rate_neuron_opn_impl.h

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/*
 *  rate_neuron_opn_impl.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 RATE_NEURON_OPN_IMPL_H
#define RATE_NEURON_OPN_IMPL_H
 
#include "rate_neuron_opn.h"
 
// C++ includes:
#include <cmath>  // in case we need isnan() // fabs
#include <cstdio>
#include <iomanip>
#include <iostream>
#include <limits>
#include <string>
 
// Includes from libnestutil:
#include "numerics.h"
 
// Includes from nestkernel:
#include "exceptions.h"
#include "kernel_manager.h"
#include "universal_data_logger_impl.h"
 
 
namespace nest
{
 
/* ----------------------------------------------------------------
 * Recordables map
 * ---------------------------------------------------------------- */
 
template < class TNonlinearities >
RecordablesMap< rate_neuron_opn< TNonlinearities > > rate_neuron_opn< TNonlinearities >::recordablesMap_;
 
 
/* ----------------------------------------------------------------
 * Default constructors defining default parameters and state
 * ---------------------------------------------------------------- */
 
template < class TNonlinearities >
rate_neuron_opn< TNonlinearities >::Parameters_::Parameters_()
  : tau_( 10.0 )  // ms
  , sigma_( 1.0 )
  , mu_( 0.0 )
  , linear_summation_( true )
  , mult_coupling_( false )
{
  recordablesMap_.create();
}
 
template < class TNonlinearities >
rate_neuron_opn< TNonlinearities >::State_::State_()
  : rate_( 0.0 )
  , noise_( 0.0 )
  , noisy_rate_( 0.0 )
{
}
 
/* ----------------------------------------------------------------
 * Parameter and state extractions and manipulation functions
 * ---------------------------------------------------------------- */
 
template < class TNonlinearities >
void
rate_neuron_opn< TNonlinearities >::Parameters_::get( Dictionary& d ) const
{
  d[ names::tau ] = tau_;
  d[ names::sigma ] = sigma_;
  d[ names::mu ] = mu_;
  d[ names::linear_summation ] = linear_summation_;
  d[ names::mult_coupling ] = mult_coupling_;
 
  // Also allow old names (to not break old scripts)
  d[ names::std ] = sigma_;
  d[ names::mean ] = mu_;
}
 
template < class TNonlinearities >
void
rate_neuron_opn< TNonlinearities >::Parameters_::set( const Dictionary& d, Node* node )
{
  update_value_param( d, names::tau, tau_, node );
  update_value_param( d, names::mu, mu_, node );
  update_value_param( d, names::sigma, sigma_, node );
  update_value_param( d, names::linear_summation, linear_summation_, node );
  update_value_param( d, names::mult_coupling, mult_coupling_, node );
 
  // Check for old names
  if ( update_value_param( d, names::mean, mu_, node ) )
  {
    LOG( VerbosityLevel::WARNING,
      "rate_neuron_opn< TNonlinearities >::Parameters_::set",
      "The parameter mean has been renamed to mu. Please use the new "
      "name from now on." );
  }
 
  if ( update_value_param( d, names::std, sigma_, node ) )
  {
    LOG( VerbosityLevel::WARNING,
      "rate_neuron_opn< TNonlinearities >::Parameters_::set",
      "The parameter std has been renamed to sigma. Please use the new "
      "name from now on." );
  }
 
  // Check for invalid parameters
  if ( tau_ <= 0 )
  {
    throw BadProperty( "Time constant must be > 0." );
  }
  if ( sigma_ < 0 )
  {
    throw BadProperty( "Noise parameter must not be negative." );
  }
}
 
template < class TNonlinearities >
void
rate_neuron_opn< TNonlinearities >::State_::get( Dictionary& d ) const
{
  d[ names::rate ] = rate_;              // Rate
  d[ names::noise ] = noise_;            // Noise
  d[ names::noisy_rate ] = noisy_rate_;  // Noisy rate
}
 
template < class TNonlinearities >
void
rate_neuron_opn< TNonlinearities >::State_::set( const Dictionary& d, Node* node )
{
  update_value_param( d, names::rate, rate_, node );  // Rate
}
 
template < class TNonlinearities >
rate_neuron_opn< TNonlinearities >::Buffers_::Buffers_( rate_neuron_opn< TNonlinearities >& n )
  : logger_( n )
{
}
 
template < class TNonlinearities >
rate_neuron_opn< TNonlinearities >::Buffers_::Buffers_( const Buffers_&, rate_neuron_opn< TNonlinearities >& n )
  : logger_( n )
{
}
 
/* ----------------------------------------------------------------
 * Default and copy constructor for node
 * ---------------------------------------------------------------- */
 
template < class TNonlinearities >
rate_neuron_opn< TNonlinearities >::rate_neuron_opn()
  : ArchivingNode()
  , P_()
  , S_()
  , B_( *this )
{
  recordablesMap_.create();
  Node::set_node_uses_wfr( kernel().simulation_manager.use_wfr() );
}
 
template < class TNonlinearities >
rate_neuron_opn< TNonlinearities >::rate_neuron_opn( const rate_neuron_opn& n )
  : ArchivingNode( n )
  , P_( n.P_ )
  , S_( n.S_ )
  , B_( n.B_, *this )
{
  Node::set_node_uses_wfr( kernel().simulation_manager.use_wfr() );
}
 
/* ----------------------------------------------------------------
 * Node initialization functions
 * ---------------------------------------------------------------- */
 
template < class TNonlinearities >
void
rate_neuron_opn< TNonlinearities >::init_buffers_()
{
  B_.delayed_rates_ex_.clear();  // includes resize
  B_.delayed_rates_in_.clear();  // includes resize
 
  // resize buffers
  const size_t buffer_size = kernel().connection_manager.get_min_delay();
  B_.instant_rates_ex_.resize( buffer_size, 0.0 );
  B_.instant_rates_in_.resize( buffer_size, 0.0 );
  B_.last_y_values.resize( buffer_size, 0.0 );
  B_.random_numbers.resize( buffer_size, numerics::nan );
 
  // initialize random numbers
  for ( unsigned int i = 0; i < buffer_size; i++ )
  {
    B_.random_numbers[ i ] = V_.normal_dist_( get_vp_specific_rng( get_thread() ) );
  }
 
  B_.logger_.reset();  // includes resize
  ArchivingNode::clear_history();
}
 
template < class TNonlinearities >
void
rate_neuron_opn< TNonlinearities >::pre_run_hook()
{
  B_.logger_.init();  // ensures initialization in case mm connected after Simulate
 
  const double h = Time::get_resolution().get_ms();
 
  // propagators
  V_.P1_ = std::exp( -h / P_.tau_ );
  V_.P2_ = -numerics::expm1( -h / P_.tau_ );
 
  // Gaussian white noise approximated by piecewise constant value
  V_.output_noise_factor_ = std::sqrt( P_.tau_ / h );
}
 
/* ----------------------------------------------------------------
 * Update and event handling functions
 */
 
template < class TNonlinearities >
bool
rate_neuron_opn< TNonlinearities >::update_( Time const& origin,
  const long from,
  const long to,
  const bool called_from_wfr_update )
{
  const size_t buffer_size = kernel().connection_manager.get_min_delay();
  const double wfr_tol = kernel().simulation_manager.get_wfr_tol();
  bool wfr_tol_exceeded = false;
 
  // allocate memory to store rates to be sent by rate events
  std::vector< double > new_rates( buffer_size, 0.0 );
 
  for ( long lag = from; lag < to; ++lag )
  {
    // get noise
    S_.noise_ = P_.sigma_ * B_.random_numbers[ lag ];
    // the noise is added to the noisy_rate variable
    S_.noisy_rate_ = S_.rate_ + V_.output_noise_factor_ * S_.noise_;
    // store rate
    new_rates[ lag ] = S_.noisy_rate_;
    // propagate rate to new time step (exponential integration)
    S_.rate_ = V_.P1_ * S_.rate_ + V_.P2_ * P_.mu_;
 
    double delayed_rates_in = 0;
    double delayed_rates_ex = 0;
    if ( called_from_wfr_update )
    {
      // use get_value_wfr_update to keep values in buffer
      delayed_rates_in = B_.delayed_rates_in_.get_value_wfr_update( lag );
      delayed_rates_ex = B_.delayed_rates_ex_.get_value_wfr_update( lag );
    }
    else
    {
      // use get_value to clear values in buffer after reading
      delayed_rates_in = B_.delayed_rates_in_.get_value( lag );
      delayed_rates_ex = B_.delayed_rates_ex_.get_value( lag );
    }
    double instant_rates_in = B_.instant_rates_in_[ lag ];
    double instant_rates_ex = B_.instant_rates_ex_[ lag ];
    double H_ex = 1.;  // valid value for non-multiplicative coupling
    double H_in = 1.;  // valid value for non-multiplicative coupling
    if ( P_.mult_coupling_ )
    {
      H_ex = nonlinearities_.mult_coupling_ex( new_rates[ lag ] );
      H_in = nonlinearities_.mult_coupling_in( new_rates[ lag ] );
    }
 
    if ( P_.linear_summation_ )
    {
      // In this case we explicitly need to distinguish the cases of
      // multiplicative coupling and non-multiplicative coupling in
      // order to compute input( ex + in ) instead of input(ex) + input(in) in
      // the non-multiplicative case.
      if ( P_.mult_coupling_ )
      {
        S_.rate_ += V_.P2_ * H_ex * nonlinearities_.input( delayed_rates_ex + instant_rates_ex );
        S_.rate_ += V_.P2_ * H_in * nonlinearities_.input( delayed_rates_in + instant_rates_in );
      }
      else
      {
        S_.rate_ +=
          V_.P2_ * nonlinearities_.input( delayed_rates_ex + instant_rates_ex + delayed_rates_in + instant_rates_in );
      }
    }
    else
    {
      // In this case multiplicative and non-multiplicative coupling
      // can be handled with the same code.
      S_.rate_ += V_.P2_ * H_ex * ( delayed_rates_ex + instant_rates_ex );
      S_.rate_ += V_.P2_ * H_in * ( delayed_rates_in + instant_rates_in );
    }
 
    if ( called_from_wfr_update )
    {
      // check if deviation from last iteration exceeds wfr_tol
      wfr_tol_exceeded = wfr_tol_exceeded or fabs( S_.rate_ - B_.last_y_values[ lag ] ) > wfr_tol;
      // update last_y_values for next wfr iteration
      B_.last_y_values[ lag ] = S_.rate_;
    }
    else
    {
      // rate logging
      B_.logger_.record_data( origin.get_steps() + lag );
    }
  }
 
  if ( not called_from_wfr_update )
  {
    // Send delay-rate-neuron-event. This only happens in the final iteration
    // to avoid accumulation in the buffers of the receiving neurons.
    DelayedRateConnectionEvent drve;
    drve.set_coeffarray( new_rates );
    kernel().event_delivery_manager.send_secondary( *this, drve );
 
    // clear last_y_values
    std::vector< double >( buffer_size, 0.0 ).swap( B_.last_y_values );
 
    // modify new_rates for rate-neuron-event as proxy for next min_delay
    for ( long temp = from; temp < to; ++temp )
    {
      new_rates[ temp ] = S_.noisy_rate_;
    }
 
    // create new random numbers
    B_.random_numbers.resize( buffer_size, numerics::nan );
    for ( unsigned int i = 0; i < buffer_size; i++ )
    {
      B_.random_numbers[ i ] = V_.normal_dist_( get_vp_specific_rng( get_thread() ) );
    }
  }
 
  // Send rate-neuron-event
  InstantaneousRateConnectionEvent rve;
  rve.set_coeffarray( new_rates );
  kernel().event_delivery_manager.send_secondary( *this, rve );
 
  // Reset variables
  std::vector< double >( buffer_size, 0.0 ).swap( B_.instant_rates_ex_ );
  std::vector< double >( buffer_size, 0.0 ).swap( B_.instant_rates_in_ );
 
  return wfr_tol_exceeded;
}
 
 
template < class TNonlinearities >
void
rate_neuron_opn< TNonlinearities >::handle( InstantaneousRateConnectionEvent& e )
{
  const double weight = e.get_weight();
 
  size_t i = 0;
  std::vector< unsigned int >::iterator it = e.begin();
  // The call to get_coeffvalue( it ) in this loop also advances the iterator it
  while ( it != e.end() )
  {
    if ( P_.linear_summation_ )
    {
      if ( weight >= 0.0 )
      {
        B_.instant_rates_ex_[ i ] += weight * e.get_coeffvalue( it );
      }
      else
      {
        B_.instant_rates_in_[ i ] += weight * e.get_coeffvalue( it );
      }
    }
    else
    {
      if ( weight >= 0.0 )
      {
        B_.instant_rates_ex_[ i ] += weight * nonlinearities_.input( e.get_coeffvalue( it ) );
      }
      else
      {
        B_.instant_rates_in_[ i ] += weight * nonlinearities_.input( e.get_coeffvalue( it ) );
      }
    }
    i++;
  }
}
 
template < class TNonlinearities >
void
rate_neuron_opn< TNonlinearities >::handle( DelayedRateConnectionEvent& e )
{
  const double weight = e.get_weight();
  const long delay = e.get_delay_steps() - kernel().connection_manager.get_min_delay();
 
  size_t i = 0;
  std::vector< unsigned int >::iterator it = e.begin();
  // The call to get_coeffvalue( it ) in this loop also advances the iterator it
  while ( it != e.end() )
  {
    if ( P_.linear_summation_ )
    {
      if ( weight >= 0.0 )
      {
        B_.delayed_rates_ex_.add_value( delay + i, weight * e.get_coeffvalue( it ) );
      }
      else
      {
        B_.delayed_rates_in_.add_value( delay + i, weight * e.get_coeffvalue( it ) );
      }
    }
    else
    {
      if ( weight >= 0.0 )
      {
        B_.delayed_rates_ex_.add_value( delay + i, weight * nonlinearities_.input( e.get_coeffvalue( it ) ) );
      }
      else
      {
        B_.delayed_rates_in_.add_value( delay + i, weight * nonlinearities_.input( e.get_coeffvalue( it ) ) );
      }
    }
    ++i;
  }
}
 
template < class TNonlinearities >
void
rate_neuron_opn< TNonlinearities >::handle( DataLoggingRequest& e )
{
  B_.logger_.handle( e );
}
 
}  // namespace
 
#endif /* #ifndef RATE_NEURON_OPN_IMPL_H */