Connection.cpp

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/************************************************************************
* Verve                                                                 *
* Copyright (C) 2004-2006                                               *
* Tyler Streeter  tylerstreeter@gmail.com                               *
* All rights reserved.                                                  *
* Web: http://verve-agents.sourceforge.net                              *
*                                                                       *
* This library is free software; you can redistribute it and/or         *
* modify it under the terms of EITHER:                                  *
*   (1) The GNU Lesser General Public License as published by the Free  *
*       Software Foundation; either version 2.1 of the License, or (at  *
*       your option) any later version. The text of the GNU Lesser      *
*       General Public License is included with this library in the     *
*       file license-LGPL.txt.                                          *
*   (2) The BSD-style license that is included with this library in     *
*       the file license-BSD.txt.                                       *
*                                                                       *
* This library 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 files    *
* license-LGPL.txt and license-BSD.txt for more details.                *
************************************************************************/

#include "Connection.h"
#include "Neuron.h"

namespace verve
{
        Connection::Connection(Neuron* preNeuron, Neuron* postNeuron)
        {
                assert(preNeuron && postNeuron);

                // Note: We don't have to update the post-synaptic Neuron's input 
                // sum here because the weight is intially zero, so it doesn't 
                // affect anything.

                mPreNeuron = preNeuron;
                mPostNeuron = postNeuron;
                mWeight = 0;
        }

        Connection::~Connection()
        {
        }

        void Connection::resetShortTermMemory()
        {
        }

        real Connection::getWeight()const
        {
                return mWeight;
        }

        void Connection::setWeight(real value)
        {
                // If nothing has changed, quit early to save time.
                if (value == mWeight)
                {
                        return;
                }

                real deltaWeight = value - mWeight;
                mWeight = value;

                // If the pre-synaptic Neuron is active, then when need to update 
                // the post-synaptic Neuron's input sum.
                real preFiringRate = mPreNeuron->getFiringRate();
                if (0 != preFiringRate)
                {
                        mPostNeuron->addToSynapticInputSum(
                                deltaWeight * preFiringRate);
                }
        }

        void Connection::addToWeight(real delta)
        {
                // If nothing has changed, quit early to save time.
                if (0 == delta)
                {
                        return;
                }

                mWeight += delta;

                // If the pre-synaptic Neuron is active, then when need to update 
                // the post-synaptic Neuron's input sum.
                real preFiringRate = mPreNeuron->getFiringRate();
                if (0 != preFiringRate)
                {
                        mPostNeuron->addToSynapticInputSum(
                                delta * preFiringRate);
                }
        }

        Neuron* Connection::getPreNeuron()const
        {
                return mPreNeuron;
        }

        Neuron* Connection::getPostNeuron()const
        {
                return mPostNeuron;
        }

        //void Connection::applyIdealInitialNoise()
        //{
        //      // Applies Gaussian noise to the Connection weights with standard 
        //      // deviation equal to the inverse of the square root of the number 
        //      // of dendrites connected to the post-synaptic Neuron.  See Remi 
        //      // Coulom's PhD thesis for the reasoning.

        //      // We calculate the variance instead of standard deviation 
        //      // since that's what the random function takes as an argument.  
        //      // The standard deviation here should be 
        //      // 1 / sqrt(# of post-synaptic Neuron dendrites).  Since the 
        //      // variance equals the standard deviation squared, we can 
        //      // simply use 1 / # of post-synaptic Neuron dendrites.

        //      // A Connection's post-synaptic Neuron will always have at least 
        //      // one dendrite (i.e. the Connection in question), so we're safe 
        //      // from divide by zero errors here.
        //      real variance = 1 / (real)(mPostNeuron->getNumDendrites());
        //      addToWeight(globals::randomRealGauss(variance));
        //}
}

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