mcculloch_pitts_neuron.h

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/*
 *  mcculloch_pitts_neuron.h
 *
 *  This file is part of NEST.
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 *  Copyright (C) 2004 The NEST Initiative
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#ifndef MCCULLOCH_PITTS_NEURON_H
#define MCCULLOCH_PITTS_NEURON_H

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

namespace nest
{
  /* BeginDocumentation
     Name: mcculloch_pitts_neuron - Binary deterministic neuron with Heaviside activation function.

     Description:
     The mcculloch_pitts_neuron is an implementation of a binary
     neuron that is irregularly updated as Poisson time points [1]. 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) = H(h-theta), with H the
     Heaviside function.  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
 
     References:
     [1] W. McCulloch und W. Pitts (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5:115-133.
     [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 2013
     Author: Moritz Helias
     SeeAlso: pp_psc_delta
  */

  class gainfunction_mcculloch_pitts
  {
  private:
  
    double_t theta_;

  public:
  
    gainfunction_mcculloch_pitts() :
            theta_ ( 0.0 )   // mV
    {}

    void get(DictionaryDatum&) const;  
    void set(const DictionaryDatum&);  

    bool operator()(librandom::RngPtr, double_t h);
  };

  inline
  bool gainfunction_mcculloch_pitts::operator()(librandom::RngPtr, double_t h)
  {
    return h > theta_;
  }

  typedef nest::binary_neuron<nest::gainfunction_mcculloch_pitts> mcculloch_pitts_neuron;

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

} // namespace nest

#endif /* #ifndef MCCULLOCH_PITTS_NEURON_H */