ginzburg_neuron.h

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/*
 *  ginzburg_neuron.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 GINZBURG_NEURON_H
#define GINZBURG_NEURON_H
 
// Includes from models:
#include "binary_neuron.h"
 
namespace nest
{
 
/* BeginUserDocs: neuron, binary
 
Short description
+++++++++++++++++
 
Binary stochastic neuron with sigmoidal activation function
 
Description
+++++++++++
 
The ``ginzburg_neuron`` is an implementation of a binary neuron that
is irregularly updated as Poisson time points. At each update
point, the total synaptic input h into the neuron is summed up,
passed through a gain function g whose output is interpreted as
the probability of the neuron to be in the active (1) state.
 
The gain function used here is :math:`g(h) = c_1 h + c_2 (1 +
\tanh(c_3 (h-\theta)))/2` (output clipped to :math:`[0, 1]`). This permits
affine-linear (:math:`c_1\neq0, c_2\neq0, c_3=0`) or sigmoidally shaped
(:math:`c_1=0, c_2=1, c_3\neq0`) gain functions. The latter choice
corresponds to the definition in :footcite:p:`Ginzburg1994`, giving the name to this
neuron model.
 
The choice :math:`c_1=0, c_2=1, c_3=\beta/2` corresponds to the Glauber
dynamics :footcite:p:`Hertz1991`, :math:`g(h) = 1 / (1 + \exp(-\beta (h-\theta)))`.
The time constant :math:`\tau_m` is defined as the mean
inter-update-interval that is drawn from an exponential
distribution with this parameter. Using this neuron to reproduce
simulations with asynchronous update :footcite:p:`Ginzburg1994`, the time constant needs
to be chosen as :math:`\tau_m = dt \times N`, where :math:`dt` is the simulation time
step and :math:`N` the number of neurons in the original simulation with
asynchronous update. This ensures that a neuron is updated on
average every :math:`\tau_m` ms. Since in the original paper :footcite:p:`Ginzburg1994` neurons
are coupled with zero delay, this implementation follows this
definition. It uses the update scheme described in :footcite:p:`Morrison2007b` to
maintain causality: The incoming events in time step :math:`t_i` are
taken into account at the beginning of the time step to calculate
the gain function and to decide upon a transition.  In order to
obtain delayed coupling with delay :math:`d`, the user has to specify the
delay :math:`d+h` upon connection, where :math:`h` is the simulation time step.
 
 
Parameters
++++++++++
 
====== ============= ===========================================================
tau_m  ms            Membrane time constant (mean inter-update-interval)
theta  mV            Threshold for sigmoidal activation function
c_1    probability/  Linear gain factor
       mV
c_2    probability   Prefactor of sigmoidal gain
c_3    1/mV          Slope factor of sigmoidal gain
====== ============= ===========================================================
 
.. admonition:: Special requirements for binary neurons
 
   As the ``ginzburg_neuron`` is a binary neuron, the user must
   ensure that the following requirements are observed. NEST does not
   enforce them. Breaching the requirements can lead to meaningless
   results.
 
   1. Binary neurons must only be connected to other binary neurons.
 
   #. No more than connection must be created between any pair of
      binary neurons. When using probabilistic connection rules, specify
      ``'allow_autapses': False`` to avoid accidental creation of
      multiple connections between a pair of neurons.
 
   #. Binary neurons can be driven by current-injecting devices, but
      *not* by spike generators.
 
   #. Activity of binary neurons can only be recored using a ``spin_detector``
      or ``correlospinmatrix_detector``.
 
 
References
++++++++++
 
.. footbibliography::
 
Receives
++++++++
 
CurrentEvent
 
See also
++++++++
 
 
Examples using this model
+++++++++++++++++++++++++
 
.. listexamples:: ginzburg_neuron
 
EndUserDocs */
 
class gainfunction_ginzburg
{
private:
  double theta_;
 
  double c1_;
 
  double c2_;
 
  double c3_;
 
public:
  gainfunction_ginzburg()
    : theta_( 0.0 )  // mV
    , c1_( 0.0 )     // (mV)^-1
    , c2_( 1.0 )     // dimensionless
    , c3_( 1.0 )     // (mV)^-1
  {
  }
 
  void get( Dictionary& ) const;              
  void set( const Dictionary&, Node* node );  
 
  bool operator()( RngPtr rng, double h ) const;
};
 
inline bool
gainfunction_ginzburg::operator()( RngPtr rng, double h ) const
{
  return rng->drand() < c1_ * h + c2_ * 0.5 * ( 1.0 + tanh( c3_ * ( h - theta_ ) ) );
}
 
typedef binary_neuron< gainfunction_ginzburg > ginzburg_neuron;
void register_ginzburg_neuron( const std::string& name );
 
 
template <>
void RecordablesMap< ginzburg_neuron >::create();
 
}  // namespace nest
 
 
#endif /* #ifndef GINZBURG_NEURON_H */