stdp_synapse_hom.h

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/*
 *  stdp_synapse_hom.h
 *
 *  This file is part of NEST.
 *
 *  Copyright (C) 2004 The NEST Initiative
 *
 *  NEST is free software: you can redistribute it and/or modify
 *  it under the terms of the GNU General Public License as published by
 *  the Free Software Foundation, either version 2 of the License, or
 *  (at your option) any later version.
 *
 *  NEST is distributed in the hope that it will be useful,
 *  but WITHOUT ANY WARRANTY; without even the implied warranty of
 *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *  GNU General Public License for more details.
 *
 *  You should have received a copy of the GNU General Public License
 *  along with NEST.  If not, see <http://www.gnu.org/licenses/>.
 *
 */
 
#ifndef STDP_SYNAPSE_HOM_H
#define STDP_SYNAPSE_HOM_H
 
// C++ includes:
#include <cmath>
 
// Includes from nestkernel:
#include "connection.h"
 
namespace nest
{
 
/* BeginUserDocs: synapse, chemical, functional, stdp
 
Short description
+++++++++++++++++
 
Synapse type for spike-timing dependent plasticity using homogeneous parameters
 
Description
+++++++++++
 
``stdp_synapse_hom`` is a connector to create synapses with spike time
dependent plasticity (as defined in :footcite:p:`Guetig2003`). Here the weight dependence
exponent can be set separately for potentiation and depression.
 
 Parameters controlling plasticity are identical for all synapses of the
 model, reducing the memory required per synapse considerably.
 
Examples:
 
* multiplicative STDP :footcite:p:`Rubin2001`  mu_plus = mu_minus = 1.0
* additive STDP       :footcite:p:`Song2000`  mu_plus = mu_minus = 0.0
* Guetig STDP         :footcite:p:`Guetig2003`  mu_plus = mu_minus = [0.0,1.0]
* van Rossum STDP     :footcite:p:`vanRossum2000`  mu_plus = 0.0 mu_minus = 1.0
 
.. warning::
 
   This synaptic plasticity rule does not take
   :ref:`precise spike timing <sim_precise_spike_times>` into
   account. When calculating the weight update, the precise spike time part
   of the timestamp is ignored.
 
Parameters
++++++++++
 
========= =======  ======================================================
 tau_plus ms       Time constant of STDP window, potentiation
                   (tau_minus defined in postsynaptic neuron)
 lambda   real     Step size
 alpha    real     Asymmetry parameter (scales depressing increments as
                   alpha*lambda)
 mu_plus  real     Weight dependence exponent, potentiation
 mu_minus real     Weight dependence exponent, depression
 Wmax     real     Maximum allowed weight
========= =======  ======================================================
 
The parameters are common to all synapses of the model and must be set using
SetDefaults on the synapse model.
 
Transmits
+++++++++
 
SpikeEvent
 
References
++++++++++
 
.. footbibliography::
 
See also
++++++++
 
tsodyks_synapse, static_synapse
 
Examples using this model
+++++++++++++++++++++++++
 
.. listexamples:: stdp_synapse_hom
 
EndUserDocs */
 
class STDPHomCommonProperties : public CommonSynapseProperties
{
 
public:
  STDPHomCommonProperties();
 
  void get_status( Dictionary& d ) const;
 
  void set_status( const Dictionary& d, ConnectorModel& cm );
 
  // data members common to all connections
  double tau_plus_;
  double lambda_;
  double alpha_;
  double mu_plus_;
  double mu_minus_;
  double Wmax_;
};
 
 
void register_stdp_synapse_hom( const std::string& name );
 
template < typename targetidentifierT >
class stdp_synapse_hom : public Connection< targetidentifierT >
{
 
public:
  typedef STDPHomCommonProperties CommonPropertiesType;
  typedef Connection< targetidentifierT > ConnectionBase;
 
  static constexpr ConnectionModelProperties properties = ConnectionModelProperties::HAS_DELAY
    | ConnectionModelProperties::IS_PRIMARY | ConnectionModelProperties::SUPPORTS_HPC
    | ConnectionModelProperties::SUPPORTS_LBL;
 
  stdp_synapse_hom();
 
  stdp_synapse_hom( const stdp_synapse_hom& ) = default;
  stdp_synapse_hom& operator=( const stdp_synapse_hom& ) = default;
 
 
  // Explicitly declare all methods inherited from the dependent base
  // ConnectionBase. This avoids explicit name prefixes in all places these
  // functions are used. Since ConnectionBase depends on the template parameter,
  // they are not automatically found in the base class.
  using ConnectionBase::get_delay;
  using ConnectionBase::get_delay_steps;
  using ConnectionBase::get_rport;
  using ConnectionBase::get_target;
 
  void get_status( Dictionary& d ) const;
 
  void set_status( const Dictionary& d, ConnectorModel& cm );
 
  bool send( Event& e, size_t t, const STDPHomCommonProperties& );
 
  void
  set_weight( double w )
  {
    weight_ = w;
  }
 
 
  class ConnTestDummyNode : public ConnTestDummyNodeBase
  {
  public:
    // Ensure proper overriding of overloaded virtual functions.
    // Return values from functions are ignored.
    using ConnTestDummyNodeBase::handles_test_event;
    size_t
    handles_test_event( SpikeEvent&, size_t ) override
    {
      return invalid_port;
    }
  };
 
  /*
   * This function calls check_connection on the sender and checks if the
   * receiver accepts the event type and receptor type requested by the sender.
   * Node::check_connection() will either confirm the receiver port by returning
   * true or false if the connection should be ignored.
   * We have to override the base class' implementation, since for STDP
   * connections we have to call register_stdp_connection on the target neuron
   * to inform the Archiver to collect spikes for this connection.
   *
   * \param s The source node
   * \param r The target node
   * \param receptor_type The ID of the requested receptor type
   */
  void
  check_connection( Node& s, Node& t, size_t receptor_type, const CommonPropertiesType& )
  {
    ConnTestDummyNode dummy_target;
    ConnectionBase::check_connection_( dummy_target, s, t, receptor_type );
 
    t.register_stdp_connection( t_lastspike_ - get_delay(), get_delay() );
  }
 
private:
  double
  facilitate_( double w, double kplus, const STDPHomCommonProperties& cp )
  {
    double norm_w = ( w / cp.Wmax_ ) + ( cp.lambda_ * std::pow( 1.0 - ( w / cp.Wmax_ ), cp.mu_plus_ ) * kplus );
    return norm_w < 1.0 ? norm_w * cp.Wmax_ : cp.Wmax_;
  }
 
  double
  depress_( double w, double kminus, const STDPHomCommonProperties& cp )
  {
    double norm_w = ( w / cp.Wmax_ ) - ( cp.alpha_ * cp.lambda_ * std::pow( w / cp.Wmax_, cp.mu_minus_ ) * kminus );
    return norm_w > 0.0 ? norm_w * cp.Wmax_ : 0.0;
  }
 
  // data members of each connection
  double weight_;
  double Kplus_;
  double t_lastspike_;
};
 
template < typename targetidentifierT >
constexpr ConnectionModelProperties stdp_synapse_hom< targetidentifierT >::properties;
 
//
// Implementation of class stdp_synapse_hom.
//
 
template < typename targetidentifierT >
stdp_synapse_hom< targetidentifierT >::stdp_synapse_hom()
  : ConnectionBase()
  , weight_( 1.0 )
  , Kplus_( 0.0 )
  , t_lastspike_( 0.0 )
{
}
 
template < typename targetidentifierT >
inline bool
stdp_synapse_hom< targetidentifierT >::send( Event& e, size_t t, const STDPHomCommonProperties& cp )
{
  // synapse STDP depressing/facilitation dynamics
 
  const double t_spike = e.get_stamp().get_ms();
 
  // t_lastspike_ = 0 initially
 
  Node* target = get_target( t );
  double dendritic_delay = get_delay();
 
  // get spike history in relevant range (t1, t2] from postsynaptic neuron
  std::deque< histentry >::iterator start;
  std::deque< histentry >::iterator finish;
  target->get_history( t_lastspike_ - dendritic_delay, t_spike - dendritic_delay, &start, &finish );
  // facilitation due to postsynaptic spikes since last pre-synaptic spike
  double minus_dt;
  while ( start != finish )
  {
    minus_dt = t_lastspike_ - ( start->t_ + dendritic_delay );
    ++start;
    // get_history() should make sure that
    // start->t_ > t_lastspike - dendritic_delay, i.e. minus_dt < 0
    assert( minus_dt < -1.0 * kernel().connection_manager.get_stdp_eps() );
    weight_ = facilitate_( weight_, Kplus_ * std::exp( minus_dt / cp.tau_plus_ ), cp );
  }
 
  // depression due to new pre-synaptic spike
  weight_ = depress_( weight_, target->get_K_value( t_spike - dendritic_delay ), cp );
 
  e.set_receiver( *target );
  e.set_weight( weight_ );
  e.set_delay_steps( get_delay_steps() );
  e.set_rport( get_rport() );
  e();
 
  Kplus_ = Kplus_ * std::exp( ( t_lastspike_ - t_spike ) / cp.tau_plus_ ) + 1.0;
 
  t_lastspike_ = t_spike;
 
  return true;
}
 
template < typename targetidentifierT >
void
stdp_synapse_hom< targetidentifierT >::get_status( Dictionary& d ) const
{
 
  // base class properties, different for individual synapse
  ConnectionBase::get_status( d );
  d[ names::weight ] = weight_;
 
  // own properties, different for individual synapse
  d[ names::Kplus ] = Kplus_;
  d[ names::size_of ] = static_cast< long >( sizeof( *this ) );
}
 
template < typename targetidentifierT >
void
stdp_synapse_hom< targetidentifierT >::set_status( const Dictionary& d, ConnectorModel& cm )
{
  // base class properties
  ConnectionBase::set_status( d, cm );
  d.update_value( names::weight, weight_ );
 
  d.update_value( names::Kplus, Kplus_ );
}
 
}  // of namespace nest
 
#endif  // of #ifndef STDP_SYNAPSE_HOM_H