Fix doxygen parameters

models/eprop_iaf.cpp

@@ -170,7 +170,7 @@ eprop_iaf::Parameters_::set( const DictionaryDatum& d, Node* node )
 
   if ( c_reg_ < 0 )
   {
-    throw BadProperty( "Firing rate regularization prefactor c_reg ≥ 0 required." );
+    throw BadProperty( "Firing rate regularization coefficient c_reg ≥ 0 required." );
   }
 
   if ( f_target_ < 0 )

models/eprop_iaf.h

@@ -234,7 +234,7 @@ V_th                        mV      :math:`v_\text{th}`                -55.0 Spi
 ----------------------------------------------------------------------------------------------------------------
 Parameter                   Unit Math equivalent             Default          Description
 =========================== ==== =========================== ================ ==================================
-c_reg                            :math:`c_\text{reg}`                     0.0 Prefactor of firing rate
+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
                                                              representable    e-prop update between two
@@ -347,6 +347,8 @@ EndUserDocs */
 void register_eprop_iaf( const std::string& name );
 
 /**
+ * @brief Class implementing a LIF neuron model for e-prop plasticity with additional biological features.
+ *
  * 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).
@@ -385,16 +387,16 @@ class eprop_iaf : public EpropArchivingNodeRecurrent
 
   void update( Time const&, const long, const long ) override;
 
-  void compute_gradient( const long t_spike,
-    const long t_spike_previous,
-    double& z_previous_buffer,
-    double& z_bar,
-    double& e_bar,
-    double& e_bar_reg,
-    double& epsilon,
-    double& weight,
-    const CommonSynapseProperties& cp,
-    WeightOptimizer* optimizer ) override;
+  void compute_gradient( const long,
+    const long,
+    double&,
+    double&,
+    double&,
+    double&,
+    double&,
+    double&,
+    const CommonSynapseProperties&,
+    WeightOptimizer* ) override;
 
   long get_shift() const override;
   bool is_eprop_recurrent_node() const override;
@@ -415,7 +417,7 @@ class eprop_iaf : public EpropArchivingNodeRecurrent
     //! Capacitance of the membrane (pF).
     double C_m_;
 
-    //! Prefactor of firing rate regularization.
+    //! Coefficient of firing rate regularization.
     double c_reg_;
 
     //! Leak / resting membrane potential (mV).

models/eprop_iaf_adapt.cpp

@@ -190,7 +190,7 @@ eprop_iaf_adapt::Parameters_::set( const DictionaryDatum& d, Node* node )
 
   if ( c_reg_ < 0 )
   {
-    throw BadProperty( "Firing rate regularization prefactor c_reg ≥ 0 required." );
+    throw BadProperty( "Firing rate regularization coefficient c_reg ≥ 0 required." );
   }
 
   if ( f_target_ < 0 )

models/eprop_iaf_adapt.h

@@ -217,7 +217,7 @@ V_th                        mV      :math:`v_\text{th}`                -55.0 Spi
 ----------------------------------------------------------------------------------------------------------------
 Parameter                   Unit    Math equivalent             Default          Description
 =========================== ======= =========================== ================ ===============================
-c_reg                               :math:`c_\text{reg}`                     0.0 Prefactor of firing rate
+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
                                                                 representable    e-prop update between two
@@ -315,6 +315,8 @@ EndUserDocs */
 void register_eprop_iaf_adapt( const std::string& name );
 
 /**
+ * @brief Class implementing an adaptive LIF neuron model for e-prop plasticity with additional biological features.
+ *
  * Class implementing a current-based leaky integrate-and-fire neuron model with delta-shaped postsynaptic currents and
  * threshold adaptation 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).
@@ -353,16 +355,16 @@ class eprop_iaf_adapt : public EpropArchivingNodeRecurrent
 
   void update( Time const&, const long, const long ) override;
 
-  void compute_gradient( const long t_spike,
-    const long t_spike_previous,
-    double& z_previous_buffer,
-    double& z_bar,
-    double& e_bar,
-    double& e_bar_reg,
-    double& epsilon,
-    double& weight,
-    const CommonSynapseProperties& cp,
-    WeightOptimizer* optimizer ) override;
+  void compute_gradient( const long,
+    const long,
+    double&,
+    double&,
+    double&,
+    double&,
+    double&,
+    double&,
+    const CommonSynapseProperties&,
+    WeightOptimizer* ) override;
 
   long get_shift() const override;
   bool is_eprop_recurrent_node() const override;
@@ -389,7 +391,7 @@ class eprop_iaf_adapt : public EpropArchivingNodeRecurrent
     //! Capacitance of the membrane (pF).
     double C_m_;
 
-    //! Prefactor of firing rate regularization.
+    //! Coefficient of firing rate regularization.
     double c_reg_;
 
     //! Leak / resting membrane potential (mV).

models/eprop_iaf_adapt_bsshslm_2020.cpp

@@ -181,7 +181,7 @@ eprop_iaf_adapt_bsshslm_2020::Parameters_::set( const DictionaryDatum& d, Node*
 
   if ( c_reg_ < 0 )
   {
-    throw BadProperty( "Firing rate regularization prefactor c_reg ≥ 0 required." );
+    throw BadProperty( "Firing rate regularization coefficient c_reg ≥ 0 required." );
   }
 
   if ( f_target_ < 0 )

models/eprop_iaf_adapt_bsshslm_2020.h

@@ -196,7 +196,7 @@ V_th                        mV      :math:`v_\text{th}`                -55.0 Spi
 ----------------------------------------------------------------------------------------------------------------
 Parameter                   Unit    Math equivalent         Default          Description
 =========================== ======= ======================= ================ ===================================
-c_reg                               :math:`c_\text{reg}`                 0.0 Prefactor of firing rate
+c_reg                               :math:`c_\text{reg}`                 0.0 Coefficient of firing rate
                                                                              regularization
 f_target                    Hz      :math:`f^\text{target}`             10.0 Target firing rate of rate
                                                                              regularization
@@ -285,6 +285,8 @@ EndUserDocs */
 void register_eprop_iaf_adapt_bsshslm_2020( const std::string& name );
 
 /**
+ * @brief Class implementing an adaptive LIF neuron model for e-prop plasticity.
+ *
  * Class implementing a current-based leaky integrate-and-fire neuron model with delta-shaped postsynaptic currents and
  * threshold adaptation for e-prop plasticity according to Bellec et al. (2020).
  */
@@ -322,11 +324,7 @@ class eprop_iaf_adapt_bsshslm_2020 : public EpropArchivingNodeRecurrent
 
   void update( Time const&, const long, const long ) override;
 
-  double compute_gradient( std::vector< long >& presyn_isis,
-    const long t_previous_update,
-    const long t_previous_trigger_spike,
-    const double kappa,
-    const bool average_gradient ) override;
+  double compute_gradient( std::vector< long >&, const long, const long, const double, const bool ) override;
 
   long get_shift() const override;
   bool is_eprop_recurrent_node() const override;
@@ -352,7 +350,7 @@ class eprop_iaf_adapt_bsshslm_2020 : public EpropArchivingNodeRecurrent
     //! Capacitance of the membrane (pF).
     double C_m_;
 
-    //! Prefactor of firing rate regularization.
+    //! Coefficient of firing rate regularization.
     double c_reg_;
 
     //! Leak / resting membrane potential (mV).

models/eprop_iaf_bsshslm_2020.cpp

@@ -161,7 +161,7 @@ eprop_iaf_bsshslm_2020::Parameters_::set( const DictionaryDatum& d, Node* node )
 
   if ( c_reg_ < 0 )
   {
-    throw BadProperty( "Firing rate regularization prefactor c_reg ≥ 0 required." );
+    throw BadProperty( "Firing rate regularization coefficient c_reg ≥ 0 required." );
   }
 
   if ( f_target_ < 0 )

models/eprop_iaf_bsshslm_2020.h

@@ -183,7 +183,7 @@ V_th                        mV      :math:`v_\text{th}`                -55.0 Spi
 ----------------------------------------------------------------------------------------------------------------
 Parameter                   Unit    Math equivalent         Default          Description
 =========================== ======= ======================= ================ ===================================
-c_reg                               :math:`c_\text{reg}`                 0.0 Prefactor of firing rate
+c_reg                               :math:`c_\text{reg}`                 0.0 Coefficient of firing rate
                                                                              regularization
 f_target                    Hz      :math:`f^\text{target}`             10.0 Target firing rate of rate
                                                                              regularization
@@ -270,6 +270,8 @@ EndUserDocs */
 void register_eprop_iaf_bsshslm_2020( const std::string& name );
 
 /**
+ * @brief Class implementing a LIF neuron model for e-prop plasticity.
+ *
  * 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).
  */
@@ -307,11 +309,7 @@ class eprop_iaf_bsshslm_2020 : public EpropArchivingNodeRecurrent
 
   void update( Time const&, const long, const long ) override;
 
-  double compute_gradient( std::vector< long >& presyn_isis,
-    const long t_previous_update,
-    const long t_previous_trigger_spike,
-    const double kappa,
-    const bool average_gradient ) override;
+  double compute_gradient( std::vector< long >&, const long, const long, const double, const bool ) override;
 
   long get_shift() const override;
   bool is_eprop_recurrent_node() const override;
@@ -331,7 +329,7 @@ class eprop_iaf_bsshslm_2020 : public EpropArchivingNodeRecurrent
     //! Capacitance of the membrane (pF).
     double C_m_;
 
-    //! Prefactor of firing rate regularization.
+    //! Coefficient of firing rate regularization.
     double c_reg_;
 
     //! Leak / resting membrane potential (mV).

models/eprop_iaf_psc_delta.cpp

@@ -202,7 +202,7 @@ nest::eprop_iaf_psc_delta::Parameters_::set( const DictionaryDatum& d, Node* nod
   updateValueParam< bool >( d, names::refractory_input, with_refr_input_, node );
   if ( c_reg_ < 0 )
   {
-    throw BadProperty( "Firing rate regularization prefactor c_reg ≥ 0 required." );
+    throw BadProperty( "Firing rate regularization coefficient c_reg ≥ 0 required." );
   }
 
   if ( f_target_ < 0 )

models/eprop_iaf_psc_delta.h

@@ -157,7 +157,7 @@ The following parameters can be set in the status dictionary.
 -------------------------------------------------------------------------------------------------------------------
 Parameter                   Unit    Math equivalent            Default          Description
 =========================== ======= ========================== ================ ===================================
-c_reg                               :math:`c_\text{reg}`                    0.0 Prefactor of firing rate
+c_reg                               :math:`c_\text{reg}`                    0.0 Coefficient of firing rate
                                                                                 regularization
 f_target                    Hz      :math:`f^\text{target}`                10.0 Target firing rate of rate
                                                                                 regularization
@@ -266,16 +266,16 @@ class eprop_iaf_psc_delta : public EpropArchivingNodeRecurrent
 
   void update( Time const&, const long, const long ) override;
 
-  void compute_gradient( const long t_spike,
-    const long t_spike_previous,
-    double& z_previous_buffer,
-    double& z_bar,
-    double& e_bar,
-    double& e_bar_reg,
-    double& epsilon,
-    double& weight,
-    const CommonSynapseProperties& cp,
-    WeightOptimizer* optimizer ) override;
+  void compute_gradient( const long,
+    const long,
+    double&,
+    double&,
+    double&,
+    double&,
+    double&,
+    double&,
+    const CommonSynapseProperties&,
+    WeightOptimizer* ) override;
 
   long get_shift() const override;
   bool is_eprop_recurrent_node() const override;
@@ -324,7 +324,7 @@ class eprop_iaf_psc_delta : public EpropArchivingNodeRecurrent
     bool with_refr_input_; //!< spikes arriving during refractory period are
                            //!< counted
 
-    //! Prefactor of firing rate regularization.
+    //! Coefficient of firing rate regularization.
     double c_reg_;
 
     //! Target firing rate of rate regularization (spikes/s).

models/eprop_learning_signal_connection.h

@@ -123,6 +123,8 @@ EndUserDocs */
 void register_eprop_learning_signal_connection( const std::string& name );
 
 /**
+ * @brief Class implementing a feedback connection model for e-prop plasticity with additional biological features.
+ *
  * Class implementing a synapse model transmitting secondary feedback learning signals 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).

models/eprop_learning_signal_connection_bsshslm_2020.h

@@ -127,6 +127,8 @@ EndUserDocs */
 void register_eprop_learning_signal_connection_bsshslm_2020( const std::string& name );
 
 /**
+ * @brief Class implementing a feedback connection model for e-prop plasticity.
+ *
  * Class implementing a synapse model transmitting secondary feedback learning signals for e-prop plasticity
  * according to Bellec et al. (2020).
  */

models/eprop_readout.h

@@ -252,6 +252,8 @@ EndUserDocs */
 void register_eprop_readout( const std::string& name );
 
 /**
+ * @brief Class implementing a readout neuron model for e-prop plasticity with additional biological features.
+ *
  * Class implementing a current-based leaky integrate readout 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).
@@ -300,16 +302,16 @@ class eprop_readout : public EpropArchivingNodeReadout
 
   void update( Time const&, const long, const long ) override;
 
-  void compute_gradient( const long t_spike,
-    const long t_spike_previous,
-    double& z_previous_buffer,
-    double& z_bar,
-    double& e_bar,
-    double& e_bar_reg,
-    double& epsilon,
-    double& weight,
-    const CommonSynapseProperties& cp,
-    WeightOptimizer* optimizer ) override;
+  void compute_gradient( const long,
+    const long,
+    double&,
+    double&,
+    double&,
+    double&,
+    double&,
+    double&,
+    const CommonSynapseProperties&,
+    WeightOptimizer* ) override;
 
   long get_shift() const override;
   bool is_eprop_recurrent_node() const override;

models/eprop_readout_bsshslm_2020.h

@@ -243,6 +243,8 @@ EndUserDocs */
 void register_eprop_readout_bsshslm_2020( const std::string& name );
 
 /**
+ * @brief Class implementing a readout neuron model for e-prop plasticity.
+ *
  * Class implementing a current-based leaky integrate readout neuron model with delta-shaped postsynaptic currents for
  * e-prop plasticity according to Bellec et al. (2020).
  */

models/eprop_synapse.h

@@ -152,6 +152,8 @@ Examples using this model
 EndUserDocs */
 
 /**
+ * @brief Base class implementing common properties for e-prop synapses with additional biological features.
+ *
  * Base class implementing common properties for the e-prop synapse model according to Bellec et al. (2020) with
  * additional biological features described in Korcsak-Gorzo, Stapmanns, and Espinoza Valverde et al.
  * (in preparation).
@@ -197,6 +199,8 @@ class EpropSynapseCommonProperties : public CommonSynapseProperties
 void register_eprop_synapse( const std::string& name );
 
 /**
+ * @brief Class implementing a synapse model for e-prop plasticity with additional biological features.
+ *
  * Class implementing a synapse model 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).
  *

models/eprop_synapse_bsshslm_2020.h

@@ -164,6 +164,8 @@ Examples using this model
 EndUserDocs */
 
 /**
+ * @brief Base class implementing common properties for e-prop synapses.
+ *
  * Base class implementing common properties for the e-prop synapse model according to Bellec et al. (2020).
  *
  * This class in particular manages a pointer to weight-optimizer common properties to support
@@ -210,6 +212,8 @@ class EpropSynapseBSSHSLM2020CommonProperties : public CommonSynapseProperties
 void register_eprop_synapse_bsshslm_2020( const std::string& name );
 
 /**
+ * @brief Class implementing a synapse model for e-prop plasticity.
+ *
  * Class implementing a synapse model for e-prop plasticity according to Bellec et al. (2020).
  *
  * @note Several aspects of this synapse are in place to reproduce the Tensorflow implementation of Bellec et al.

nestkernel/eprop_archiving_node.cpp

@@ -211,7 +211,7 @@ EpropArchivingNodeRecurrent::write_firing_rate_reg_to_history( const long t_curr
 }
 
 void
-EpropArchivingNodeRecurrent::write_firing_rate_reg_to_history( const long t,
+EpropArchivingNodeRecurrent::write_firing_rate_reg_to_history( const long time_step,
   const double z,
   const double f_target,
   const double kappa_reg,
@@ -230,7 +230,7 @@ EpropArchivingNodeRecurrent::write_firing_rate_reg_to_history( const long t,
 
   firing_rate_reg_ = c_reg * ( f_av_ - f_target_ );
 
-  auto it_hist = get_eprop_history( t );
+  auto it_hist = get_eprop_history( time_step );
   it_hist->firing_rate_reg_ = firing_rate_reg_;
 }