#ifndef _HPP_LAYER
#define _HPP_LAYER

#include <vector>

#include "simelement.hpp"
#include "libcsim.hpp"



enum csimNeuronType {
    csimNType_Izhikevich5,
    csimNType_Izhikevich7,
    csimNType_Izhikevich8,
    csimNType_Izhikevich8_integrator,
    csimNType_Izhikevich9,
    csimNType_MMN02,
    csimNType_DecoLif,

// add new layer types above here
    csimNumberNTypes
};


class SpikeTrain;

/*! \brief Klasse layer
 *         von layer stammen alle layer-Klassen ab
 *
 *  Detailed description starts here.
 */

class layer : public SimElement
{
protected:
    float *v, *u;
    std::string SpikeFileName, FileType, MemPotFileName;
    bool Binary;
    float RandomSpikeFrequency;
    int RestProb, nris;
    float NumRandomInputSpikes;
    float RandomInputStrength;
    float NoiseSigma;
    double PoissonLambda;
    float NoiseAmplitude;
    float BalancedInhibition;
    void ThrowTooManySpikes(int time);
    bool TooManySpikes;
private:
    SpikeTrain* mSpikeTrain;
public:
    T_NNeurons N;
    T_NNeurons Nx, Ny;  // numbers of raws and collumns for 2d layer
    T_NNeurons NNeurons() {
        return N;
    };
    T_NNeurons NeuronsNx() {
        return Nx;
    };
    T_NNeurons NeuronsNy() {
        return Ny;
    };
    vector2d *Pos;
    vector<vector2d> mPositions;
    bool NormPos;
    int N_firings;              // the number of fired neurons
    int last_N_firings;
    int **firings;
    int N_firings_max;
    int Dmax;
    float *Input; // input current
    
    layer (int n=1000);
    virtual ~layer();
    virtual void hallo();
    virtual int proceede(int);
    virtual int prepare(int =0);
    virtual int reset(int);
    virtual int SetupPositions();
    virtual int SetupPositions(int _nx, int ny, bool Normalize=false);
    virtual int SetupPositionsShift(int _nx, int ny, float xshift,float yshift, bool Normalize=false);
    virtual int SetupPositionsLinDiscrete(int NeuronsPerPatch);
    const vector<vector2d>& getPositions();
    bool isPositionNormalized() {return NormPos;}
    virtual void SetName(const char*);
    void SetSpikeFileName(char*);
    void SetRandomInputStrength(float);
    void SetRandomSpikeFrequency(float);
    void SetNoiseSigma(float);
    void SetBalancedInhibition(float);
    void SetNoiseAmplitude(float);
    int TuneNoiseAmplitude(float DesiredFrequency);
    int TuneBalancedInhibition(float DesiredFrequency);
    virtual float* GetInputPointer(csimInputChannel);
    virtual float* GetPspPointer(csimInputChannel);
    virtual int StartBinRec(int n, int StartNumber=0);
    virtual long calcMemoryConsumption();
    
    float GetDt();
    virtual int SaveSimInfo();
    virtual int WriteSimInfo(fstream &fw);
    virtual int WriteSimInfo(fstream &fw, const string &ChildInfo);
    virtual SpikeTrain* GetSpikeTrain();
};


struct LevyShuntingParas {
    LevyShuntingParas(float _k0, float _k1, float _k2, float _kffi, float _kfbi)
        : K0(_k0), K1(_k1), K2(_k2), Kffi(_kffi), Kfbi(_kfbi)
    {}
    float K0,K1, K2, Kffi, Kfbi;
} ;

static const LevyShuntingParas DefaultShunt (1,4,4,1,1);


class liflayer : public layer
{
protected:
    float TauM;  // membrane time constant
    float TauMfac;
    float Threshold;
    float K0,K1, K2, Kffi, Kfbi; // inhibition parameters
    float InputSaturation;
    float TauS; // synaptic time constant
    float TauSfac;
    int *LastSpike;
    float* mquer;  // number of spikes in last ms
    float* squer;
    float RunAvgFac;
    float Iinh;
    int InhibitionDelay; // in timesteps =(ms/dt)
    int RefractoryPeriod;
public:
    liflayer(
        int n,
        float tau=20, float thresh=0.0033, float k0=1,
        float k1=4, float k2=4, float kffi=1, float kfbi=1,
        float taus=2, float _InputSat=1.0, float _NoiseSigma=0);
    ~liflayer();
    virtual int proceede(int);
    virtual int prepare(int =0);
    virtual float* GetInputPointer(csimInputChannel num=csimInputChannel_AMPA);
    int InitInhibitionPot(float _mquer, float _squer);
    int SetKfbi(float);
    float *Input1;
};

//  leaky integrate and fire neuron with theta rhythmic deactivation of recurrent input
class thetaliflayer: public liflayer
{
protected:
    int ThetaPeriod;  // in ms
    int ThetaPhase;
    bool ThetaOn;
public:
    thetaliflayer(
        int n,
        float tau=20, float thresh=0.0033, float k0=1,
        float k1=4, float k2=4, float kffi=1, float kfbi=1,
        float taus=2, float _InputSat=1.0, float _NoiseSigma=0,
        int _ThetaPeriod=100);
    ~thetaliflayer();
    virtual int proceede(int);
    virtual int prepare(int =0);
    void SetThetaOn(bool status);
};


// leaky integrate and fire neuron with adaptive synapses to inhibitory interneuron
class lif2layer: public liflayer
{
private:
    float *Dinh;
    float DesiredActivity;
    float *InhibitoryActivity;
    float PyrToInhLearningRate;
    bool learnpti;
    float TauInhLearn;
    float InhLearnFac;
public:
    lif2layer(int, float =20, float =0.0033, float =1, float =4, float=4, float=1, float=1, float =2, float =0.012, float =0.1);
    ~lif2layer();
    virtual int proceede(int);
    virtual int prepare(int =0);
    int SetLearnPti(bool);
};


struct IzhParas {
    IzhParas(float a, float b, float c, float d, float e, float f, float sigma=0): A(a), B(b), C(c), D(d), E(e), F(f) {}
    float A, B, C, D, E, F, sigma;
    int print();
};


static const IzhParas IzhParaRegularSpiking       (0.02, 0.2 , -65, 8.  , 140, 5. , 0.0);
static const IzhParas IzhParaIntrinsicallyBursting(0.02, 0.2 , -55, 4.  , 140, 5. , 0.0);
static const IzhParas IzhParaChattering           (0.02, 0.2 , -50, 2.  , 140, 5. , 0.0);
static const IzhParas IzhParaFastSpiking          (0.1 , 0.2 , -65, 2.  , 140, 5. , 0.0);
static const IzhParas IzhParaLowThreshold         (0.02, 0.25, -65, 2.  , 140, 5. , 0.0);
static const IzhParas IzhParaThalamoCortical      (0.02, 0.25, -65, 0.05, 140, 5. , 0.0);
static const IzhParas IzhParaResonator            (0.1 , 0.26, -65, 2.  , 140, 5. , 0.0);
static const IzhParas IzhParaIntegrator           (0.02, -0.1, -55, 6.  , 108, 4.1, 0.0);
static const IzhParas IzhParaIntegrator2          (0.02, -0.1, -55, 6.  , 110, 4.1, 0.0);
static const IzhParas IzhParaClass2               (0.2 , 0.26, -65, 0.  , 139, 5. , 0.0);
static const IzhParas IzhLowDIntegrator           (0.02, -0.1, -55, 2.  , 108, 4.1, 0.0);

enum IzhType {Integrator, FastSpiking};

class izhlayer : public layer
{
protected:
    float A, B, C, D, E, F;   //first version: uniform dynamics, same parameters for every neuron
    // of the layer; later a,b,c... have to be pointers
    float TauS; // synaptic time constant
    float TauSfac;
    float TauInh, TauInhFac; // inhibitory synaptic time constant
    float EquilibriumPot;
    int InitEquilibriumPotentials();
    float *ExSynPot; // excitatory synaptic potential
//     float *InhInput; // Inhibitory Input
    float *InhSynPot; // ShuntingInhibitionPotential
    float InputSaturation;
    float InhInputSaturation;
    int Init();


public:
    izhlayer(int n=1000, float  a=0.02, float b=-0.1, float c=-55, float  d=4, float  e=108, float  f=4.1, float _InputSat=1, float _InhInputSat=1);
    izhlayer(int n=1000, IzhParas _paras=IzhParaIntegrator, float _InputSat=1, float _InhInputSat=1);
    izhlayer(IzhType type, int n=1000, float _InputSat=30, float _InhInputSat=1);
    ~izhlayer();
    virtual int proceede(int);
    virtual float* GetInputPointer(csimInputChannel num=csimInputChannel_AMPA);
    virtual int StartBinRec(int n, int StartNumber=0);
    virtual int SetTauInh(float _TauInh);
    virtual int SetTauEx(float _TauEx);
    virtual int WriteSimInfo(fstream &fw);
    virtual int WriteSimInfo(fstream &fw, const string &ChildInfo);
};

// inhibitory potential is also subtracted from membrane potential v
class izh2layer : public izhlayer
{
protected:
public:
    izh2layer(int n=1000, float  a=0.02, float b=-0.1, float c=-55, float  d=4, float  e=108, float  f=4.1, float _InputSat=1);
    izh2layer(int n=1000, IzhParas _paras=IzhParaIntegrator, float _InputSat=1);
    ~izh2layer();
    virtual int proceede(int);

};

// conductance based inhibition, noise is addet to excitatory input
class izh3layer : public izhlayer
{
protected:
    float InhReversePot;
public:
    float *InhInput; // inhibitory input current
    izh3layer(int n=1000, float  a=0.02, float b=-0.1, float c=-55, float  d=4, float  e=108, float  f=4.1, float _InputSat=1, float IRPotDiff=10, float _InhInputSaturation=1);
    izh3layer(int n=1000, IzhParas _paras=IzhParaIntegrator, float _InputSat=1, float IRPotDiff=10, float _InhInputSaturation=1);
    ~izh3layer();
    virtual int proceede(int);
    virtual float* GetInputPointer(csimInputChannel InputNumber=csimInputChannel_AMPA);
};

// conductance based inhibition, noise is addet to excitatory input
// plus linking input
class izh4layer : public izh3layer
{
protected:
    float TauLink, TauLinkFac;
    float *LinkSynPot; // LinkingInputPotential
public:
    izh4layer(int n=1000, IzhParas _paras=IzhParaIntegrator, float _InputSat=1, float IRPotDiff=10, float _TauLink=10);
    ~izh4layer();
    virtual int proceede(int);
    virtual float* GetInputPointer(csimInputChannel num=csimInputChannel_AMPA);
};

// my currend STANDARD IZH-Neuron
// conductance based inhibition, noise is addet to excitatory input
// like izh3layer, but input saturation occurs before adding synaptic
// input to the membrane potential and not before adding input to
// synaptic potential.
class izh5layer : public izhlayer
{
protected:
    float InhReversePot;
public:
    izh5layer(int n=1000, float  a=0.02, float b=-0.1, float c=-55, float  d=4, float  e=108, float  f=4.1, float _InputSat=200, float IRPotDiff=10, float _InhInputSaturation=4.5);
    izh5layer(int n=1000, IzhParas _paras=IzhParaIntegrator, float _InputSat=200, float IRPotDiff=10, float _InhInputSaturation=4.5);
    ~izh5layer();
    virtual int proceede(int);
    virtual float* GetInputPointer(csimInputChannel num=csimInputChannel_AMPA);
};



// like izh5layer,
// but with additionall NMDA conductance
class izh6layer : public izhlayer
{
protected:
    float InhReversePot;
    float *NmdaSynPot; // NMDA synaptic potential
    float NmdaSaturation;
    float TauNmda, TauNmdaFac;
    float NmdaAmpaRatio;
public:
    izh6layer(int n=1000, IzhParas _paras=IzhParaIntegrator, float _InputSat=200, float IRPotDiff=10, float _InhInputSaturation=4.5, float _NmdaInputSaturation=10);
    ~izh6layer();
    virtual int proceede(int);
    virtual float* GetInputPointer(csimInputChannel num=csimInputChannel_AMPA);
    virtual void SetNmdaAmpaRatio(float ratio);
    virtual void SetTauNmda(float value);
    virtual int WriteSimInfo(fstream &fw);
    virtual int StartBinRec(int n, int StartNumber=0);
};


inline double DoubleExpCorrectionFactor(double Tau1, double Tau2)
{
    double PeakTime = log(Tau2/Tau1)/(1/Tau1 - 1/Tau2);
    return 1.0/(exp(-PeakTime/Tau2)-exp(-PeakTime/Tau1));
}


// like izh6layer,
// but with double exponential PSPs (with rise and fall time constants)
class izh7layer : public izhlayer
{
protected:
    float InhReversePot;
    float *NmdaRisePot; // NMDA synaptic potential
    float *NmdaFallPot; // NMDA synaptic potential
    float *AmpaRisePot; // AMPA synaptic potential
    float *AmpaFallPot; // AMPA synaptic potential
    float *GabaRisePot; // GABA synaptic potential
    float *GabaFallPot; // GABA synaptic potential
    float *InhInput;
    float *NmdaAmpaInput;
    float NmdaSaturation;
    float TauAmpaRise, TauAmpaFall, TauAmpaRiseFac, TauAmpaFallFac;
    float TauGabaRise, TauGabaFall, TauGabaRiseFac, TauGabaFallFac;
    float TauNmdaRise, TauNmdaFall, TauNmdaRiseFac, TauNmdaFallFac;
    float AmpaCorr, GabaCorr, NmdaCorr;
    float NmdaAmpaRatio;
public:
    izh7layer(int n=1000, IzhParas _paras=IzhParaIntegrator, float _InputSat=200, float IRPotDiff=10, float _InhInputSaturation=4.5, float _NmdaInputSaturation=10);
    ~izh7layer();
    virtual int proceede(int);
    virtual float* GetInputPointer(csimInputChannel num=csimInputChannel_AMPA);
    virtual void SetNmdaAmpaRatio(float ratio);
    virtual void SetTauNmda(float rise, float fall);
    virtual void SetTauAmpa(float rise, float fall);
    virtual void SetTauGaba(float rise, float fall);
    virtual int WriteSimInfo(fstream &fw);
    virtual int StartBinRec(int n, int StartNumber=0);
};



// like izh6layer,
// but with double exponential PSPs (with rise and fall time constants)
// and NO input saturation
class izh8layer : public izhlayer
{
protected:
    float InhReversePot;
    float *NmdaRisePot; // NMDA synaptic potential
    float *NmdaFallPot; // NMDA synaptic potential
    float *AmpaRisePot; // AMPA synaptic potential
    float *AmpaFallPot; // AMPA synaptic potential
    float *AmpaPot; // AMPA synaptic potential (fall-rise)
    float *GabaRisePot; // GABA synaptic potential
    float *GabaFallPot; // GABA synaptic potential
    float *InhInput;
    float *NmdaAmpaInput;
    float TauAmpaRise, TauAmpaFall, TauAmpaRiseFac, TauAmpaFallFac;
    float TauGabaRise, TauGabaFall, TauGabaRiseFac, TauGabaFallFac;
    float TauNmdaRise, TauNmdaFall, TauNmdaRiseFac, TauNmdaFallFac;
    float AmpaCorr, GabaCorr, NmdaCorr;
    float NmdaAmpaRatio;
public:
    izh8layer(int n=1000, IzhParas _paras=IzhParaIntegrator, float IRPotDiff=10);
    ~izh8layer();
    virtual int proceede(int);
    virtual float* GetInputPointer(csimInputChannel num=csimInputChannel_AMPA);
    virtual float* GetPspPointer(csimInputChannel);
    virtual void SetNmdaAmpaRatio(float ratio);
    virtual void SetTauNmda(float rise, float fall);
    virtual void SetTauAmpa(float rise, float fall);
    virtual void SetTauGaba(float rise, float fall);
    virtual int WriteSimInfo(fstream &fw);
    virtual int StartBinRec(int n, int StartNumber=0);
};



// like izh8layer,
// but with GabaA and GabaB inhibition
class izh9layer : public izhlayer
{
protected:
    float InhReversePot;
    float *NmdaRisePot; // NMDA synaptic potential
    float *NmdaFallPot; // NMDA synaptic potential
    float *AmpaRisePot; // AMPA synaptic potential
    float *AmpaFallPot; // AMPA synaptic potential
    float *AmpaPot; // AMPA synaptic potential (fall-rise)
    float *GabaARisePot; // GABA synaptic potential
    float *GabaAFallPot; // GABA synaptic potential
    float *GabaBRisePot; // GABA synaptic potential
    float *GabaBFallPot; // GABA synaptic potential
    float *InhInput;
    float *NmdaAmpaInput;
    float NmdaSaturation;
    float TauAmpaRise, TauAmpaFall, TauAmpaRiseFac, TauAmpaFallFac;
    float TauGabaARise, TauGabaAFall, TauGabaARiseFac, TauGabaAFallFac;
    float TauGabaBRise, TauGabaBFall, TauGabaBRiseFac, TauGabaBFallFac;
    float TauNmdaRise, TauNmdaFall, TauNmdaRiseFac, TauNmdaFallFac;
    float AmpaCorr, GabaACorr, GabaBCorr, NmdaCorr;
    float NmdaAmpaRatio;
    float GabaABRatio;
    float GabaBReversePot;
public:
    izh9layer(int n=1000, IzhParas _paras=IzhParaIntegrator, float GabaARPotDiff=0, float GabaBRPotDiff=30);
    ~izh9layer();
    virtual int proceede(int);
    virtual float* GetInputPointer(csimInputChannel num=csimInputChannel_AMPA);
    virtual float* GetPspPointer(csimInputChannel);
    virtual void SetNmdaAmpaRatio(float ratio);
    virtual void SetGabaABRatio(float ratio);
    virtual void SetTauNmda(float rise, float fall);
    virtual void SetTauAmpa(float rise, float fall);
    virtual void SetTauGabaA(float rise, float fall);
    virtual void SetTauGabaB(float rise, float fall);
    virtual int WriteSimInfo(fstream &fw);
    virtual int StartBinRec(int n, int StartNumber=0);
};




// izhikevich neuron layer with levy like global shunting inhibition

class izhshuntlayer : public izhlayer
{
protected:
    LevyShuntingParas inh; // inhibition parameters
    /*      float TauS; // synaptic time constant */
    /*      float TauSfac; */
//     int *LastSpike;
    float* mquer;  // running average of last spikes
    float* squer;   // input running avarage
    float RunAvgFac;
    float Iinh;
    int InhibitionDelay; // in timesteps =(ms/dt)
    float NoiseSigma;
    float *Input1;
public:
    izhshuntlayer(
        int n=1000, IzhParas _paras=IzhParaIntegrator,
        LevyShuntingParas _shuntparas= DefaultShunt,
        float _InputSat=60.0, float _NoiseSigma=0);
    ~izhshuntlayer();
    virtual int proceede(int);
    virtual int prepare(int step);
    virtual float* GetInputPointer(csimInputChannel num=csimInputChannel_AMPA);
    int SetKfbi(float);
};

// izhkevich neuron layer with levy like global shunting inhibition
// and presynaptic inhibition of recurrent excitation
class PresynIzhShuntLayer: public izhshuntlayer
{
protected:
    float *PresynInhInput; // input nr 2
public:
    PresynIzhShuntLayer(
        int n=1000, IzhParas _paras=IzhParaIntegrator,
        LevyShuntingParas _shuntparas= DefaultShunt,
        float _InputSat=60.0, float _NoiseSigma=0);
    ~PresynIzhShuntLayer();
    virtual float* GetInputPointer(csimInputChannel num=csimInputChannel_AMPA);
    virtual int proceede(int TotalTime);

};


class MMN01Layer: public layer
{
protected:
    float TauFeeding, FeedingFac;
    float TauThreshold, ThresholdFac;
    float RestingThreshold, ThresInc;
    float *inh;
public:
    MMN01Layer(int n=1000, float _TauThres=10,
               float _RestingThres=1, float _ThresInc=1,
               float _TauFeeding=10);
    ~MMN01Layer();
    virtual int proceede(int TotalTime);
    virtual float* GetInputPointer(csimInputChannel);
    virtual int StartBinRec(int n, int StartNumber=0);
};

// mit Inhibitorischem Potential
class MMN02Layer: public MMN01Layer
{
protected:
    float TauInhibition, InhibitionFac;
public:
    MMN02Layer(int n=1000, float _TauThres=10,
               float _RestingThres=1, float _ThresInc=1,
               float _TauFeeding=10, float _TauInhibition=10);
    ~MMN02Layer();
    virtual int proceede(int TotalTime);
    virtual float* GetInputPointer(csimInputChannel);
    virtual int StartBinRec(int n, int StartNumber=0);
};

// mit Linking-Potential
class MMN03Layer: public MMN02Layer
{
protected:
    float TauLinking, LinkingFac;
    float *Linking;
public:
    MMN03Layer(int n=1000, float _TauThres=10,
               float _RestingThres=1, float _ThresInc=1,
               float _TauFeeding=2.4, float _TauInhibition=7, float _TauLinking=10);
    ~MMN03Layer();
    virtual int proceede(int TotalTime);
    virtual float* GetInputPointer(csimInputChannel);
    virtual int StartBinRec(int n, int StartNumber=0);
    virtual int reset(int t);
    virtual int WriteSimInfo(fstream &fw);
};

class MMN04Layer: public MMN03Layer
{
protected:
    float TauEnduFeeding, EnduFeedingFac;
    float TauEnduLinking, EnduLinkingFac;
    float *endu;
    float *enduL;
    float *v_Self;
public:
    MMN04Layer(int n=1000, float _TauThres=10,
               float _RestingThres=1, float _ThresInc=1,
               float _TauFeeding=2.4, float _TauInhibition=7, float _TauLinking=2.4, float _TauEnduFeeding=100, float _TauEnduLinking=100);
    ~MMN04Layer();
    virtual int proceede(int TotalTime);
    virtual float* GetInputPointer(csimInputChannel);
    virtual int StartBinRec(int n, int StartNumber=0);
    virtual int reset(int t);
    virtual int WriteSimInfo(fstream &fw);
};


class MMN05Layer: public MMN03Layer
{
protected:
    float TauEnduFeeding, EnduFeedingFac;
    float TauEnduLinking, EnduLinkingFac;
    float *endu;
    float *enduL;
    float *v_Self;
public:
    MMN05Layer(int n=1000, float _TauThres=10,
               float _RestingThres=1, float _ThresInc=1,
               float _TauFeeding=2.4, float _TauInhibition=7, float _TauLinking=2.4, float _TauEnduFeeding=100, float _TauEnduLinking=100);
    ~MMN05Layer();
    virtual int proceede(int TotalTime);
    virtual float* GetInputPointer(csimInputChannel);
    virtual int StartBinRec(int n, int StartNumber=0);
    virtual int reset(int t);
    virtual int WriteSimInfo(fstream &fw);
};



/*! \brief Klasse  AmpaNmdaGabaChannels
 *         kuemmert sich um alle Arrays fuer NMDA, AMPA, GABAa und GABAb-Synapsen
 *
 *  Detailed description starts here.
 */
class AmpaNmdaGabaChannels
{
private:
    float DeltaT;
    SimLoop* MainSimLoop;
    int NNeurons;
protected:
    float *NmdaRisePot; // NMDA synaptic potential
    float *NmdaFallPot; // NMDA synaptic potential
    float *AmpaRisePot; // AMPA synaptic potential
    float *AmpaFallPot; // AMPA synaptic potential
    float *AmpaPot; // AMPA synaptic potential (fall-rise)
    float *GabaARisePot; // GABAa synaptic potential
    float *GabaAFallPot; // GABAa synaptic potential
    float *GabaBRisePot; // GABAb synaptic potential
    float *GabaBFallPot; // GABAb synaptic potential

    float *InhInput;
    float *NmdaAmpaInput;
    float NmdaSaturation;
    float TauAmpaRise, TauAmpaFall, TauAmpaRiseFac, TauAmpaFallFac;
    float TauGabaARise, TauGabaAFall, TauGabaARiseFac, TauGabaAFallFac;
    float TauGabaBRise, TauGabaBFall, TauGabaBRiseFac, TauGabaBFallFac;
    float TauNmdaRise, TauNmdaFall, TauNmdaRiseFac, TauNmdaFallFac;
    float AmpaCorr, GabaACorr, GabaBCorr, NmdaCorr;
    float NmdaAmpaRatio;
    float NmdaAmpaEPSPRatio;
    float GabaABRatio;
    float NaReversePot;
    float GabaAReversePot;
    float GabaBReversePot;
public:
    AmpaNmdaGabaChannels(int N,
                         float TauAmpaRise=0.5,
                         float TauAmpaFall=2.4,
                         float TauNmdaRise=5.5,
                         float TauNmdaFall=100,
                         float TauGabaARise=1.0,
                         float TauGabaAFall=7.0,
                         float TauGabaBRise=13.5,
                         float TauGabaBFall=140,
                         float GabaABRatio=0.0,
                         float NmdaAmpaRatio=0.0);
    
    ~AmpaNmdaGabaChannels();
    virtual void SetTauNmda(float rise, float fall);
    virtual void SetTauAmpa(float rise, float fall);
    virtual void SetTauGabaA(float rise, float fall);
    virtual void SetTauGabaB(float rise, float fall);
    virtual void SetNmdaAmpaRatio(float ratio);
    long calcMemConsumption();
    void WriteSimInfo(stringstream &sstr);
    virtual void reset();
};


struct DecoParas {
    DecoParas(float a, float b, float c, float d, float e, float f, float g):
            RestingPot(a),
            ResetPot(b),
            Threshold(c),
            CMembrane(d),
            GLeak(e),
            AbsoluteRefractoryPeriod(f),
            SpikePot (g) {}
    float RestingPot, ResetPot, Threshold, CMembrane, GLeak, AbsoluteRefractoryPeriod, SpikePot;
    int print();
};

static const DecoParas DecoParaExcitatory (-70,-55,-50,0.5,25,1,-40);
static const DecoParas DecoParaInhibitory (-70,-55,-50,0.2,20,1,-40);

/*! \brief Klasse  DecoLifLayer
 *         Implementation eines leaky integrate-and-fire neurons nach Deco und rolls 2005
 *
 *  inspired by Deco and Rolls 2005,
 * "Neurodynamics of biased competition and cooperation for attention:
 * A model with spiking neurons., Journal of Neurophysiology, 94:295-331
 */
//
class DecoLifLayer: public layer, public AmpaNmdaGabaChannels
{
protected:

    float RestingPot;  // etwa -60 bis -70 mV
    float ResetPot;  // ==RestingPot
    float Threshold; // 40-50 mV
    float TauMembrane; // Membran-Zeitkonstante, meist 20 ms
    float CMembrane; // Membran-Kapazitaet, nach Deco/Rolls 2005: 0.5nF bzw. 0.2 nF fuer ex/inh Neuronen
    float CMembraneFac; //==dt/CMembrane;
    float GLeak; // leak conductance, Deco/Rolls 2005: 25nS/20nS for ex/inh neurons
    int* Refractory;
    float AbsoluteRefractoryPeriod;
    int arf;
    float SpikePot;
    int t;
public:
    DecoLifLayer(int n, DecoParas Paras=DecoParaExcitatory);
    ~DecoLifLayer();
    virtual float* GetInputPointer(csimInputChannel num=csimInputChannel_AMPA);
    virtual float* GetPspPointer(csimInputChannel);
    virtual int proceede(int);
    virtual int WriteSimInfo(fstream &fw);
    virtual int StartBinRec(int n, int StartNumber=0);
    virtual int reset(int t);
    virtual long calcMemoryConsumption();

    int proceedeInThread(int TotalTime);
    int calculateInThread(int Start_Index, int Stop_Index);

};

//Hodgkin-Huxley-Model-Neuron

enum HodgHuxGate {HodgHux_h,HodgHux_m,HodgHux_n};

class HodgHuxLayer : public layer, public AmpaNmdaGabaChannels
{
protected:
    float *hAlphaArr,*mAlphaArr,*nAlphaArr,*hBetaArr,*mBetaArr,*nBetaArr;
    float VL,VNa,VK,gL,gNa,gK,CM,VShift,SpikeDetectionThreshold;
    float hParas[6],mParas[6],nParas[6];
    float *hNa,*mNa,*nK,*M;
    float Mmin,Mmax,Mstart;
    int MN;
    bool *alreadySpiked;

public:
    HodgHuxLayer(int n,float vl=10.6,float vna=120.,float vk=-12.,float gl=0.3,float gna=120.,float gk=36.,float cm=1.,float vshift=70,float spikedetectionthreshold=50,float mmin=-100,float mmax=200,float mstart=-65, int mn=1000);
    ~HodgHuxLayer();
    virtual int proceede(int);
    void setxAlphaBeta(HodgHuxGate ,float [6]);
    int M2index(float );
    virtual float* GetInputPointer(csimInputChannel num=csimInputChannel_AMPA);
    virtual int WriteSimInfo(fstream &fw);
    virtual int StartBinRec(int n, int StartNumber=0);
};
/////////////////


#endif /*_HPP_LAYER */