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

#include "binary_neuron.h"
#include "binary_neuron_impl.h"

namespace nest
{
  /* BeginDocumentation
     Name: ginzburg_neuron - 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 g used here is g(h) = c1*h + c2 * 0.5*(1 +
     tanh(c3*(h-theta))) (output clipped to [0,1]). This allows to
     obtain affin-linear (c1!=0, c2!=0, c3=0) or sigmoidal (c1=0,
     c2=1, c3!=0) shaped gain functions.  The latter choice
     corresponds to the definition in [1], giving the name to this
     neuron model.
     The choice c1=0, c2=1, c3=beta/2 corresponds to the Glauber
     dynamics [2], g(h) = 1 / (1 + exp(-beta (h-theta))).
     The time constant tau_m is defined as the mean
     inter-update-interval that is drawn from an exponential
     distribution with this parameter. Using this neuron to reprodce
     simulations with asynchronous update [1], the time constant needs
     to be chosen as tau_m = dt*N, where dt is the simulation time
     step and N the number of neurons in the original simulation with
     asynchronous update. This ensures that a neuron is updated on
     average every tau_m ms. Since in the original paper [1] neurons
     are coupled with zero delay, this implementation follows this
     definition. It uses the update scheme described in [3] to
     maintain causality: The incoming events in time step 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 d, the user has to specify the
     delay d+h upon connection, where h is the simulation time step.
     
     Remarks:
     This neuron has a special use for spike events to convey the
     binary state of the neuron to the target. The neuron model
     only sends a spike if a transition of its state occurs. If the
     state makes an up-transition it sends a spike with multiplicity 2,
     if a down transition occurs, it sends a spike with multiplicity 1.
     The neuron accepts several sources of currents, e.g. from a
     noise_generator.
     
     Parameters:
     tau_m      double - Membrane time constant (mean inter-update-interval) in ms.
     theta      double - threshold for sigmoidal activation function mV
     c1         double - linear gain factor (probability/mV)
     c2         double - prefactor of sigmoidal gain (probability)
     c3         double - slope factor of sigmoidal gain (1/mV)
 
     References:
     [1] Iris Ginzburg, Haim Sompolinsky. Theory of correlations in stochastic neural networks (1994). PRE 50(4) p. 3171
     [2] Hertz Krogh, Palmer. Introduction to the theory of neural computation. Westview (1991).
     [3] Abigail Morrison, Markus Diesmann. Maintaining Causality in Discrete Time Neuronal Simulations.
     In: Lectures in Supercomputational Neuroscience, p. 267. Peter beim Graben, Changsong Zhou, Marco Thiel, Juergen Kurths (Eds.), Springer 2008.

     Sends: SpikeEvent
     Receives: SpikeEvent, PotentialRequest
     FirstVersion: February 2010
     Author: Moritz Helias
     SeeAlso: pp_psc_delta
  */

  class gainfunction_ginzburg
  {
  private:
    
    double_t theta_;
  
    double_t c1_;
  
    double_t c2_;
  
    double_t c3_;
  
  public:
    gainfunction_ginzburg() :
            theta_ ( 0.0 ),   // mV
            c1_    ( 0.0 ),   // (mV)^-1
            c2_    ( 1.0 ),   // dimensionless
            c3_    ( 1.0 )    // (mV)^-1 
    {}
  
    void get(DictionaryDatum&) const;  
    void set(const DictionaryDatum&);  
    
    bool operator()(librandom::RngPtr rng, double_t h);
  };
  
  inline
  bool gainfunction_ginzburg::operator()(librandom::RngPtr rng, double_t h)
  {
    return rng->drand() <  c1_ * h + c2_ * 0.5 * (1.0 + tanh( c3_ * (h - theta_) ));
  }

  typedef binary_neuron<nest::gainfunction_ginzburg> ginzburg_neuron;

  template <>
  void RecordablesMap< ginzburg_neuron >::create();

} // namespace nest



#endif /* #ifndef GINZBURG_NEURON_H */