicity() ); // keep conductances positive
}
}
void
iaf_psc_alpha_multisynapse::handle( SpikeEvent& e )
{
assert( e.get_delay() > 0 );
for ( size_t i = 0; i < P_.num_of_receptors_; ++i )
{
if ( P_.receptor_types_[ i ] == e.get_rport() )
{
B_.spikes_[ i ].add_value( e.get_rel_delivery_steps( network()->get_slice_origin() ),
e.get_weight() * e.get_multiplicity() );
}
}
}
// function handles exact spike times
void
parrot_neuron_ps::handle( SpikeEvent& e )
{
// Repeat only spikes incoming on port 0, port 1 will be ignored
if ( 0 == e.get_rport() )
{
assert( e.get_delay() > 0 );
// We need to compute the absolute time stamp of the delivery time
// of the spike, since spikes might spend longer than min_delay_
// in the queue. The time is computed according to Time Memo, Rule 3.
const long_t Tdeliver = e.get_stamp().get_steps() + e.get_delay() - 1;
// parrot ignores weight of incoming connection, store multiplicity
B_.events_.add_spike(
e.get_rel_delivery_steps(
nest::kernel().simulation_manager.get_slice_origin() ),
Tdeliver,
e.get_offset(),
static_cast< double_t >( e.get_multiplicity() ) );
}
}
void
nest::correlospinmatrix_detector::handle( SpikeEvent& e )
{
// The receiver port identifies the sending node in our
// sender list.
const rport curr_i = e.get_rport();
// If this assertion breaks, the sender does not honor the
// receiver port during connection or sending.
assert( 0 <= curr_i && curr_i <= P_.N_channels_ - 1 );
// accept spikes only if detector was active when spike was emitted
Time const stamp = e.get_stamp();
if ( device_.is_active( stamp ) )
{
// The following logic implements the decoding
// A single spike signals a transition to 0 state, two spikes in same time step
// signal the transition to 1 state.
//
// Remember the global id of the sender of the last spike being received
// this assumes that several spikes being sent by the same neuron in the same time step
// are received consecutively or are conveyed by setting the multiplicity accordingly.
long_t m = e.get_multiplicity();
bool down_transition = false;
if ( m == 1 )
{ // multiplicity == 1, either a single 1->0 event or the first or second of a pair of 0->1
// events
if ( curr_i == S_.last_i_ && stamp == S_.t_last_in_spike_ )
{
// received twice the same gid, so transition 0->1
// revise the last event written to the buffer
S_.curr_state_[ curr_i ] = true;
S_.last_change_[ curr_i ] = stamp.get_steps();
S_.tentative_down_ = false; // previous event was first event of two, so no down transition
}
else
{
// count this event negatively, assuming it comes as single event
// transition 1->0
// assume it will stay alone, so meaning a down tansition
if ( S_.tentative_down_ ) // really was a down transition, because we now have another event
{
down_transition = true;
}
S_.tentative_down_ = true;
}
}
else // multiplicity != 1
if ( m == 2 )
{
S_.curr_state_[ curr_i ] = true;
if ( S_.tentative_down_ ) // really was a down transition, because we now have another double
{ // event
down_transition = true;
}
S_.curr_state_[ S_.last_i_ ] = false;
S_.last_change_[ curr_i ] = stamp.get_steps();
S_.tentative_down_ = false; // previous event was first event of two, so no down transition
}
if ( down_transition ) // only do something on the downtransitions
{
long_t i = S_.last_i_; // index of neuron making the down transition
long_t t_i_on = S_.last_change_[ i ]; // last time point of change, must have been on
const long_t t_i_off = S_.t_last_in_spike_.get_steps();
// throw out all binary pulses from event list that are too old to enter the correlation
// window
BinaryPulselistType& otherPulses = S_.incoming_;
// calculate the minimum of those neurons that switched on and are not off yet
// every impulse in the queue that is further in the past than
// this minimum - tau_max cannot contribute to the count covariance
long_t t_min_on = t_i_on;
for ( int n = 0; n < P_.N_channels_; n++ )
{
if ( S_.curr_state_[ n ] )
{
if ( S_.last_change_[ n ] < t_min_on )
{
t_min_on = S_.last_change_[ n ];
}
}
}
const double_t tau_edge = P_.tau_max_.get_steps() + P_.delta_tau_.get_steps();
const delay min_delay = Scheduler::get_min_delay();
while (
!otherPulses.empty() && ( t_min_on - otherPulses.front().t_off_ ) >= tau_edge + min_delay )
otherPulses.pop_front();
// insert new event into history
// must happen here so event is taken into account in autocorrelation
const BinaryPulse_ bp_i( t_i_on, t_i_off, i );
BinaryPulselistType::iterator insert_pos = std::find_if( S_.incoming_.begin(),
S_.incoming_.end(),
std::bind2nd( std::greater< BinaryPulse_ >(), bp_i ) );
// insert before the position we have found
// if no element greater found, insert_pos == end(), so append at the end of the deque
S_.incoming_.insert( insert_pos, bp_i );
// go through history of other binary pulses
for ( BinaryPulselistType::const_iterator pulse_j = otherPulses.begin();
pulse_j != otherPulses.end();
++pulse_j )
{
// id of other neuron
long_t j = pulse_j->receptor_channel_;
long_t t_j_on = pulse_j->t_on_;
long_t t_j_off = pulse_j->t_off_;
// minimum and maximum time difference in histogram
long_t Delta_ij_min = std::max( t_j_on - t_i_off, -P_.tau_max_.get_steps() );
long_t Delta_ij_max = std::min( t_j_off - t_i_on, P_.tau_max_.get_steps() );
long_t t0 = P_.tau_max_.get_steps() / P_.delta_tau_.get_steps();
long_t dt = P_.delta_tau_.get_steps();
// zero time lag covariance
long_t l = std::min( t_i_off, t_j_off ) - std::max( t_i_on, t_j_on );
if ( l > 0 )
{
S_.count_covariance_[ i ][ j ][ t0 ] += l;
if ( i != j )
{
S_.count_covariance_[ j ][ i ][ t0 ] += l;
}
}
// non-zero time lag covariance
for ( long_t Delta = Delta_ij_min / dt; Delta < 0; Delta++ )
{
long_t l =
std::min( t_i_off, t_j_off - Delta * dt ) - std::max( t_i_on, t_j_on - Delta * dt );
if ( l > 0 )
{
S_.count_covariance_[ i ][ j ][ t0 - Delta ] += l;
S_.count_covariance_[ j ][ i ][ t0 + Delta ] += l;
}
}
if ( i != j )
{
for ( long_t Delta = 1; Delta <= Delta_ij_max / dt; Delta++ )
{
long_t l =
std::min( t_i_off, t_j_off - Delta * dt ) - std::max( t_i_on, t_j_on - Delta * dt );
if ( l > 0 )
{
S_.count_covariance_[ i ][ j ][ t0 - Delta ] += l;
S_.count_covariance_[ j ][ i ][ t0 + Delta ] += l;
}
}
}
} // loop over history
S_.last_change_[ i ] = t_i_off;
} // down transition happened
S_.last_i_ = curr_i;
S_.t_last_in_spike_ = stamp;
} // device active
}
void
nest::correlomatrix_detector::handle( SpikeEvent& e )
{
// The receiver port identifies the sending node in our
// sender list.
const rport sender = e.get_rport();
// If this assertion breaks, the sender does not honor the
// receiver port during connection or sending.
assert( 0 <= sender && sender <= P_.N_channels_ - 1 );
// accept spikes only if detector was active when spike was emitted
Time const stamp = e.get_stamp();
if ( device_.is_active( stamp ) )
{
const long_t spike_i = stamp.get_steps();
// find first appearence of element which is greater than spike_i
const Spike_ sp_i( spike_i, e.get_multiplicity() * e.get_weight(), sender );
SpikelistType::iterator insert_pos = std::find_if(
S_.incoming_.begin(), S_.incoming_.end(), std::bind2nd( std::greater< Spike_ >(), sp_i ) );
// insert before the position we have found
// if no element greater found, insert_pos == end(), so append at the end of the deque
S_.incoming_.insert( insert_pos, sp_i );
SpikelistType& otherSpikes = S_.incoming_;
const double_t tau_edge = P_.tau_max_.get_steps() + 0.5 * P_.delta_tau_.get_steps();
// throw away all spikes which are too old to
// enter the correlation window
const delay min_delay = Scheduler::get_min_delay();
while (
!otherSpikes.empty() && ( spike_i - otherSpikes.front().timestep_ ) >= tau_edge + min_delay )
otherSpikes.pop_front();
// all remaining spike times in the queue are >= spike_i - tau_edge - min_delay
// only count events in histogram, if the current event is within the time window [Tstart,
// Tstop]
// this is needed in order to prevent boundary effects
if ( P_.Tstart_ <= stamp && stamp <= P_.Tstop_ )
{
// calculate the effect of this spike immediately with respect to all
// spikes in the past of the respectively other sources
S_.n_events_[ sender ]++; // count this spike
for ( SpikelistType::const_iterator spike_j = otherSpikes.begin();
spike_j != otherSpikes.end();
++spike_j )
{
size_t bin;
long_t other = spike_j->receptor_channel_;
long_t sender_ind, other_ind;
if ( spike_i < spike_j->timestep_ )
{
sender_ind = other;
other_ind = sender;
}
else
{
sender_ind = sender;
other_ind = other;
}
if ( sender_ind <= other_ind )
{
bin = -1. * std::floor( ( 0.5 * P_.delta_tau_.get_steps()
- std::abs( spike_i - spike_j->timestep_ ) )
/ P_.delta_tau_.get_steps() );
}
else
{
bin = std::floor(
( 0.5 * P_.delta_tau_.get_steps() + std::abs( spike_i - spike_j->timestep_ ) )
/ P_.delta_tau_.get_steps() );
}
if ( bin < S_.covariance_[ sender_ind ][ other_ind ].size() )
{
// weighted histogram
S_.covariance_[ sender_ind ][ other_ind ][ bin ] +=
e.get_multiplicity() * e.get_weight() * spike_j->weight_;
if ( bin == 0 && ( spike_i - spike_j->timestep_ != 0 || other != sender ) )
S_.covariance_[ other_ind ][ sender_ind ][ bin ] +=
e.get_multiplicity() * e.get_weight() * spike_j->weight_;
// pure (unweighted) count histogram
S_.count_covariance_[ sender_ind ][ other_ind ][ bin ] += e.get_multiplicity();
if ( bin == 0 && ( spike_i - spike_j->timestep_ != 0 || other != sender ) )
S_.count_covariance_[ other_ind ][ sender_ind ][ bin ] += e.get_multiplicity();
}
}
} // t in [TStart, Tstop]
} // device active
}
