ObjSim/src/vlearn.hpp

#ifndef _HPP_VECLEARN
#define _HPP_VECLEARN

#include "libcsim.hpp"
#include "layer.hpp"
#include "vconnection.hpp"

class veclearning : public SimElement
{
protected:
    layer  *TargetLayer,  *SourceLayer;
    int ns, nt;  // target and source layer dimensions
    VecConnection *con;
    float maxWeight, minWeight;
    int Dmax;
    int maximumDelay;
    vector<float> *PSynWeights;
    vector<T_NNeurons>* PSynSourceNr;
    vector<T_NNeurons>* PSynTargetNr;
    vector<T_Delays>* PSynDelays;
    vector<vector<T_NSynapses> >* PPreSynNr;
    vector<vector<vector<T_NSynapses> > >* Pdelays;
public:
    veclearning(VecConnection* con, float _maxWeight=1);
    ~veclearning();
    virtual int proceede(int)=0;
    virtual int prepare(int =0)=0;
//      virtual void SetMinWeight(float value);  // removed extra qualification learning::
//      virtual void SetMaxWeight(float value);
};



class VecLearnHebbLP2: public veclearning
{
protected: 
     float **LTP;
     float LtpDecFac;
     float LtpInc;
     bool Accumulate;
     float LearnSpeed;
     float BaseLine;
public:
     VecLearnHebbLP2(VecConnection* con, float _MaxWeight=1, float TauDec=20, float BaseLine=0.1, float _LearnSpeed=0.001, bool _Accumulate=true);
     ~VecLearnHebbLP2();
     virtual int proceede(int =0);
     virtual int prepare(int =0);     
     virtual int WriteSimInfo(fstream &fw);
};



class VecLearnPrePost: public veclearning
{
protected:
     float **LPpre; //presynaptic potentials
     float *LPpost;     // postsynaptic potentials
     float dec_pre, dec_post;   // decay of presynaptic and postsynaptic learning potential  
public:
     VecLearnPrePost(VecConnection* con, float _maxWeight=1, float TauLearnPre=30, float TauLearnPost=30);
     ~VecLearnPrePost();
     virtual int proceede(int =0)=0;
     virtual int prepare(int =0);     
};


// wie LearnHebbLP2, aber mit st�rkerer Gewichts�nderung bei hoher 
// postsynaptischer Rate (d.h. Multiplikation der lernrate mit dem Postsynaptischen Spike Trace)
// daher auch von LearnPrePost abgeleitet
class VecLearnHebbLP3: public VecLearnPrePost
{
protected:
     float LearnRate, BaseLine;
     float LtpInc;
     bool Accumulate;
public:
     VecLearnHebbLP3(VecConnection* con, float _maxWeight=1, float TauLearnPre=30, float TauLearnPost=30, float _BaseLine=0.2, float _LearnRate=0.0001, bool _Accumulate=false);
     virtual int proceede(int =0);
};


#endif /*_HPP_VECLEARN */