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;
/
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_NEURON_CC
#define CN_NEURON_CC

#include "CN_neuron.h"

neuron::neuron(int inlabel, int iniVarNo, int intype, double *inp, int inpno)
{
  label= inlabel;
  iVarNo= iniVarNo;
  type= intype;
  pno= inpno;
  if (pno > 0) {
    p= new double[pno];
    set_p(inp);
  }
  den_it= den.iterator();
  start_spiking= 0;
  spiking= 0;
  spike_time= -1.0;

  // we don't know our index number yet
  idx= 0;
  enabled= 0;
}

neuron::neuron(int inlabel, int iniVarNo, int intype, tnvector<int> inpos,
	       double *inp, int inpno)
{
  label= inlabel;
  iVarNo= iniVarNo;
  type= intype;
  pno= inpno;
  if (pno > 0) {
    p= new double[pno];
    set_p(inp);
  }
  pos.resize(inpos.dim());
  pos= inpos;
  den_it= den.iterator();
  start_spiking= 0;
  spiking= 0;
  spike_time= -1.0;

 // we don't know our index number yet
  idx= 0;
  enabled= 0;
}

neuron::~neuron()
{
  forall(den_it) {
    den_it->c_value()->target= NULL;
  }
  delete den_it;
  delete[] p;
}

void neuron::set_p(double *inp)
{
  for (int i= 0; i < pno; i++) p[i]= inp[i];
}

void neuron::spike_detect(double *x)
{
  assert(enabled);
  if (E(x) >= SPK_V_THRESH)
  {
    if (!spiking)
    {
      start_spiking= 1;
      spiking= 1;
      spike_time= x[0];
    }
    else start_spiking= 0;
  }
  else {
    spiking= 0;
    start_spiking= 0;
  }
}

void neuron::init(double *x, double *iniVars)
{
  assert(enabled);
  for (int i= 0; i < iVarNo; i++)
  {
    x[idx+i]= iniVars[i];
  }
  start_spiking= 0;
  spiking= 0;
  spike_time= -1.0;
}

void neuron::setIdx(int inidx)
{
  assert(!enabled);
  idx= inidx;
  enabled= 1;
}

#endif


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