/*-------------------------------------------------------------------------- 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 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