CompLayer.H

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/*!@file ModelNeuron/CompLayer.H Class declarations for structure that
consists of an Gaussian excitatory layer which projects to an inhibitory layer,
which projects in a gaussian pattern pack to the excitatory layer. Inspired by
the layout of the mamillian SC. */

// //////////////////////////////////////////////////////////////////// //
// The iLab Neuromorphic Vision C++ Toolkit - Copyright (C) 2001 by the //
// University of Southern California (USC) and the iLab at USC.         //
// See http://iLab.usc.edu for information about this project.          //
// //////////////////////////////////////////////////////////////////// //
// Major portions of the iLab Neuromorphic Vision Toolkit are protected //
// under the U.S. patent ``Computation of Intrinsic Perceptual Saliency //
// in Visual Environments, and Applications'' by Christof Koch and      //
// Laurent Itti, California Institute of Technology, 2001 (patent       //
// pending; application number 09/912,225 filed July 23, 2001; see      //
// http://pair.uspto.gov/cgi-bin/final/home.pl for current status).     //
// //////////////////////////////////////////////////////////////////// //
// This file is part of the iLab Neuromorphic Vision C++ Toolkit.       //
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// The iLab Neuromorphic Vision C++ Toolkit is free software; you can   //
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// Primary maintainer for this file: David Berg <dberg@usc.edu>
// $HeadURL: svn://isvn.usc.edu:/software/invt/trunk/saliency/src/ModelNeuron/CompLayer.H $

#ifndef  NEURALMODEL_COMPLAYER_H_DEFINED
#define  NEURALMODEL_COMPLAYER_H_DEFINED

#include "ModelNeuron/Structure.H"
#include "ModelNeuron/Layer.H"
#include "ModelNeuron/Weights.H"

// ######################################################################
//  CompLayer
//  ######################################################################
// A class representing a single lamina containing excitatory and
// inhibitory neurons. A network containing interacting excitatory and
// inhhibitory units where an excitatory layer (input layer) has local
// recurrent excitation. Each unit in the exciatory layer connects
// directly to a single unit in the inhibitory layer. The inhibitory
// units connect in a gaussian pattern back to the excitatory
// network. A variety of behaviors can be obtained such as bumbs,
// waves, weak winner take all selection etc....
//
// It is hypothesized that each layer of the primate colliculus (Sgs,
// Sgi) supperior colliculus is organized in such a way, with
// different sized excitation and inhibition regions.
//
// Computational framework inspired by work from SI Amari. See SC
// class for citations on inspiration for the construction of a model
// Sgs and Sgi, and interlaminar connectivity.
//  ######################################################################
template <class EUnit, class IUnit>
struct CompLayer : public Structure<SimLayer*>
{
  //! constructor
  CompLayer(const double& excite_std, const double& inhibit_std,
            const double& excite_w, const double& feedforward, const double& feedback,
            const BorderPolicy bp, const SimTime& timestep, 
            const uint width, const uint height, const std::string name = "", const std::string units = "")
    : Structure<SimLayer*>(timestep, width, height, name, units),
      itsW(inhibit_std, feedback, false, bp), itsFF(feedforward) 
  { 
    Layer<EUnit, WeightsBinomial> itsE(excite_std, excite_w, true, bp, timestep, width, height, "Excitatory");
    Layer<IUnit, WeightsEmpty> itsI(timestep, width, height, "Inhibitory");
    addSub(itsE); 
    addSub(itsI);
    setDefaultIO(0,0);
  }
  
  //default copy and assignment OK
  
  //!destructor 
  virtual ~CompLayer() { };  
  
  //set the modules
  void setModules(const SimUnit& excite_module, const SimUnit& inhibit_module)
  {
    editSub(0).setModule(excite_module);
    editSub(1).setModule(inhibit_module);
  }
  
  //!get the current display output 
  Image<double> getDisplayOutput(const int pos = -1) const
  {
    if (pos < 0)
      {
        Image<double> e = getSub(0).getDisplayOutput();
        const Image<double> i = getSub(1).getDisplayOutput();
        e -= i;
        return e;
      }
    else
      return getSub(pos).getDisplayOutput();
  }

  //!cone the unit
  CompLayer<EUnit, IUnit>* clone() const {return new CompLayer<EUnit,IUnit>(*this); }
  
private:
  //!do our class specific interactions
  void interact()
  {

    //excite->inhibit
    Image<double> e = getSub(0).getOutput();
    e *= itsFF;
    input(e, 1);
    
    //inhbit->excite
    const Image<double> i = getSub(1).getOutput();
    const Image<double> iw = itsW.compute(i);
    input(iw, 0);
  }

  WeightsBinomial itsW;
  double itsFF;
};

#endif  
// ######################################################################
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