void
ht_neuron::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 )
{
double tt = 0.0; // it's all relative!
// adaptive step integration
while ( tt < B_.step_ )
{
const int status = gsl_odeiv_evolve_apply( B_.e_,
B_.c_,
B_.s_,
&B_.sys_, // system of ODE
&tt, // from t...
B_.step_, // ...to t=t+h
&B_.IntegrationStep_, // integration window (written on!)
S_.y_ ); // neuron state
if ( status != GSL_SUCCESS )
throw GSLSolverFailure( get_name(), status );
}
// Deactivate potassium current after spike time have expired
if ( S_.r_potassium_ && --S_.r_potassium_ == 0 )
S_.g_spike_ = false; // Deactivate potassium current.
// Add new spikes to node state array
for ( size_t i = 0; i < B_.spike_inputs_.size(); ++i )
S_.y_[ 2 + 2 * i ] += V_.cond_steps_[ i ] * B_.spike_inputs_[ i ].get_value( lag );
// A spike is generated when the membrane potential (V) exceeds
// the threshold (Theta).
if ( !S_.g_spike_ && S_.y_[ State_::VM ] >= S_.y_[ State_::THETA ] )
{
// Set V and Theta to the sodium reversal potential.
S_.y_[ State_::VM ] = P_.E_Na;
S_.y_[ State_::THETA ] = P_.E_Na;
// Activate fast potassium current. Drives the
// membrane potential towards the potassium reversal
// potential (activate only if duration is non-zero).
S_.g_spike_ = V_.PotassiumRefractoryCounts_ > 0;
S_.r_potassium_ = V_.PotassiumRefractoryCounts_;
set_spiketime( Time::step( origin.get_steps() + lag + 1 ) );
SpikeEvent se;
network()->send( *this, se, lag );
}
// set new input current
B_.I_stim_ = B_.currents_.get_value( lag );
B_.logger_.record_data( origin.get_steps() + lag );
}
}
/* ----------------------------------------------------------------
* Update and spike handling functions
* ---------------------------------------------------------------- */
void
nest::hh_cond_exp_traub::update( Time const& origin, const long_t from, const long_t to )
{
assert( to >= 0 && ( delay ) from < kernel().connection_builder_manager.get_min_delay() );
assert( from < to );
for ( long_t lag = from; lag < to; ++lag )
{
double tt = 0.0; // it's all relative!
V_.U_old_ = S_.y_[ State_::V_M ];
// adaptive step integration
while ( tt < B_.step_ )
{
const int status = gsl_odeiv_evolve_apply( B_.e_,
B_.c_,
B_.s_,
&B_.sys_, // system of ODE
&tt, // from t...
B_.step_, // ...to t=t+h
&B_.IntegrationStep_, // integration window (written on!)
S_.y_ ); // neuron state
if ( status != GSL_SUCCESS )
throw GSLSolverFailure( get_name(), status );
}
S_.y_[ State_::G_EXC ] += B_.spike_exc_.get_value( lag );
S_.y_[ State_::G_INH ] += B_.spike_inh_.get_value( lag );
// sending spikes: crossing 0 mV, pseudo-refractoriness and local maximum...
// refractory?
if ( S_.r_ )
{
--S_.r_;
}
else
{
// (threshold && maximum )
if ( S_.y_[ State_::V_M ] >= P_.V_T + 30. && V_.U_old_ > S_.y_[ State_::V_M ] )
{
S_.r_ = V_.RefractoryCounts_;
set_spiketime( Time::step( origin.get_steps() + lag + 1 ) );
SpikeEvent se;
kernel().event_delivery_manager.send( *this, se, lag );
}
}
// set new input current
B_.I_stim_ = B_.currents_.get_value( lag );
// log state data
B_.logger_.record_data( origin.get_steps() + lag );
}
}
void
nest::iaf_cond_alpha::update( Time const& origin,
const long from,
const long to )
{
assert(
to >= 0 && ( delay ) from < kernel().connection_manager.get_min_delay() );
assert( from < to );
for ( long lag = from; lag < to; ++lag )
{
double t = 0.0;
// numerical integration with adaptive step size control:
// ------------------------------------------------------
// gsl_odeiv_evolve_apply performs only a single numerical
// integration step, starting from t and bounded by step;
// the while-loop ensures integration over the whole simulation
// step (0, step] if more than one integration step is needed due
// to a small integration step size;
// note that (t+IntegrationStep > step) leads to integration over
// (t, step] and afterwards setting t to step, but it does not
// enforce setting IntegrationStep to step-t; this is of advantage
// for a consistent and efficient integration across subsequent
// simulation intervals
while ( t < B_.step_ )
{
const int status = gsl_odeiv_evolve_apply( B_.e_,
B_.c_,
B_.s_,
&B_.sys_, // system of ODE
&t, // from t
B_.step_, // to t <= step
&B_.IntegrationStep_, // integration step size
S_.y ); // neuronal state
if ( status != GSL_SUCCESS )
{
throw GSLSolverFailure( get_name(), status );
}
}
// refractoriness and spike generation
if ( S_.r )
{ // neuron is absolute refractory
--S_.r;
S_.y[ State_::V_M ] = P_.V_reset; // clamp potential
}
else
// neuron is not absolute refractory
if ( S_.y[ State_::V_M ] >= P_.V_th )
{
S_.r = V_.RefractoryCounts;
S_.y[ State_::V_M ] = P_.V_reset;
// log spike with Archiving_Node
set_spiketime( Time::step( origin.get_steps() + lag + 1 ) );
SpikeEvent se;
kernel().event_delivery_manager.send( *this, se, lag );
}
// add incoming spikes
S_.y[ State_::DG_EXC ] += B_.spike_exc_.get_value( lag ) * V_.PSConInit_E;
S_.y[ State_::DG_INH ] += B_.spike_inh_.get_value( lag ) * V_.PSConInit_I;
// set new input current
B_.I_stim_ = B_.currents_.get_value( lag );
// log state data
B_.logger_.record_data( origin.get_steps() + lag );
}
}
void
nest::iaf_cond_exp::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 )
{
double t = 0.0;
// numerical integration with adaptive step size control:
// ------------------------------------------------------
// gsl_odeiv_evolve_apply performs only a single numerical
// integration step, starting from t and bounded by step;
// the while-loop ensures integration over the whole simulation
// step (0, step] if more than one integration step is needed due
// to a small integration step size;
// note that (t+IntegrationStep > step) leads to integration over
// (t, step] and afterwards setting t to step, but it does not
// enforce setting IntegrationStep to step-t; this is of advantage
// for a consistent and efficient integration across subsequent
// simulation intervals
while ( t < B_.step_ )
{
const int status = gsl_odeiv_evolve_apply( B_.e_,
B_.c_,
B_.s_,
&B_.sys_, // system of ODE
&t, // from t
B_.step_, // to t <= step
&B_.IntegrationStep_, // integration step size
S_.y_ ); // neuronal state
if ( status != GSL_SUCCESS )
throw GSLSolverFailure( get_name(), status );
}
S_.y_[ State_::G_EXC ] += B_.spike_exc_.get_value( lag );
S_.y_[ State_::G_INH ] += B_.spike_inh_.get_value( lag );
// absolute refractory period
if ( S_.r_ )
{ // neuron is absolute refractory
--S_.r_;
S_.y_[ State_::V_M ] = P_.V_reset_;
}
else
// neuron is not absolute refractory
if ( S_.y_[ State_::V_M ] >= P_.V_th_ )
{
S_.r_ = V_.RefractoryCounts_;
S_.y_[ State_::V_M ] = P_.V_reset_;
set_spiketime( Time::step( origin.get_steps() + lag + 1 ) );
SpikeEvent se;
network()->send( *this, se, lag );
}
// set new input current
B_.I_stim_ = B_.currents_.get_value( lag );
// log state data
B_.logger_.record_data( origin.get_steps() + lag );
}
}
void nest::aeif_cond_exp::update(const Time &origin, const long_t from, const long_t to)
{
assert ( to >= 0 && (delay) from < Scheduler::get_min_delay() );
assert ( from < to );
assert ( State_::V_M == 0 );
for ( long_t lag = from; lag < to; ++lag )
{
double t = 0.0;
if ( S_.r_ > 0 )
--S_.r_;
// numerical integration with adaptive step size control:
// ------------------------------------------------------
// gsl_odeiv_evolve_apply performs only a single numerical
// integration step, starting from t and bounded by step;
// the while-loop ensures integration over the whole simulation
// step (0, step] if more than one integration step is needed due
// to a small integration step size;
// note that (t+IntegrationStep > step) leads to integration over
// (t, step] and afterwards setting t to step, but it does not
// enforce setting IntegrationStep to step-t
while ( t < B_.step_ )
{
const int status = gsl_odeiv_evolve_apply(B_.e_, B_.c_, B_.s_,
&B_.sys_, // system of ODE
&t, // from t
B_.step_, // to t <= step
&B_.IntegrationStep_, // integration step size
S_.y_); // neuronal state
if ( status != GSL_SUCCESS )
throw GSLSolverFailure(get_name(), status);
// check for unreasonable values; we allow V_M to explode
if ( S_.y_[State_::V_M] < -1e3 ||
S_.y_[State_::W ] < -1e6 || S_.y_[State_::W] > 1e6 )
throw NumericalInstability(get_name());
// spikes are handled inside the while-loop
// due to spike-driven adaptation
if ( S_.r_ > 0 )
S_.y_[State_::V_M] = P_.V_reset_;
else if ( S_.y_[State_::V_M] >= P_.V_peak_ )
{
S_.y_[State_::V_M] = P_.V_reset_;
S_.y_[State_::W] += P_.b; // spike-driven adaptation
S_.r_ = V_.RefractoryCounts_;
set_spiketime(Time::step(origin.get_steps() + lag + 1));
SpikeEvent se;
network()->send(*this, se, lag);
}
}
S_.y_[State_::G_EXC] += B_.spike_exc_.get_value(lag);
S_.y_[State_::G_INH] += B_.spike_inh_.get_value(lag);
// set new input current
B_.I_stim_ = B_.currents_.get_value(lag);
// log state data
B_.logger_.record_data(origin.get_steps() + lag);
}
}
void
nest::iaf_cond_alpha_mc::update( Time const& origin,
const long from,
const long to )
{
assert(
to >= 0 && ( delay ) from < kernel().connection_manager.get_min_delay() );
assert( from < to );
for ( long lag = from; lag < to; ++lag )
{
double t = 0.0;
// numerical integration with adaptive step size control:
// ------------------------------------------------------
// gsl_odeiv_evolve_apply performs only a single numerical
// integration step, starting from t and bounded by step;
// the while-loop ensures integration over the whole simulation
// step (0, step] if more than one integration step is needed due
// to a small integration step size;
// note that (t+IntegrationStep > step) leads to integration over
// (t, step] and afterwards setting t to step, but it does not
// enforce setting IntegrationStep to step-t; this is of advantage
// for a consistent and efficient integration across subsequent
// simulation intervals
while ( t < B_.step_ )
{
const int status = gsl_odeiv_evolve_apply( B_.e_,
B_.c_,
B_.s_,
&B_.sys_, // system of ODE
&t, // from t
B_.step_, // to t <= step
&B_.IntegrationStep_, // integration step size
S_.y_ ); // neuronal state
if ( status != GSL_SUCCESS )
{
throw GSLSolverFailure( get_name(), status );
}
}
// add incoming spikes at end of interval
// exploit here that spike buffers are compartment for compartment,
// alternating between excitatory and inhibitory
for ( size_t n = 0; n < NCOMP; ++n )
{
S_.y_[ n * State_::STATE_VEC_COMPS + State_::DG_EXC ] +=
B_.spikes_[ 2 * n ].get_value( lag ) * V_.PSConInit_E_[ n ];
S_.y_[ n * State_::STATE_VEC_COMPS + State_::DG_INH ] +=
B_.spikes_[ 2 * n + 1 ].get_value( lag ) * V_.PSConInit_I_[ n ];
}
// refractoriness and spiking
// exploit here that plain offset enum value V_M indexes soma V_M
if ( S_.r_ )
{ // neuron is absolute refractory
--S_.r_;
S_.y_[ State_::V_M ] = P_.V_reset;
}
else if ( S_.y_[ State_::V_M ] >= P_.V_th )
{ // neuron fires spike
S_.r_ = V_.RefractoryCounts_;
S_.y_[ State_::V_M ] = P_.V_reset;
set_spiketime( Time::step( origin.get_steps() + lag + 1 ) );
SpikeEvent se;
kernel().event_delivery_manager.send( *this, se, lag );
}
// set new input currents
for ( size_t n = 0; n < NCOMP; ++n )
{
B_.I_stim_[ n ] = B_.currents_[ n ].get_value( lag );
}
// log state data
B_.logger_.record_data( origin.get_steps() + lag );
}
}
bool
nest::hh_psc_alpha_gap::update_( Time const& origin,
const long from,
const long to,
const bool wfr_update )
{
assert(
to >= 0 && ( delay ) from < kernel().connection_manager.get_min_delay() );
assert( from < to );
bool done = true;
const size_t interpolation_order =
kernel().simulation_manager.get_wfr_interpolation_order();
const double wfr_tol = kernel().simulation_manager.get_wfr_tol();
// allocate memory to store the new interpolation coefficients
// to be sent by gap event
const size_t quantity =
kernel().connection_manager.get_min_delay() * ( interpolation_order + 1 );
std::vector< double > new_coefficients( quantity, 0.0 );
// parameters needed for piecewise interpolation
double y_i = 0.0, y_ip1 = 0.0, hf_i = 0.0, hf_ip1 = 0.0;
double f_temp[ State_::STATE_VEC_SIZE ];
for ( long lag = from; lag < to; ++lag )
{
// B_.lag is needed by hh_psc_alpha_gap_dynamics to
// determine the current section
B_.lag_ = lag;
if ( wfr_update )
{
y_i = S_.y_[ State_::V_M ];
if ( interpolation_order == 3 )
{
hh_psc_alpha_gap_dynamics(
0, S_.y_, f_temp, reinterpret_cast< void* >( this ) );
hf_i = B_.step_ * f_temp[ State_::V_M ];
}
}
double t = 0.0;
const double U_old = S_.y_[ State_::V_M ];
// numerical integration with adaptive step size control:
// ------------------------------------------------------
// gsl_odeiv_evolve_apply performs only a single numerical
// integration step, starting from t and bounded by step;
// the while-loop ensures integration over the whole simulation
// step (0, step] if more than one integration step is needed due
// to a small integration step size;
// note that (t+IntegrationStep > step) leads to integration over
// (t, step] and afterwards setting t to step, but it does not
// enforce setting IntegrationStep to step-t; this is of advantage
// for a consistent and efficient integration across subsequent
// simulation intervals
while ( t < B_.step_ )
{
const int status = gsl_odeiv_evolve_apply( B_.e_,
B_.c_,
B_.s_,
&B_.sys_, // system of ODE
&t, // from t
B_.step_, // to t <= step
&B_.IntegrationStep_, // integration step size
S_.y_ ); // neuronal state
if ( status != GSL_SUCCESS )
throw GSLSolverFailure( get_name(), status );
}
if ( not wfr_update )
{
S_.y_[ State_::DI_EXC ] +=
B_.spike_exc_.get_value( lag ) * V_.PSCurrInit_E_;
S_.y_[ State_::DI_INH ] +=
B_.spike_inh_.get_value( lag ) * V_.PSCurrInit_I_;
// sending spikes: crossing 0 mV, pseudo-refractoriness and local
// maximum...
// refractory?
if ( S_.r_ > 0 )
--S_.r_;
else
// ( threshold && maximum )
if ( S_.y_[ State_::V_M ] >= 0 && U_old > S_.y_[ State_::V_M ] )
{
S_.r_ = V_.RefractoryCounts_;
set_spiketime( Time::step( origin.get_steps() + lag + 1 ) );
SpikeEvent se;
kernel().event_delivery_manager.send( *this, se, lag );
}
// log state data
B_.logger_.record_data( origin.get_steps() + lag );
// set new input current
B_.I_stim_ = B_.currents_.get_value( lag );
}
else // if(wfr_update)
{
S_.y_[ State_::DI_EXC ] +=
B_.spike_exc_.get_value_wfr_update( lag ) * V_.PSCurrInit_E_;
S_.y_[ State_::DI_INH ] +=
B_.spike_inh_.get_value_wfr_update( lag ) * V_.PSCurrInit_I_;
// check deviation from last iteration
done = ( fabs( S_.y_[ State_::V_M ] - B_.last_y_values[ lag ] )
<= wfr_tol ) && done;
B_.last_y_values[ lag ] = S_.y_[ State_::V_M ];
// update different interpolations
// constant term is the same for each interpolation order
new_coefficients[ lag * ( interpolation_order + 1 ) + 0 ] = y_i;
switch ( interpolation_order )
{
case 0:
break;
case 1:
y_ip1 = S_.y_[ State_::V_M ];
new_coefficients[ lag * ( interpolation_order + 1 ) + 1 ] = y_ip1 - y_i;
break;
case 3:
y_ip1 = S_.y_[ State_::V_M ];
hh_psc_alpha_gap_dynamics(
B_.step_, S_.y_, f_temp, reinterpret_cast< void* >( this ) );
hf_ip1 = B_.step_ * f_temp[ State_::V_M ];
new_coefficients[ lag * ( interpolation_order + 1 ) + 1 ] = hf_i;
new_coefficients[ lag * ( interpolation_order + 1 ) + 2 ] =
-3 * y_i + 3 * y_ip1 - 2 * hf_i - hf_ip1;
new_coefficients[ lag * ( interpolation_order + 1 ) + 3 ] =
2 * y_i - 2 * y_ip1 + hf_i + hf_ip1;
break;
default:
throw BadProperty( "Interpolation order must be 0, 1, or 3." );
}
}
} // end for-loop
// if !wfr_update perform constant extrapolation and reset last_y_values
if ( not wfr_update )
{
for ( long temp = from; temp < to; ++temp )
new_coefficients[ temp * ( interpolation_order + 1 ) + 0 ] =
S_.y_[ State_::V_M ];
B_.last_y_values.clear();
B_.last_y_values.resize( kernel().connection_manager.get_min_delay(), 0.0 );
}
// Send gap-event
GapJunctionEvent ge;
ge.set_coeffarray( new_coefficients );
kernel().event_delivery_manager.send_secondary( *this, ge );
// Reset variables
B_.sumj_g_ij_ = 0.0;
B_.interpolation_coefficients.clear();
B_.interpolation_coefficients.resize( quantity, 0.0 );
return done;
}
void
nest::aeif_psc_alpha::update( Time const& origin,
const long from,
const long to )
{
assert(
to >= 0 && ( delay ) from < kernel().connection_manager.get_min_delay() );
assert( from < to );
assert( State_::V_M == 0 );
for ( long lag = from; lag < to; ++lag )
{
double t = 0.0;
// numerical integration with adaptive step size control:
// ------------------------------------------------------
// gsl_odeiv_evolve_apply performs only a single numerical
// integration step, starting from t and bounded by step;
// the while-loop ensures integration over the whole simulation
// step (0, step] if more than one integration step is needed due
// to a small integration step size;
// note that (t+IntegrationStep > step) leads to integration over
// (t, step] and afterwards setting t to step, but it does not
// enforce setting IntegrationStep to step-t; this is of advantage
// for a consistent and efficient integration across subsequent
// simulation intervals
while ( t < B_.step_ )
{
const int status = gsl_odeiv_evolve_apply( B_.e_,
B_.c_,
B_.s_,
&B_.sys_, // system of ODE
&t, // from t
B_.step_, // to t <= step
&B_.IntegrationStep_, // integration step size
S_.y_ ); // neuronal state
if ( status != GSL_SUCCESS )
{
throw GSLSolverFailure( get_name(), status );
}
// check for unreasonable values; we allow V_M to explode
if ( S_.y_[ State_::V_M ] < -1e3 || S_.y_[ State_::W ] < -1e6
|| S_.y_[ State_::W ] > 1e6 )
{
throw NumericalInstability( get_name() );
}
// spikes are handled inside the while-loop
// due to spike-driven adaptation
if ( S_.r_ > 0 )
{
S_.y_[ State_::V_M ] = P_.V_reset_;
}
else if ( S_.y_[ State_::V_M ] >= V_.V_peak )
{
S_.y_[ State_::V_M ] = P_.V_reset_;
S_.y_[ State_::W ] += P_.b; // spike-driven adaptation
/* Initialize refractory step counter.
* - We need to add 1 to compensate for count-down immediately after
* while loop.
* - If neuron has no refractory time, set to 0 to avoid refractory
* artifact inside while loop.
*/
S_.r_ = V_.refractory_counts_ > 0 ? V_.refractory_counts_ + 1 : 0;
set_spiketime( Time::step( origin.get_steps() + lag + 1 ) );
SpikeEvent se;
kernel().event_delivery_manager.send( *this, se, lag );
}
}
// decrement refractory count
if ( S_.r_ > 0 )
{
--S_.r_;
}
// apply spikes
S_.y_[ State_::DI_EXC ] += B_.spike_exc_.get_value( lag ) * V_.i0_ex_;
S_.y_[ State_::DI_INH ] += B_.spike_inh_.get_value( lag ) * V_.i0_in_;
// set new input current
B_.I_stim_ = B_.currents_.get_value( lag );
// log state data
B_.logger_.record_data( origin.get_steps() + lag );
}
}
