void nest::iaf_chs_2007::handle(SpikeEvent &e)
{
assert ( e.get_delay() > 0 );
if ( e.get_weight() >= 0.0 )
B_.spikes_ex_.add_value(e.get_rel_delivery_steps(network()->get_slice_origin()),
e.get_weight() * e.get_multiplicity() );
}
void nest::aeif_cond_exp::handle(SpikeEvent &e)
{
assert ( e.get_delay() > 0 );
if(e.get_weight() > 0.0)
B_.spike_exc_.add_value(e.get_rel_delivery_steps(network()->get_slice_origin()),
e.get_weight() * e.get_multiplicity());
else
B_.spike_inh_.add_value(e.get_rel_delivery_steps(network()->get_slice_origin()),
-e.get_weight() * e.get_multiplicity()); // keep conductances positive
}
void nest::iaf_cond_delta::handle(SpikeEvent & e)
{
assert(e.get_delay() > 0);
if(e.get_weight() > 0.0)
B_.spikes_ex_.add_value(e.get_rel_delivery_steps(network()->get_slice_origin()),
e.get_weight() * e.get_multiplicity() );
else
B_.spikes_in_.add_value(e.get_rel_delivery_steps(network()->get_slice_origin()),
-e.get_weight() * e.get_multiplicity() ); // note: conductance is positive for both exc and inh synapses but we use the sign of spike event weight to distinguish which kind of synapse it is
}
void nest::hh_psc_alpha::handle(SpikeEvent & e)
{
assert(e.get_delay() > 0);
if(e.get_weight() > 0.0)
B_.spike_exc_.add_value(e.get_rel_delivery_steps(network()->get_slice_origin()),
e.get_weight() * e.get_multiplicity() );
else
B_.spike_inh_.add_value(e.get_rel_delivery_steps(network()->get_slice_origin()),
e.get_weight() * e.get_multiplicity() ); // current input, keep negative weight
}
void
nest::iaf_cond_exp_sfa_rr::handle( SpikeEvent& e )
{
assert( e.get_delay() > 0 );
if ( e.get_weight() > 0.0 )
B_.spike_exc_.add_value(
e.get_rel_delivery_steps( kernel().simulation_manager.get_slice_origin() ),
e.get_weight() * e.get_multiplicity() );
else
B_.spike_inh_.add_value(
e.get_rel_delivery_steps( kernel().simulation_manager.get_slice_origin() ),
-e.get_weight() * e.get_multiplicity() ); // ensure conductance is positive
}
void
nest::iaf_tum_2000::handle( SpikeEvent& e )
{
assert( e.get_delay() > 0 );
if ( e.get_weight() >= 0.0 )
B_.spikes_ex_.add_value(
e.get_rel_delivery_steps( kernel().simulation_manager.get_slice_origin() ),
e.get_weight() * e.get_multiplicity() );
else
B_.spikes_in_.add_value(
e.get_rel_delivery_steps( kernel().simulation_manager.get_slice_origin() ),
e.get_weight() * e.get_multiplicity() );
}
void
nest::sli_neuron::handle( SpikeEvent& e )
{
assert( e.get_delay() > 0 );
if ( e.get_weight() > 0.0 )
B_.ex_spikes_.add_value( e.get_rel_delivery_steps(
kernel().simulation_manager.get_slice_origin() ),
e.get_weight() * e.get_multiplicity() );
else
B_.in_spikes_.add_value( e.get_rel_delivery_steps(
kernel().simulation_manager.get_slice_origin() ),
e.get_weight() * e.get_multiplicity() );
}
// function handles exact spike times
void
nest::iaf_psc_alpha_presc::handle( SpikeEvent& e )
{
assert( e.get_delay() > 0 );
const long_t Tdeliver = e.get_rel_delivery_steps(
nest::kernel().simulation_manager.get_slice_origin() );
const double_t spike_weight =
V_.PSCInitialValue_ * e.get_weight() * e.get_multiplicity();
const double_t dt = e.get_offset();
// Building the new matrix for the offset of the spike
// NOTE: We do not use get matrix, but compute only those
// components we actually need for spike registration
// needed in any case
const double_t ps_e_TauSyn = numerics::expm1( -dt / P_.tau_syn_ );
const double_t ps_e_Tau = numerics::expm1( -dt / P_.tau_m_ );
const double_t ps_P31 = V_.gamma_sq_ * ps_e_Tau - V_.gamma_sq_ * ps_e_TauSyn
- dt * V_.gamma_ * ps_e_TauSyn - dt * V_.gamma_;
B_.spike_y1_.add_value( Tdeliver, spike_weight * ps_e_TauSyn + spike_weight );
B_.spike_y2_.add_value(
Tdeliver, spike_weight * dt * ps_e_TauSyn + spike_weight * dt );
B_.spike_y3_.add_value( Tdeliver, spike_weight * ps_P31 );
}
void
iaf_psc_alpha::handle( SpikeEvent& e )
{
assert( e.get_delay() > 0 );
const double s = e.get_weight() * e.get_multiplicity();
if ( e.get_weight() > 0.0 )
B_.ex_spikes_.add_value( e.get_rel_delivery_steps(
kernel().simulation_manager.get_slice_origin() ),
s );
else
B_.in_spikes_.add_value( e.get_rel_delivery_steps(
kernel().simulation_manager.get_slice_origin() ),
s );
}
void
nest::izhikevich::handle( SpikeEvent& e )
{
assert( e.get_delay() > 0 );
B_.spikes_.add_value( e.get_rel_delivery_steps( kernel().simulation_manager.get_slice_origin() ),
e.get_weight() * e.get_multiplicity() );
}
void
nest::izhikevich::handle( SpikeEvent& e )
{
assert( e.get_delay() > 0 );
B_.spikes_.add_value( e.get_rel_delivery_steps( network()->get_slice_origin() ),
e.get_weight() * e.get_multiplicity() );
}
void nest::hh_cond_exp_traub::handle(SpikeEvent & e)
{
assert(e.get_delay() > 0);
if(e.get_weight() > 0.0)
{
B_.spike_exc_.add_value(e.get_rel_delivery_steps(network()->get_slice_origin()),
e.get_weight() * e.get_multiplicity());
}
else
{
// add with negative weight, ie positive value, since we are changing a
// conductance
B_.spike_inh_.add_value(e.get_rel_delivery_steps(network()->get_slice_origin()),
-e.get_weight() * e.get_multiplicity());
}
}
void nest::iaf_neuron_dif_alpha::handle(SpikeEvent & e)
{
assert(e.get_delay() > 0);
B_.spikes_.add_value(e.get_rel_delivery_steps(network()->get_slice_origin()),
e.get_weight() * e.get_multiplicity() );
//std::cout<<"spike handle "<<e.get_stamp().get_ms()<<"\n";
}
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::amat2_psc_exp::handle( SpikeEvent& e )
{
assert( e.get_delay_steps() > 0 );
if ( e.get_weight() >= 0.0 )
{
B_.spikes_ex_.add_value( e.get_rel_delivery_steps(
kernel().simulation_manager.get_slice_origin() ),
e.get_weight() * e.get_multiplicity() );
}
else
{
B_.spikes_in_.add_value( e.get_rel_delivery_steps(
kernel().simulation_manager.get_slice_origin() ),
e.get_weight() * e.get_multiplicity() );
}
}
void
nest::aeif_cond_alpha_RK5::handle( SpikeEvent& e )
{
assert( e.get_delay_steps() > 0 );
if ( e.get_weight() > 0.0 )
{
B_.spike_exc_.add_value( e.get_rel_delivery_steps(
kernel().simulation_manager.get_slice_origin() ),
e.get_weight() * e.get_multiplicity() );
}
else
{
B_.spike_inh_.add_value( e.get_rel_delivery_steps(
kernel().simulation_manager.get_slice_origin() ),
-e.get_weight() * e.get_multiplicity() );
} // keep conductances positive
}
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 ginzburg::handle(SpikeEvent & e)
{
assert(e.get_delay() > 0);
// The following logic implements the encoding:
// 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();
long_t gid = e.get_sender_gid();
const Time &t_spike = e.get_stamp();
if (m == 1)
{ //multiplicity == 1, either a single 1->0 event or the first or second of a pair of 0->1 events
if (gid == S_.last_in_gid_ && t_spike == S_.t_last_in_spike_)
{
// received twice the same gid, so transition 0->1
// take double weight to compensate for subtracting first event
B_.spikes_.add_value(e.get_rel_delivery_steps(network()->get_slice_origin()),
2.0*e.get_weight());
}
else
{
// count this event negatively, assuming it comes as single event
// transition 1->0
B_.spikes_.add_value(e.get_rel_delivery_steps(network()->get_slice_origin()),
-e.get_weight());
}
}
else // multiplicity != 1
if (m == 2)
{
// count this event positively, transition 0->1
B_.spikes_.add_value(e.get_rel_delivery_steps(network()->get_slice_origin()),
e.get_weight());
}
S_.last_in_gid_ = gid;
S_.t_last_in_spike_ = t_spike;
}
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
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
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
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 pif_psc_delta_canon_cvv::handle(SpikeEvent & e)
{
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;
B_.events_.add_spike(e.get_rel_delivery_steps(network()->get_slice_origin()),
Tdeliver, e.get_offset(), e.get_weight() * e.get_multiplicity());
}
void
nest::iaf_psc_delta::handle( SpikeEvent& e )
{
assert( e.get_delay() > 0 );
// EX: We must compute the arrival time of the incoming spike
// explicity, since it depends on delay and offset within
// the update cycle. The way it is done here works, but
// is clumsy and should be improved.
B_.spikes_.add_value( e.get_rel_delivery_steps( network()->get_slice_origin() ),
e.get_weight() * e.get_multiplicity() );
}
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() );
}
}
}
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
}