Generalize recurrent-to-readout and readout-to-recurrent delays

models/eprop_iaf.cpp

@@ -83,6 +83,9 @@ eprop_iaf::Parameters_::Parameters_()
   , V_th_( -55.0 - E_L_ )
   , kappa_( 0.97 )
   , eprop_isi_trace_cutoff_( std::numeric_limits< long >::max() )
+  , delay_rec_out_( 1 )
+  , delay_out_rec_( 1 )
+  , delay_total_( 1 )
 {
 }
 
@@ -129,6 +132,11 @@ eprop_iaf::Parameters_::get( DictionaryDatum& d ) const
   def< double >( d, names::V_th, V_th_ + E_L_ );
   def< double >( d, names::kappa, kappa_ );
   def< long >( d, names::eprop_isi_trace_cutoff, eprop_isi_trace_cutoff_ );
+
+  double delay_rec_out_ms = Time( Time::step( delay_rec_out_ ) ).get_ms();
+  def< double >( d, names::delay_rec_out, delay_rec_out_ms );
+  double delay_out_rec_ms = Time( Time::step( delay_out_rec_ ) ).get_ms();
+  def< double >( d, names::delay_out_rec, delay_out_rec_ms );
 }
 
 double
@@ -160,6 +168,14 @@ eprop_iaf::Parameters_::set( const DictionaryDatum& d, Node* node )
   updateValueParam< double >( d, names::kappa, kappa_, node );
   updateValueParam< long >( d, names::eprop_isi_trace_cutoff, eprop_isi_trace_cutoff_, node );
 
+  double delay_rec_out_ms = Time( Time::step( delay_rec_out_ ) ).get_ms();
+  updateValueParam< double >( d, names::delay_rec_out, delay_rec_out_ms, node );
+  delay_rec_out_ = Time( Time::ms( delay_rec_out_ms ) ).get_steps();
+
+  double delay_out_rec_ms = Time( Time::step( delay_out_rec_ ) ).get_ms();
+  updateValueParam< double >( d, names::delay_out_rec, delay_out_rec_ms, node );
+  delay_out_rec_ = Time( Time::ms( delay_out_rec_ms ) ).get_steps();
+
   if ( C_m_ <= 0 )
   {
     throw BadProperty( "Membrane capacitance C_m > 0 required." );
@@ -200,6 +216,18 @@ eprop_iaf::Parameters_::set( const DictionaryDatum& d, Node* node )
     throw BadProperty( "Cutoff of integration of eprop trace between spikes eprop_isi_trace_cutoff ≥ 0 required." );
   }
 
+  if ( delay_rec_out_ < 1 )
+  {
+    throw BadProperty( "Connection delay from recurrent to output neuron ≥ 1 required." );
+  }
+
+  if ( delay_out_rec_ < 1 )
+  {
+    throw BadProperty( "Broadcast delay of learning signals ≥ 1 required." );
+  }
+
+  delay_total_ = delay_rec_out_ + ( delay_out_rec_ - 1 );
+
   return delta_EL;
 }
 
@@ -266,6 +294,14 @@ eprop_iaf::pre_run_hook()
   V_.P_v_m_ = std::exp( -dt / P_.tau_m_ );
   V_.P_i_in_ = P_.tau_m_ / P_.C_m_ * ( 1.0 - V_.P_v_m_ );
   V_.P_z_in_ = P_.regular_spike_arrival_ ? 1.0 : 1.0 - V_.P_v_m_;
+
+  if ( eprop_history_.empty() )
+  {
+    for ( long t = -P_.delay_total_; t < 0; ++t )
+    {
+      emplace_new_eprop_history_entry( t );
+    }
+  }
 }
 
 long
@@ -379,33 +415,39 @@ eprop_iaf::handle( DataLoggingRequest& e )
 void
 eprop_iaf::compute_gradient( const long t_spike,
   const long t_spike_previous,
-  double& z_previous_buffer,
+  std::queue< double >& z_previous_buffer,
+  double& z_previous,
   double& z_bar,
   double& e_bar,
   double& epsilon,
   double& weight,
   const CommonSynapseProperties& cp,
   WeightOptimizer* optimizer )
 {
-  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 grad = 0.0;             // gradient
+  double e = 0.0;         // eligibility trace
+  double z = 0.0;         // spiking variable
+  double z_current = 1.0; // buffer containing the spike that triggered the current integration
+  double psi = 0.0;       // surrogate gradient
+  double L = 0.0;         // learning signal
+  double grad = 0.0;      // gradient
 
   const EpropSynapseCommonProperties& ecp = static_cast< const EpropSynapseCommonProperties& >( cp );
   const auto optimize_each_step = ( *ecp.optimizer_cp_ ).optimize_each_step_;
 
-  auto eprop_hist_it = get_eprop_history( t_spike_previous - 1 );
+  auto eprop_hist_it = get_eprop_history( t_spike_previous - P_.delay_total_ );
 
   const long t_compute_until = std::min( t_spike_previous + P_.eprop_isi_trace_cutoff_, t_spike );
 
   for ( long t = t_spike_previous; t < t_compute_until; ++t, ++eprop_hist_it )
   {
-    z = z_previous_buffer;
-    z_previous_buffer = z_current_buffer;
-    z_current_buffer = 0.0;
+    if ( P_.delay_total_ > 1 )
+    {
+      update_pre_syn_buffer_multiple_entries( z, z_current, z_previous, z_previous_buffer, t_spike, t );
+    }
+    else
+    {
+      update_pre_syn_buffer_one_entry( z, z_current, z_previous, z_previous_buffer, t_spike, t );
+    }
 
     psi = eprop_hist_it->surrogate_gradient_;
     L = eprop_hist_it->learning_signal_;

models/eprop_iaf.h

@@ -330,7 +330,8 @@ class eprop_iaf : public EpropArchivingNodeRecurrent
 
   void compute_gradient( const long t_spike,
     const long t_spike_previous,
-    double& z_previous_buffer,
+    std::queue< double >& z_previous_buffer,
+    double& z_previous,
     double& z_bar,
     double& e_bar,
     double& epsilon,
@@ -341,6 +342,9 @@ class eprop_iaf : public EpropArchivingNodeRecurrent
   long get_shift() const override;
   bool is_eprop_recurrent_node() const override;
   long get_eprop_isi_trace_cutoff() override;
+  long get_delay_total() const override;
+  long get_delay_recurrent_to_readout() const override;
+  long get_delay_readout_to_recurrent() const override;
 
   //! Compute the surrogate gradient.
   double ( eprop_iaf::*compute_surrogate_gradient )( double, double, double, double, double, double );
@@ -401,6 +405,15 @@ class eprop_iaf : public EpropArchivingNodeRecurrent
     //! eprop_isi_trace_cutoff_ and the inter-spike distance.
     long eprop_isi_trace_cutoff_;
 
+    //! Connection delay from recurrent to output neurons.
+    long delay_rec_out_;
+
+    //! Broadcast delay of learning signals.
+    long delay_out_rec_;
+
+    //! Sum of broadcast delay of learning signals and connection delay from recurrent to output neurons.
+    long delay_total_;
+
     //! Default constructor.
     Parameters_();
 
@@ -526,10 +539,35 @@ eprop_iaf::get_eprop_isi_trace_cutoff()
   return P_.eprop_isi_trace_cutoff_;
 }
 
+inline long
+eprop_iaf::get_delay_total() const
+{
+  return P_.delay_total_;
+}
+
+inline long
+eprop_iaf::get_delay_recurrent_to_readout() const
+{
+  return P_.delay_rec_out_;
+}
+
+inline long
+eprop_iaf::get_delay_readout_to_recurrent() const
+{
+  return P_.delay_out_rec_;
+}
+
 inline size_t
 eprop_iaf::send_test_event( Node& target, size_t receptor_type, synindex, bool )
 {
   SpikeEvent e;
+
+  // To perform a consistency check on the delay parameter d_out_rec between recurrent
+  // neurons and output neurons, the recurrent neurons send a test event with a delay
+  // specified by d_rec_out. Upon receiving the test event from the recurrent neuron,
+  // the output neuron checks if the delay with which the event was received matches
+  // its own specified delay parameter d_rec_out.
+  e.set_delay_steps( P_.delay_rec_out_ );
   e.set_sender( *this );
   return target.handles_test_event( e, receptor_type );
 }

models/eprop_iaf_adapt.cpp

@@ -87,6 +87,9 @@ eprop_iaf_adapt::Parameters_::Parameters_()
   , V_th_( -55.0 - E_L_ )
   , kappa_( 0.97 )
   , eprop_isi_trace_cutoff_( std::numeric_limits< long >::max() )
+  , delay_rec_out_( 1 )
+  , delay_out_rec_( 1 )
+  , delay_total_( 1 )
 {
 }
 
@@ -137,6 +140,11 @@ eprop_iaf_adapt::Parameters_::get( DictionaryDatum& d ) const
   def< double >( d, names::V_th, V_th_ + E_L_ );
   def< double >( d, names::kappa, kappa_ );
   def< long >( d, names::eprop_isi_trace_cutoff, eprop_isi_trace_cutoff_ );
+
+  double delay_rec_out_ms = Time( Time::step( delay_rec_out_ ) ).get_ms();
+  def< double >( d, names::delay_rec_out, delay_rec_out_ms );
+  double delay_out_rec_ms = Time( Time::step( delay_out_rec_ ) ).get_ms();
+  def< double >( d, names::delay_out_rec, delay_out_rec_ms );
 }
 
 double
@@ -170,6 +178,14 @@ eprop_iaf_adapt::Parameters_::set( const DictionaryDatum& d, Node* node )
   updateValueParam< double >( d, names::kappa, kappa_, node );
   updateValueParam< long >( d, names::eprop_isi_trace_cutoff, eprop_isi_trace_cutoff_, node );
 
+  double delay_rec_out_ms = Time( Time::step( delay_rec_out_ ) ).get_ms();
+  updateValueParam< double >( d, names::delay_rec_out, delay_rec_out_ms, node );
+  delay_rec_out_ = Time( Time::ms( delay_rec_out_ms ) ).get_steps();
+
+  double delay_out_rec_ms = Time( Time::step( delay_out_rec_ ) ).get_ms();
+  updateValueParam< double >( d, names::delay_out_rec, delay_out_rec_ms, node );
+  delay_out_rec_ = Time( Time::ms( delay_out_rec_ms ) ).get_steps();
+
   if ( adapt_beta_ < 0 )
   {
     throw BadProperty( "Threshold adaptation prefactor adapt_beta ≥ 0 required." );
@@ -220,6 +236,18 @@ eprop_iaf_adapt::Parameters_::set( const DictionaryDatum& d, Node* node )
     throw BadProperty( "Cutoff of integration of eprop trace between spikes eprop_isi_trace_cutoff ≥ 0 required." );
   }
 
+  if ( delay_rec_out_ < 1 )
+  {
+    throw BadProperty( "Connection delay from recurrent to output neuron ≥ 1 required." );
+  }
+
+  if ( delay_out_rec_ < 1 )
+  {
+    throw BadProperty( "Broadcast delay of learning signals ≥ 1 required." );
+  }
+
+  delay_total_ = delay_rec_out_ + ( delay_out_rec_ - 1 );
+
   return delta_EL;
 }
 
@@ -301,6 +329,14 @@ eprop_iaf_adapt::pre_run_hook()
   V_.P_i_in_ = P_.tau_m_ / P_.C_m_ * ( 1.0 - V_.P_v_m_ );
   V_.P_z_in_ = P_.regular_spike_arrival_ ? 1.0 : 1.0 - V_.P_v_m_;
   V_.P_adapt_ = std::exp( -dt / P_.adapt_tau_ );
+
+  if ( eprop_history_.empty() )
+  {
+    for ( long t = -P_.delay_total_; t < 0; ++t )
+    {
+      emplace_new_eprop_history_entry( t );
+    }
+  }
 }
 
 long
@@ -417,33 +453,39 @@ eprop_iaf_adapt::handle( DataLoggingRequest& e )
 void
 eprop_iaf_adapt::compute_gradient( const long t_spike,
   const long t_spike_previous,
-  double& z_previous_buffer,
+  std::queue< double >& z_previous_buffer,
+  double& z_previous,
   double& z_bar,
   double& e_bar,
   double& epsilon,
   double& weight,
   const CommonSynapseProperties& cp,
   WeightOptimizer* optimizer )
 {
-  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 grad = 0.0;             // gradient
+  double e = 0.0;         // eligibility trace
+  double z = 0.0;         // spiking variable
+  double z_current = 1.0; // buffer containing the spike that triggered the current integration
+  double psi = 0.0;       // surrogate gradient
+  double L = 0.0;         // learning signal
+  double grad = 0.0;      // gradient
 
   const EpropSynapseCommonProperties& ecp = static_cast< const EpropSynapseCommonProperties& >( cp );
   const auto optimize_each_step = ( *ecp.optimizer_cp_ ).optimize_each_step_;
 
-  auto eprop_hist_it = get_eprop_history( t_spike_previous - 1 );
+  auto eprop_hist_it = get_eprop_history( t_spike_previous - P_.delay_total_ );
 
   const long t_compute_until = std::min( t_spike_previous + P_.eprop_isi_trace_cutoff_, t_spike );
 
   for ( long t = t_spike_previous; t < t_compute_until; ++t, ++eprop_hist_it )
   {
-    z = z_previous_buffer;
-    z_previous_buffer = z_current_buffer;
-    z_current_buffer = 0.0;
+    if ( P_.delay_total_ > 1 )
+    {
+      update_pre_syn_buffer_multiple_entries( z, z_current, z_previous, z_previous_buffer, t_spike, t );
+    }
+    else
+    {
+      update_pre_syn_buffer_one_entry( z, z_current, z_previous, z_previous_buffer, t_spike, t );
+    }
 
     psi = eprop_hist_it->surrogate_gradient_;
     L = eprop_hist_it->learning_signal_;

models/eprop_iaf_adapt.h

@@ -346,7 +346,8 @@ class eprop_iaf_adapt : public EpropArchivingNodeRecurrent
 
   void compute_gradient( const long t_spike,
     const long t_spike_previous,
-    double& z_previous_buffer,
+    std::queue< double >& z_previous_buffer,
+    double& z_previous,
     double& z_bar,
     double& e_bar,
     double& epsilon,
@@ -357,6 +358,9 @@ class eprop_iaf_adapt : public EpropArchivingNodeRecurrent
   long get_shift() const override;
   bool is_eprop_recurrent_node() const override;
   long get_eprop_isi_trace_cutoff() override;
+  long get_delay_total() const override;
+  long get_delay_recurrent_to_readout() const override;
+  long get_delay_readout_to_recurrent() const override;
 
   //! Compute the surrogate gradient.
   double ( eprop_iaf_adapt::*compute_surrogate_gradient )( double, double, double, double, double, double );
@@ -423,6 +427,15 @@ class eprop_iaf_adapt : public EpropArchivingNodeRecurrent
     //! eprop_isi_trace_cutoff_ and the inter-spike distance.
     long eprop_isi_trace_cutoff_;
 
+    //! Connection delay from recurrent to output neurons.
+    long delay_rec_out_;
+
+    //! Broadcast delay of learning signals.
+    long delay_out_rec_;
+
+    //! Sum of broadcast delay of learning signals and connection delay from recurrent to output neurons.
+    long delay_total_;
+
     //! Default constructor.
     Parameters_();
 
@@ -571,10 +584,35 @@ eprop_iaf_adapt::get_eprop_isi_trace_cutoff()
   return P_.eprop_isi_trace_cutoff_;
 }
 
+inline long
+eprop_iaf_adapt::get_delay_total() const
+{
+  return P_.delay_total_;
+}
+
+inline long
+eprop_iaf_adapt::get_delay_recurrent_to_readout() const
+{
+  return P_.delay_rec_out_;
+}
+
+inline long
+eprop_iaf_adapt::get_delay_readout_to_recurrent() const
+{
+  return P_.delay_out_rec_;
+}
+
 inline size_t
 eprop_iaf_adapt::send_test_event( Node& target, size_t receptor_type, synindex, bool )
 {
   SpikeEvent e;
+
+  // To perform a consistency check on the delay parameter d_out_rec between recurrent
+  // neurons and output neurons, the recurrent neurons send a test event with a delay
+  // specified by d_rec_out. Upon receiving the test event from the recurrent neuron,
+  // the output neuron checks if the delay with which the event was received matches
+  // its own specified delay parameter d_rec_out.
+  e.set_delay_steps( P_.delay_rec_out_ );
   e.set_sender( *this );
   return target.handles_test_event( e, receptor_type );
 }