/*-------------------------------------------------------------------------- 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_RALLSYNAPSEST_CC #define CN_RALLSYNAPSEST_CC #include "CN_synapse.cc" #define Heaviside(x) ((x > 0) ? 1 : 0) // This is the constructor to be used by derived classes passing the new // internal var number, parameter number and type tag RallsynapseST::RallsynapseST(neuron *insource, neuron *intarget, double ingsyn, double inEsyn, double inEpre, double intsyn, double inxmax, int inIVARNO, int inPNO, int inTYPE): synapse(insource, intarget, inIVARNO, inPNO, inTYPE) { p[0]= ingsyn; // gsyn strength of synapse p[1]= inEsyn; // Esyn reversal potential in mV p[2]= inEpre; // Epre presyn threshold potential in mV p[3]= intsyn; // synaptic timescale in msec p[4]= inxmax; // maximal synaptic activation } // This is the constructor to be used directly ... RallsynapseST::RallsynapseST(neuron *insource, neuron *intarget, double ingsyn, double inEsyn, double inEpre, double intsyn, double inxmax): synapse(insource, intarget, RIVARNOST, RPNOST, RALL) { p[0]= ingsyn; // gsyn strength of synapse p[1]= inEsyn; // Esyn reversal potential in mV p[2]= inEpre; // Epre presyn threshold potential in mV p[3]= intsyn; // synaptic timescale in msec p[4]= inxmax; // maximal synaptic activation } RallsynapseST::RallsynapseST(neuron *insource, neuron *intarget, double *inp): synapse(insource, intarget, RIVARNOST, RPNOST, RALL) { set_p(inp); } RallsynapseST::~RallsynapseST() { } double RallsynapseST::gsyn() { return p[0]; } void RallsynapseST::set_gsyn(double ingsyn) { p[0]= ingsyn; } double RallsynapseST::Isyn(double *x) { return -p[0]*(p[4]-x[idx+1])*(target->E(x)-p[1]); } void RallsynapseST::derivative(double *x, double *dx) { dx[idx+0]= (-x[idx]+Heaviside(source->E(x)-p[2]))/p[3]; dx[idx+1]= ((p[4]-x[idx+1])/2.0-x[idx])*x[idx+1]/(p[3]*p[4]); } // end of class implementation #endif