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