void
nest::spike_detector::handle( SpikeEvent& e )
{
// accept spikes only if detector was active when spike was
// emitted
if ( device_.is_active( e.get_stamp() ) )
{
assert( e.get_multiplicity() > 0 );
long dest_buffer;
if ( kernel()
.modelrange_manager.get_model_of_gid( e.get_sender_gid() )
->has_proxies() )
{
// events from central queue
dest_buffer = kernel().event_delivery_manager.read_toggle();
}
else
{
// locally delivered events
dest_buffer = kernel().event_delivery_manager.write_toggle();
}
for ( int i = 0; i < e.get_multiplicity(); ++i )
{
// We store the complete events
Event* event = e.clone();
B_.spikes_[ dest_buffer ].push_back( event );
}
}
}
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::iaf_neuron::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::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::sinusoidal_poisson_generator::update( Time const& origin,
const long from,
const long to )
{
assert(
to >= 0 && ( delay ) from < kernel().connection_manager.get_min_delay() );
assert( from < to );
const long start = origin.get_steps();
// random number generator
librandom::RngPtr rng = kernel().rng_manager.get_rng( get_thread() );
// We iterate the dynamics even when the device is turned off,
// but do not issue spikes while it is off. In this way, the
// oscillators always have the right phase. This is quite
// time-consuming, so it should be done only if the device is
// on most of the time.
for ( long lag = from; lag < to; ++lag )
{
// update oscillator blocks, accumulate rate as sum of DC and N_osc_ AC
// elements rate is instantaneous sum of state
S_.rate_ = P_.rate_;
const double new_y_0 = V_.cos_ * S_.y_0_ - V_.sin_ * S_.y_1_;
S_.y_1_ = V_.sin_ * S_.y_0_ + V_.cos_ * S_.y_1_;
S_.y_0_ = new_y_0;
S_.rate_ += S_.y_1_;
if ( S_.rate_ < 0 )
{
S_.rate_ = 0;
}
// create spikes
if ( S_.rate_ > 0 && device_.is_active( Time::step( start + lag ) ) )
{
if ( P_.individual_spike_trains_ )
{
DSSpikeEvent se;
kernel().event_delivery_manager.send( *this, se, lag );
}
else
{
V_.poisson_dev_.set_lambda( S_.rate_ * V_.h_ );
long n_spikes = V_.poisson_dev_.ldev( rng );
SpikeEvent se;
se.set_multiplicity( n_spikes );
kernel().event_delivery_manager.send( *this, se, lag );
}
}
// store rate in Hz
B_.logger_.record_data( origin.get_steps() + lag );
}
}
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 nest::spike_generator::update(Time const & sliceT0, const long_t from, const long_t to)
{
if ( P_.spike_stamps_.empty() )
return;
assert( !P_.precise_times_
|| P_.spike_stamps_.size() == P_.spike_offsets_.size() );
assert( P_.spike_weights_.empty()
|| P_.spike_stamps_.size() == P_.spike_weights_.size() );
const Time tstart = sliceT0 + Time::step(from);
const Time tstop = sliceT0 + Time::step(to);
const Time& origin = device_.get_origin();
// We fire all spikes with time stamps up to including sliceT0 + to
while ( S_.position_ < P_.spike_stamps_.size() )
{
const Time tnext_stamp = origin + P_.spike_stamps_[S_.position_];
// this might happen due to wrong usage of the generator
if ( tnext_stamp <= tstart )
{
++S_.position_;
continue;
}
if ( tnext_stamp > tstop )
break;
if ( device_.is_active(tnext_stamp) )
{
SpikeEvent* se;
// if we have to deliver weighted spikes, we need to get the
// event back to set its weight according to the entry in
// spike_weights_, so we use a DSSpike event and event_hook()
if ( !P_.spike_weights_.empty() )
se = new DSSpikeEvent;
else
se = new SpikeEvent;
if ( P_.precise_times_ )
se->set_offset(P_.spike_offsets_[S_.position_]);
// we need to subtract one from stamp which is added again in send()
long_t lag = Time(tnext_stamp - sliceT0).get_steps() - 1;
// all spikes are sent locally, so offset information is always preserved
network()->send(*this, *se, lag);
delete se;
}
++S_.position_;
}
}
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::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::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::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 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 nest::iaf_psc_exp_ps::emit_instant_spike_(const Time & origin, const long_t lag,
const double_t spike_offs)
{
assert( S_.y2_ >= P_.U_th_ ); // ensure we are superthreshold
// set stamp and offset for spike
set_spiketime(Time::step(origin.get_steps() + lag + 1));
S_.last_spike_offset_ = spike_offs;
// reset neuron and make it refractory
S_.y2_ = P_.U_reset_;
S_.is_refractory_ = true;
// send spike
SpikeEvent se;
se.set_offset(S_.last_spike_offset_);
network()->send(*this, se, lag);
}
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( 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() );
}
void ginzburg::update(Time const & origin,
const long_t from, const long_t to)
{
assert(to >= 0 && (delay) from < Scheduler::get_min_delay());
assert(from < to);
for ( long_t lag = from ; lag < to ; ++lag )
{
// update the input current
// the buffer for incoming spikes for every time step contains the difference
// of the total input h with respect to the previous step, so sum them up
S_.h_ += B_.spikes_.get_value(lag);
// check, if the update needs to be done
if ( Time::step(origin.get_steps()+lag) > S_.t_next_ )
{
// change the state of the neuron with probability given by
// gain function
// if the state has changed, the neuron produces an event sent to all its targets
bool new_y = V_.rng_->drand() < gain_(S_.h_);
if ( new_y != S_.y_ )
{
SpikeEvent se;
// use multiplicity 2 to signalize transition to 1 state
// use multiplicity 1 to signalize transition to 0 state
se.set_multiplicity(new_y ? 2 : 1);
network()->send(*this, se, lag);
S_.y_ = new_y;
}
// draw next update interval from exponential distribution
S_.t_next_ += Time::ms ( V_.exp_dev_(V_.rng_) * P_.tau_m_ );
} // of if (update now)
// log state data
B_.logger_.record_data(origin.get_steps() + lag);
} // of for (lag ...
}
void
parrot_neuron_ps::update( Time const& origin,
long_t const from,
long_t const to )
{
assert( to >= 0 );
assert( static_cast< delay >( from )
< kernel().connection_manager.get_min_delay() );
assert( from < to );
// at start of slice, tell input queue to prepare for delivery
if ( from == 0 )
B_.events_.prepare_delivery();
for ( long_t lag = from; lag < to; ++lag )
{
// time at start of update step
long_t const T = origin.get_steps() + lag;
double_t ev_offset;
double_t ev_multiplicity; // parrot stores multiplicity in weight
bool end_of_refract;
while ( B_.events_.get_next_spike(
T, ev_offset, ev_multiplicity, end_of_refract ) )
{
const ulong_t multiplicity = static_cast< ulong_t >( ev_multiplicity );
// send spike
SpikeEvent se;
se.set_multiplicity( multiplicity );
se.set_offset( ev_offset );
kernel().event_delivery_manager.send( *this, se, lag );
for ( ulong_t i = 0; i < multiplicity; ++i )
{
set_spiketime( Time::step( T + 1 ), ev_offset );
}
}
}
}
void nest::iaf_psc_exp_canon::emit_spike_(Time const &origin, long_t const lag,
double_t const t0, double_t const dt)
{
// we know that the potential is subthreshold at t0, super at t0+dt
// compute spike time relative to beginning of step
double_t const spike_offset = V_.h_ms_ - (t0+thresh_find_(dt));
set_spiketime(Time::step(origin.get_steps() + lag + 1));
S_.last_spike_offset_ = spike_offset;
// reset neuron and make it refractory
S_.y2_ = P_.U_reset_;
S_.is_refractory_ = true;
// send spike
SpikeEvent se;
se.set_offset(S_.last_spike_offset_);
network()->send(*this, se, lag);
return;
}
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() );
}
//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(network()->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
const double_t ps_e_TauSyn = numerics::expm1(-dt/P_.tau_syn_); // needed in any case
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 nest::iaf_psc_exp_ps::emit_spike_(const Time & origin, const long_t lag,
const double_t t0, const double_t dt)
{
// we know that the potential is subthreshold at t0, super at t0+dt
// compute spike time relative to beginning of step
const double_t spike_offset = V_.h_ms_ - (t0 + bisectioning_(dt));
set_spiketime(Time::step(origin.get_steps() + lag + 1));
S_.last_spike_offset_ = spike_offset;
// reset neuron and make it refractory
S_.y2_ = P_.U_reset_;
S_.is_refractory_ = true;
// send spike
SpikeEvent se;
se.set_offset(spike_offset);
network()->send(*this, se, lag);
}
void
nest::amat2_psc_exp::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() );
}
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
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_alpha_canon::emit_instant_spike_( Time const& origin,
const long_t lag,
const double_t spike_offs )
{
assert( S_.y3_ >= P_.U_th_ ); // ensure we are superthreshold
// set stamp and offset for spike
S_.last_spike_step_ = origin.get_steps() + lag + 1;
S_.last_spike_offset_ = spike_offs;
// reset neuron and make it refractory
S_.y3_ = P_.U_reset_;
S_.is_refractory_ = true;
// send spike
set_spiketime( Time::step( S_.last_spike_step_ ), S_.last_spike_offset_ );
SpikeEvent se;
se.set_offset( S_.last_spike_offset_ );
kernel().event_delivery_manager.send( *this, se, lag );
return;
}
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::iaf_psc_alpha_canon::emit_spike_( Time const& origin,
const long_t lag,
const double_t t0,
const double_t dt )
{
// we know that the potential is subthreshold at t0, super at t0+dt
// compute spike time relative to beginning of step
S_.last_spike_step_ = origin.get_steps() + lag + 1;
S_.last_spike_offset_ = V_.h_ms_ - ( t0 + thresh_find_( dt ) );
// reset neuron and make it refractory
S_.y3_ = P_.U_reset_;
S_.is_refractory_ = true;
// send spike
set_spiketime( Time::step( S_.last_spike_step_ ), S_.last_spike_offset_ );
SpikeEvent se;
se.set_offset( S_.last_spike_offset_ );
kernel().event_delivery_manager.send( *this, se, lag );
return;
}
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());
}
}