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_ECNEURON_CC
#define CN_ECNEURON_CC

#include "CN_neuron.cc"

ECneuron::ECneuron(int inlabel, double *the_p= ECN_p):
  neuron(inlabel, ECN_IVARNO, ECNEURON, the_p, ECN_PNO)
{
}

ECneuron::ECneuron(int inlabel, tnvector<int> inpos, double *the_p= ECN_p):
  neuron(inlabel, ECN_IVARNO, ECNEURON, inpos, the_p, ECN_PNO)
{
}

inline double ECneuron::E(double *x)
{
  assert(enabled);
  return x[idx];
}

#define _xfunc(a,b,k,V) ((a)*(V)+(b))/(1.0-exp(((V)+(b)/(a))/(k)))

void ECneuron::derivative(double *x, double *dx)
{
  Isyn= 0.0;
  forall(den_it) {
    Isyn+= den_it->c_value()->Isyn(x);
  }
  
  // differential eqn for E, the membrane potential
  dx[idx]= -(ipower(x[idx+1],3)*x[idx+2]*p[0]*(x[idx]-p[1]) +
			ipower(x[idx+3],4)*p[2]*(x[idx]-p[3])+
			(x[idx+4]*p[10]+x[idx+5]*p[11])*(x[idx]-p[12])+
			p[4]*(x[idx]-p[5])+p[6]*(x[idx]-p[7])-Isyn)/p[9];

  // diferential eqn for m, the probability for one Na channel activation
  // particle
  _a= 0.32*(13.0-x[idx]-p[8]) / (exp((13.0-x[idx]-p[8])/4.0)-1.0);
  _b= 0.28*(x[idx]+p[8]-40.0)/(exp((x[idx]+p[8]-40.0)/5.0)-1.0);
  dx[idx+1]= _a*(1.0-x[idx+1])-_b*x[idx+1];

  // differential eqn for h, the probability for the Na channel blocking
  // particle to be absent
  _a= 0.128*exp((17.0-x[idx]-p[8])/18.0);   
  _b= 4.0 / (exp((40-x[idx]-p[8])/5.0)+1.0);
  dx[idx+2]= _a*(1.0-x[idx+2])-_b*x[idx+2];

  // differential eqn for n, the probability for one K channel activation
  // particle
  _a= .032*(15.0-x[idx]-p[8]) / (exp((15.0-x[idx]-p[8])/5.0)-1.0); 
  _b= 0.5*exp((10.0-x[idx]-p[8])/40.0);
  dx[idx+3]= _a*(1.0-x[idx+3])-_b*x[idx+3];

  // differential equation for the Ih1 activation variable
  _a= _xfunc(-2.89e-3, -0.445, 24.02, x[idx]);
  _b= _xfunc(2.71e-2, -1.024, -17.4, x[idx]);
  dx[idx+4]= _a*(1.0-x[idx+4])-_b*x[idx+4];

  // differential equation for the Ih2 activation variable
  _a= _xfunc(-3.18e-3, -0.695, 26.72, x[idx]);
  _b= _xfunc(2.16e-2, -1.065, -14.25, x[idx]);
  dx[idx+5]= _a*(1.0-x[idx+5])-_b*x[idx+5];
}

#undef _xfunc
#endif

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