Wang-Buzsaki Interneuron (Talathi et al., 2010)

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Accession:136308
The submitted code provides the relevant C++ files, matlabfiles and the data files essential to reproduce the figures in the JCNS paper titled Control of neural synchrony using channelrhodopsin-2: A computational study.
Reference:
1 . Talathi SS, Carney PR, Khargonekar PP (2010) Control of neural synchrony using channelrhodopsin-2: a computational study. J Comput Neurosci [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Synapse;
Brain Region(s)/Organism:
Cell Type(s): Neocortex fast spiking (FS) interneuron; Abstract Wang-Buzsaki neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s): Gaba;
Simulation Environment: C or C++ program;
Model Concept(s): Synchronization;
Implementer(s): Talathi Sachin [talathi at ufl.edu];
Search NeuronDB for information about:  Gaba;
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JCNS-2010-CodeAndData
simul_lrn
CNlib
CVS
readme *
CN_absynapse.cc *
CN_absynapse.h *
CN_absynapseECplast1.cc *
CN_absynapseECplast1.h *
CN_absynapseECplast2.cc *
CN_absynapseECplast2.h *
CN_absynapseECplast3.cc *
CN_absynapseECplast3.h *
CN_DCInput.cc *
CN_DCInput.h *
CN_ECneuron.cc *
CN_ECneuron.h *
CN_HHneuron.cc *
CN_HHneuron.h *
CN_inputneuron.cc *
CN_inputneuron.cc~
CN_inputneuron.h *
CN_LPneuronAstrid.cc *
CN_LPneuronAstrid.h *
CN_LPneuronRafi4.cc *
CN_LPneuronRafi4.h *
CN_multifire_inputneuron.cc *
CN_multifire_inputneuron.h *
CN_neuron.cc *
CN_neuron.h *
CN_NeuronModel.cc *
CN_NeuronModel.h *
CN_Poissonneuron.cc *
CN_Poissonneuron.h *
CN_Rallsynapse.cc *
CN_Rallsynapse.h *
CN_rk65n.cc *
CN_rk65n.h *
CN_rk65n.o
CN_synapse.cc *
CN_synapse.h *
CN_synapseAstrid.cc *
CN_synapseAstrid.h *
CN_TimeNeuron.cc *
CN_TimeNeuron.h *
CN_Valneuron.cc *
CN_Valneuron.h *
CN_Valneuron2.cc *
CN_Valneuron2.h *
ids.h *
Makefile *
testCN *
testCN.cc *
testCN.o
                            
/*--------------------------------------------------------------------------
   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_ABSYNAPSE_CC
#define CN_ABSYNAPSE_CC

#include "CN_synapse.cc"

// This is the constructor to be used by derived classes passing the new
// internal var number, parameter number and type tag

absynapse::absynapse(neuron *insource, neuron *intarget,
		     double ingsyn, double inEsyn, double inEpre,
		     double inasyn, double inbsyn, double inVslope,
		     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]= inasyn;           // alpha timescale in 1/msec
  p[4]= inbsyn;           // beta timescale in 1/msec
  p[5]= inVslope;         // steepness of activation curve as func of Vpre  
} 

// This is the constructor to be used directly ...

absynapse::absynapse(neuron *insource, neuron *intarget,
		     double ingsyn, double inEsyn, double inEpre,
		     double inasyn, double inbsyn, double inVslope):
  synapse(insource, intarget, ABSYNIVARNO, ABSYNPNO, ABSYN)
{
  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]= inasyn;           // alpha timescale in 1/msec
  p[4]= inbsyn;           // beta timescale in 1/msec
  p[5]= inVslope;         // steepness of activation curve as func of Vpre  
} 

absynapse::absynapse(neuron *insource, neuron *intarget, double *inp):
  synapse(insource, intarget, ABSYNIVARNO, ABSYNPNO, ABSYN)
{
  set_p(inp);
} 

absynapse::~absynapse()
{
}

double absynapse::gsyn()
{
  return p[0];
}

void absynapse::set_gsyn(double ingsyn)
{
  p[0]= ingsyn;
}

double absynapse::Isyn(double *x)
{
  return -p[0]*x[idx]*(target->E(x)-p[1]);
}


void absynapse::derivative(double *x, double *dx)
{
  dx[idx]= p[3]*(1.0-x[idx])*(1.0+tanh((source->E(x)-p[2])/p[5]))/2.0
    -p[4]*x[idx];
}

// end of class implementation

#endif



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