/*--------------------------------------------------------------------------
   Author: Thomas Nowotny
  
   Institute: Institute for Nonlinear Dynamics
              University of California San Diego
              La Jolla, CA 92093-0402
  
   email to:  tnowotny@ucsd.edu
  
   initial version: 2005-08-17
  
--------------------------------------------------------------------------*/

#ifndef CN_ECNEURON_CC
#define CN_ECNEURON_CC

#include "CN_neuron.cc"

ECneuron::ECneuron(int inlabel, double *the_p= ECN_p):
  neuron(inlabel, ECN_IVARNO, ECNEURON, the_p, ECN_PNO)
{
}

ECneuron::ECneuron(int inlabel, vector<int> inpos, double *the_p= ECN_p):
  neuron(inlabel, ECN_IVARNO, ECNEURON, inpos, the_p, ECN_PNO)
{
}

inline double ECneuron::E(double *x)
{
  assert(enabled);
  return x[idx];
}

#define _xfunc(a,b,k,V) ((a)*(V)+(b))/(1.0-exp(((V)+(b)/(a))/(k)))
#define _efunc(a,b,V) 1.0/(1.0 + exp(((a)-(V))/(b)))

void ECneuron::currents(ostream &os, double *x)
{
  os << -pw3(x[idx+1])*x[idx+2]*p[0]*(x[idx]-p[1]) << " ";
  os << -pw4(x[idx+3])*p[2]*(x[idx]-p[3]) << " ";
  os << -(x[idx+4]*p[10]+x[idx+5]*p[11])*(x[idx]-p[12]) << " ";
  os << -p[4]*(x[idx]-p[5]) << " ";
  os << -p[6]*(x[idx]-p[7]) << " ";
  os << -p[13]*x[idx+7]*pw4(x[idx+6])*(x[idx]-p[3]) << endl;
}

    
void ECneuron::derivative(double *x, double *dx)
{
  Isyn= 0.0;
  forall(den, den_it) {
    Isyn+= (*den_it)->Isyn(x);
  }
  
  // differential eqn for E, the membrane potential
  dx[idx]= -(pw3(x[idx+1])*x[idx+2]*p[0]*(x[idx]-p[1]) +
	     pw4(x[idx+3])*p[2]*(x[idx]-p[3])+
	     (x[idx+4]*p[10]+x[idx+5]*p[11])*(x[idx]-p[12])+
	     p[4]*(x[idx]-p[5])+p[6]*(x[idx]-p[7])
	     +p[13]*x[idx+6]*pw3(x[idx+7])*(x[idx]-p[3])-Isyn)/p[9];

  // differential eqn for m, the probability for one Na channel activation
  // particle
  _a= 0.32*(13.0-x[idx]-p[8]) / (exp((13.0-x[idx]-p[8])/4.0)-1.0);
  _b= 0.28*(x[idx]+p[8]-40.0)/(exp((x[idx]+p[8]-40.0)/5.0)-1.0);
  dx[idx+1]= _a*(1.0-x[idx+1])-_b*x[idx+1];

  // differential eqn for h, the probability for the Na channel blocking
  // particle to be absent
  _a= 0.128*exp((17.0-x[idx]-p[8])/18.0);   
  _b= 4.0 / (exp((40-x[idx]-p[8])/5.0)+1.0);
  dx[idx+2]= _a*(1.0-x[idx+2])-_b*x[idx+2];

  // differential eqn for n, the probability for one K channel activation
  // particle
  _a= .032*(15.0-x[idx]-p[8]) / (exp((15.0-x[idx]-p[8])/5.0)-1.0); 
  _b= 0.5*exp((10.0-x[idx]-p[8])/40.0);
  dx[idx+3]= _a*(1.0-x[idx+3])-_b*x[idx+3];

  // differential equation for the Ih1 activation variable
  _a= _xfunc(-2.89e-3, -0.445, 24.02, x[idx]);
  _b= _xfunc(2.71e-2, -1.024, -17.4, x[idx]);
  dx[idx+4]= _a*(1.0-x[idx+4])-_b*x[idx+4];

  // differential equation for the Ih2 activation variable
  _a= _xfunc(-3.18e-3, -0.695, 26.72, x[idx]);
  _b= _xfunc(2.16e-2, -1.065, -14.25, x[idx]);
  dx[idx+5]= _a*(1.0-x[idx+5])-_b*x[idx+5];
  // differential equation for the slow K+ activation variable
  //  _a= _efunc(20, 5, x[idx]);
  //  _b= _efunc(20, 15, x[idx]);
  _a= _efunc(20, 10, x[idx]);
  _b= _efunc(20, 25, x[idx]);
  dx[idx+6]= _a*(1.0-x[idx+6])-_b*x[idx+6];
  // differential equation for the slow K+ inactivation variable
  //  _a= _efunc(-60, -15, x[idx]);
  //  _b= _efunc(20, 10, x[idx]);
  _a= _efunc(-60, -15, x[idx]);
  _b= _efunc(20, 10, x[idx]);
  dx[idx+7]= _a*(1.0-x[idx+7])-_b*x[idx+7];
}

#undef _xfunc
#endif