sigmoid_rate_gg_1998.h

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/*
 *  sigmoid_rate_gg_1998.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 SIGMOID_RATE_GG_1998_H
#define SIGMOID_RATE_GG_1998_H
 
// C++ includes:
#include <cmath>
 
// Includes from models:
#include "rate_neuron_ipn.h"
#include "rate_neuron_ipn_impl.h"
#include "rate_transformer_node.h"
#include "rate_transformer_node_impl.h"
 
namespace nest
{
 
/* BeginUserDocs: neuron, rate
 
Short description
+++++++++++++++++
 
rate model with sigmoidal gain function
 
Description
+++++++++++
 
``sigmoid_rate_gg_1998`` is an implementation of a nonlinear rate model with
input function as in :footcite:p:`Gancarz1998` :math:`input(h) = ( g \cdot h )^4 / ( .1^4 +( g \cdot h )^4 )`.
 
It either models a rate neuron with input noise (see ``rate_neuron_ipn``)
or a rate transformer (see ``rate_transformer_node``).
Input transformation can either be applied to individual inputs
or to the sum of all inputs.
 
The model supports connections to other rate models with either zero or
non-zero delay, and uses the secondary_event concept introduced with
the gap-junction framework.
 
Nonlinear rate neurons can be created by typing
``nest.Create('sigmoid_rate_gg_1998_ipn')``. Nonlinear rate transformers can be
created by typing ``nest.Create('rate_transformer_sigmoid_rate_gg_1998')``.
 
See also :footcite:p:`Hahne2017`, :footcite:p:`Hahne2015`.
 
Parameters
++++++++++
 
The following parameters can be set in the status dictionary. Note
that some of the parameters only apply to rate neurons and not to rate
transformers.
 
==================  ======= ==============================================
 rate               real    Rate (unitless)
 tau                ms      Time constant of rate dynamics
 mu                 real    Mean input
 sigma              real    Noise parameter
 g                  real    Gain parameter
 rectify_rate       real    Rectifying rate
 linear_summation   boolean Specifies type of non-linearity (see above)
 rectify_output     boolean Switch to restrict rate to values >= rectify_rate
==================  ======= ==============================================
 
 
Note:
 
The boolean parameter linear_summation determines whether the
input from different presynaptic neurons is first summed linearly and
then transformed by a nonlinearity (true), or if the input from
individual presynaptic neurons is first nonlinearly transformed and
then summed up (false). Default is true.
 
References
++++++++++
 
.. footbibliography::
 
Sends
+++++
 
InstantaneousRateConnectionEvent, DelayedRateConnectionEvent
 
Receives
++++++++
 
InstantaneousRateConnectionEvent, DelayedRateConnectionEvent,
DataLoggingRequest
 
See also
++++++++
 
rate_connection_instantaneous, rate_connection_delayed
 
 
Examples using this model
+++++++++++++++++++++++++
 
.. listexamples:: sigmoid_rate_gg_1998
 
EndUserDocs */
 
class nonlinearities_sigmoid_rate_gg_1998
{
private:
  double g_;
 
public:
  nonlinearities_sigmoid_rate_gg_1998()
    : g_( 1.0 )
  {
  }
 
  void get( Dictionary& ) const;              
  void set( const Dictionary&, Node* node );  
 
  double input( double h );                // non-linearity on input
  double mult_coupling_ex( double rate );  // factor of multiplicative coupling
  double mult_coupling_in( double rate );  // factor of multiplicative coupling
};
 
inline double
nonlinearities_sigmoid_rate_gg_1998::input( double h )
{
  return pow( g_ * h, 4. ) / ( pow( .1, 4. ) + pow( g_ * h, 4. ) );
}
 
inline double
nonlinearities_sigmoid_rate_gg_1998::mult_coupling_ex( double )
{
  return 1.;
}
 
inline double
nonlinearities_sigmoid_rate_gg_1998::mult_coupling_in( double )
{
  return 1.;
}
 
typedef rate_neuron_ipn< nonlinearities_sigmoid_rate_gg_1998 > sigmoid_rate_gg_1998_ipn;
void register_sigmoid_rate_gg_1998_ipn( const std::string& name );
 
typedef rate_transformer_node< nonlinearities_sigmoid_rate_gg_1998 > rate_transformer_sigmoid_gg_1998;
void register_rate_transformer_sigmoid_gg_1998( const std::string& name );
 
 
template <>
void RecordablesMap< sigmoid_rate_gg_1998_ipn >::create();
template <>
void RecordablesMap< rate_transformer_sigmoid_gg_1998 >::create();
 
}  // namespace nest
 
 
#endif /* #ifndef SIGMOID_RATE_GG_1998_H */