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_ABSYNAPSEECPLAST1_CC
#define CN_ABSYNAPSEECPLAST1_CC

#include "CN_absynapse.cc"

absynapseECplast1::absynapseECplast1(neuron *insource, neuron *intarget,
				   double inksyn, double inEsyn, double inEpre,
				   double inasyn, double inbsyn,
				   double inVslope, double ingmax,
				   double ing12, double ingslope,
				   double inlrnampl):
  absynapse(insource, intarget, inksyn, inEsyn, inEpre, inasyn,
	    inbsyn, inVslope,
	    ABECPLAST1IVARNO, ABECPLAST1PNO, ABECPLAST1)
{
  p[6]= ingmax;           // g_max the maximum synapse strength
  p[7]= ing12;            // g_1/2 the midpoint of the smoothing fn
  p[8]= ingslope;         // g_slope the slope of the tanh
  p[9]= inlrnampl;
  set_gsyn(inksyn);       // ksyn strength of synapse
  synapse_change= 0;
} 

absynapseECplast1::~absynapseECplast1()
{
}

double absynapseECplast::rgsyn()
{
  // return raw gsyn
  return p[0];
}

void absynapseECplast::set_rgsyn(double ingsyn)
{
  // set raw gsyn
  p[0]= ingsyn;
}

double absynapseECplast::gsyn()
{
  double tmp= p[6]/2.0*(tanh(p[8]*(p[0]-p[7]))+1.0);
  return tmp;
}

void absynapseECplast::set_gsyn(double ingsyn)
{
  double tmp= ingsyn/p[6]*2.0-1.0;
  p[0]= 0.5*log((1.0+tmp)/(1.0-tmp))/p[8]+p[7];
}

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

void absynapseECplast1::update_gsyn(double *x)
{
  static double chng; 
  if ((source->start_spiking) || (target->start_spiking)) {
    if ((source->spike_time > 0.0) && (target->spike_time > 0.0)) {
      double tau= target->spike_time-source->spike_time;
      chng= STDP_func(tau);
      if (chng != 0.0) {
	synapse_change= 1;
	p[0]+=chng;
      }
      else synapse_change= 0;
    }
  }
}

double absynapseECplast1::STDP_func(double t)
{
  if (t > 0.0) {  
    return p[6]*pow(t, 10.0)*exp(-abs(t))*5.14e-9;
    // amplitude is fit to data ...
  }
  else {
    return -p[6]*pow(t, 10.0)*exp(-abs(t))*5.14e-9;
  }
}

// end of class implementation

#undef TINFIN

#endif



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