/*-------------------------------------------------------------------------- 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_VALNEURON_CC #define CN_VALNEURON_CC #include "CN_neuron.cc" Valneuron::Valneuron(int inlabel, double *the_p= Val_p): neuron(inlabel, Val_IVARNO, VALNEURON, the_p, Val_PNO) { } Valneuron::Valneuron(int inlabel, vector inpos, double *the_p= Val_p): neuron(inlabel, Val_IVARNO, VALNEURON, inpos, the_p, Val_PNO) { } inline double Valneuron::E(double *x) { assert(enabled); return x[idx]; } void Valneuron::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])+ p[4]*(x[idx]-p[5])+p[6]*(x[idx]-p[7])-Isyn)/p[9]; // diferential 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])/p[10]; // 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])/p[10]; // 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])/p[10]; } #endif