Eprop optimizations

libnestutil/nest_types.h

@@ -90,6 +90,7 @@ constexpr uint8_t NUM_BITS_SYN_ID = 6U;
 constexpr uint8_t NUM_BITS_LCID = 27U;
 constexpr uint8_t NUM_BITS_PROCESSED_FLAG = 1U;
 constexpr uint8_t NUM_BITS_MARKER_SPIKE_DATA = 2U;
+constexpr uint8_t NUM_BITS_MARKER_ACTIVATION = 1U;
 constexpr uint8_t NUM_BITS_LAG = 14U;
 constexpr uint8_t NUM_BITS_DELAY = 21U;
 constexpr uint8_t NUM_BITS_NODE_ID = 61U;

models/eprop_iaf.cpp

@@ -85,6 +85,7 @@ eprop_iaf::Parameters_::Parameters_()
   , kappa_( 0.97 )
   , kappa_reg_( 0.97 )
   , eprop_isi_trace_cutoff_( 1000.0 )
+  , activation_interval_( 3000.0 )
 {
 }
 
@@ -131,6 +132,7 @@ eprop_iaf::Parameters_::get( DictionaryDatum& d ) const
   def< double >( d, names::kappa, kappa_ );
   def< double >( d, names::kappa_reg, kappa_reg_ );
   def< double >( d, names::eprop_isi_trace_cutoff, eprop_isi_trace_cutoff_ );
+  def< double >( d, names::activation_interval, activation_interval_ );
 }
 
 double
@@ -168,6 +170,7 @@ eprop_iaf::Parameters_::set( const DictionaryDatum& d, Node* node )
   updateValueParam< double >( d, names::kappa, kappa_, node );
   updateValueParam< double >( d, names::kappa_reg, kappa_reg_, node );
   updateValueParam< double >( d, names::eprop_isi_trace_cutoff, eprop_isi_trace_cutoff_, node );
+  updateValueParam< double >( d, names::activation_interval, activation_interval_, node );
 
   if ( C_m_ <= 0 )
   {
@@ -209,11 +212,16 @@ eprop_iaf::Parameters_::set( const DictionaryDatum& d, Node* node )
     throw BadProperty( "Firing rate low-pass filter for regularization kappa_reg from range [0, 1] required." );
   }
 
-  if ( eprop_isi_trace_cutoff_ < 0.0 )
+  if ( activation_interval_ < 0 )
   {
-    throw BadProperty( "Cutoff of integration of eprop trace between spikes eprop_isi_trace_cutoff ≥ 0 required." );
+    throw BadProperty( "Interval between activations activation_interval ≥ 0 required." );
   }
 
+  if ( eprop_isi_trace_cutoff_ < 0.0 or eprop_isi_trace_cutoff_ > activation_interval_ )
+  {
+    throw BadProperty(
+      "Computation cutoff of eprop trace 0 ≤ eprop trace eprop_isi_trace_cutoff ≤ activation_interval required." );
+  }
   return delta_EL;
 }
 
@@ -271,6 +279,7 @@ eprop_iaf::pre_run_hook()
 
   V_.RefractoryCounts_ = Time( Time::ms( P_.t_ref_ ) ).get_steps();
   V_.eprop_isi_trace_cutoff_steps_ = Time( Time::ms( P_.eprop_isi_trace_cutoff_ ) ).get_steps();
+  V_.activation_interval_steps_ = Time( Time::ms( P_.activation_interval_ ) ).get_steps();
 
   // calculate the entries of the propagator matrix for the evolution of the state vector
 
@@ -314,6 +323,14 @@ eprop_iaf::update( Time const& origin, const long from, const long to )
       S_.z_ = 1.0;
       S_.v_m_ -= P_.V_th_ * S_.z_;
       S_.r_ = V_.RefractoryCounts_;
+      set_last_event_time( t );
+    }
+    else if ( get_last_event_time() > 0 and t - get_last_event_time() >= V_.activation_interval_steps_ )
+    {
+      SpikeEvent se;
+      se.set_activation();
+      kernel().event_delivery_manager.send( *this, se, lag );
+      set_last_event_time( t );
     }
 
     append_new_eprop_history_entry( t );
@@ -380,61 +397,70 @@ eprop_iaf::compute_gradient( const long t_spike,
   double& epsilon,
   double& weight,
   const CommonSynapseProperties& cp,
-  WeightOptimizer* optimizer )
+  WeightOptimizer* optimizer,
+  const bool activation,
+  const bool previous_event_was_activation,
+  double& sum_grad )
 {
-  double e = 0.0;                // eligibility trace
-  double z = 0.0;                // spiking variable
-  double z_current_buffer = 1.0; // buffer containing the spike that triggered the current integration
-  double psi = 0.0;              // surrogate gradient
-  double L = 0.0;                // learning signal
-  double firing_rate_reg = 0.0;  // firing rate regularization
-  double grad = 0.0;             // gradient
+  const auto& ecp = static_cast< const EpropSynapseCommonProperties& >( cp );
+  const auto& opt_cp = *ecp.optimizer_cp_;
+  const bool optimize_each_step = opt_cp.optimize_each_step_;
 
-  const EpropSynapseCommonProperties& ecp = static_cast< const EpropSynapseCommonProperties& >( cp );
-  const auto optimize_each_step = ( *ecp.optimizer_cp_ ).optimize_each_step_;
+  if ( not previous_event_was_activation )
+  {
+    sum_grad = 0.0; // sum of gradients
+  }
 
   auto eprop_hist_it = get_eprop_history( t_spike_previous - 1 );
 
-  const long t_compute_until = std::min( t_spike_previous + V_.eprop_isi_trace_cutoff_steps_, t_spike );
+  const long cutoff_end = t_spike_previous + V_.eprop_isi_trace_cutoff_steps_;
+  const long t_compute_until = std::min( cutoff_end, t_spike );
 
-  for ( long t = t_spike_previous; t < t_compute_until; ++t, ++eprop_hist_it )
+  if ( not previous_event_was_activation )
   {
-    z = z_previous_buffer;
-    z_previous_buffer = z_current_buffer;
-    z_current_buffer = 0.0;
-
-    psi = eprop_hist_it->surrogate_gradient_;
-    L = eprop_hist_it->learning_signal_;
-    firing_rate_reg = eprop_hist_it->firing_rate_reg_;
+    double z_current_buffer = 1.0; // spike that triggered current computation
 
-    z_bar = V_.P_v_m_ * z_bar + z;
-    e = psi * z_bar;
-    e_bar = P_.kappa_ * e_bar + e;
-    e_bar_reg = P_.kappa_reg_ * e_bar_reg + ( 1.0 - P_.kappa_reg_ ) * e;
-
-    if ( optimize_each_step )
+    for ( long t = t_spike_previous; t < t_compute_until; ++t, ++eprop_hist_it )
     {
-      grad = L * e_bar + firing_rate_reg * e_bar_reg;
-      weight = optimizer->optimized_weight( *ecp.optimizer_cp_, t, grad, weight );
-    }
-    else
-    {
-      grad += L * e_bar + firing_rate_reg * e_bar_reg;
+      const double z = z_previous_buffer; // spiking variable
+      z_previous_buffer = z_current_buffer;
+      z_current_buffer = 0.0;
+
+      const double psi = eprop_hist_it->surrogate_gradient_;          // surrogate gradient
+      const double L = eprop_hist_it->learning_signal_;               // learning signal
+      const double firing_rate_reg = eprop_hist_it->firing_rate_reg_; // firing rate regularization
+
+      z_bar = V_.P_v_m_ * z_bar + z;
+      const double e = psi * z_bar; // eligibility trace
+      e_bar = P_.kappa_ * e_bar + e;
+      e_bar_reg = P_.kappa_reg_ * e_bar_reg + ( 1.0 - P_.kappa_reg_ ) * e;
+
+      const double grad = L * e_bar + firing_rate_reg * e_bar_reg;
+
+      if ( optimize_each_step )
+      {
+        sum_grad = grad;
+        weight = optimizer->optimized_weight( opt_cp, t, sum_grad, weight );
+      }
+      else
+      {
+        sum_grad += grad;
+      }
     }
   }
 
-  if ( not optimize_each_step )
+  const long trace_decay_interval = t_spike - ( previous_event_was_activation ? t_spike_previous : t_compute_until );
+
+  if ( trace_decay_interval > 0 )
   {
-    weight = optimizer->optimized_weight( *ecp.optimizer_cp_, t_compute_until, grad, weight );
+    z_bar *= std::exp( std::log( V_.P_v_m_ ) * trace_decay_interval );
+    e_bar *= std::exp( std::log( P_.kappa_ ) * trace_decay_interval );
+    e_bar_reg *= std::exp( std::log( P_.kappa_reg_ ) * trace_decay_interval );
   }
 
-  const long cutoff_to_spike_interval = t_spike - t_compute_until;
-
-  if ( cutoff_to_spike_interval > 0 )
+  if ( not( activation or optimize_each_step ) )
   {
-    z_bar *= std::pow( V_.P_v_m_, cutoff_to_spike_interval );
-    e_bar *= std::pow( P_.kappa_, cutoff_to_spike_interval );
-    e_bar_reg *= std::pow( P_.kappa_reg_, cutoff_to_spike_interval );
+    weight = optimizer->optimized_weight( opt_cp, t_compute_until, sum_grad, weight );
   }
 }
 

models/eprop_iaf.h

@@ -230,6 +230,8 @@ Parameter                   Unit    Math equivalent         Default          Des
 ----------------------------------------------------------------------------------------------------------------
 Parameter                       Unit    Math equivalent             Default            Description
 =============================== ======= =========================== ================== =========================
+``activation_interval``         ms                                              3000.0 Interval between two
+                                                                                       activations
 ``c_reg``                               :math:`c_\text{reg}`                     0.0   Coefficient of firing
                                                                                        rate regularization
 ``eprop_isi_trace_cutoff``      ms      :math:`{\Delta t}_\text{c}` maximum value      Cutoff for integration of
@@ -300,9 +302,10 @@ References
        networks of spiking neurons. Nature Communications, 11:3625.
        https://doi.org/10.1038/s41467-020-17236-y
 
-.. [2] Korcsak-Gorzo A, Stapmanns J, Espinoza Valverde JA, Plesser HE,
-       Dahmen D, Bolten M, Van Albada SJ, Diesmann M. Event-based
-       implementation of eligibility propagation (in preparation)
+.. [2] Korcsak-Gorzo A, Espinoza Valverde JA, Stapmanns J, Plesser HE, Dahmen D,
+       Bolten M, van Albada SJ, Diesmann M (2025). Event-driven eligibility
+       propagation in large sparse networks: efficiency shaped by biological
+       realism. arXiv:2511.21674. https://doi.org/10.48550/arXiv.2511.21674
 
 .. start_surrogate-gradient-references
 
@@ -352,7 +355,7 @@ void register_eprop_iaf( const std::string& name );
  *
  * Class implementing a current-based leaky integrate-and-fire neuron model with delta-shaped postsynaptic currents for
  * e-prop plasticity according to Bellec et al. (2020) with additional biological features described in
- * Korcsak-Gorzo, Stapmanns, and Espinoza Valverde et al. (in preparation).
+ * Korcsak-Gorzo et al. (2025).
  */
 class eprop_iaf : public EpropArchivingNodeRecurrent< false >
 {
@@ -397,7 +400,10 @@ class eprop_iaf : public EpropArchivingNodeRecurrent< false >
     double&,
     double&,
     const CommonSynapseProperties&,
-    WeightOptimizer* ) override;
+    WeightOptimizer*,
+    const bool,
+    const bool,
+    double& ) override;
 
   long get_shift() const override;
   bool is_eprop_recurrent_node() const override;
@@ -458,6 +464,9 @@ class eprop_iaf : public EpropArchivingNodeRecurrent< false >
     //! Time interval from the previous spike until the cutoff of e-prop update integration between two spikes (ms).
     double eprop_isi_trace_cutoff_;
 
+    //! Interval between two activations.
+    long activation_interval_;
+
     //! Default constructor.
     Parameters_();
 
@@ -535,6 +544,9 @@ class eprop_iaf : public EpropArchivingNodeRecurrent< false >
 
     //! Time steps from the previous spike until the cutoff of e-prop update integration between two spikes.
     long eprop_isi_trace_cutoff_steps_;
+
+    //! Time steps of activation interval.
+    long activation_interval_steps_;
   };
 
   //! Get the current value of the membrane voltage.