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

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

  Implementation of a 6-5 Runge Kutta method with adaptive time step
  mostly taken from the book "The numerical analysis of ordinary differential
  equations - Runge-Kutta and general linear methods" by J.C. Butcher, Wiley,
  Chichester, 1987 and a free adaption to a 6 order Runge Kutta method
  of an ODE system with additive white noise

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

using namespace std;

#ifndef CN_RK65N_H
#define CN_RK65N_H

#include "CN_NeuronModel.h"
#include <cmath>

class rk65n
{
private:
  double a[9][8];
  double b[9];

  double newdt, dtx, theEps;
  double *Y[9];
  double *F[9];
  double *y5;
  double aF;
  double delta;
  int i, j, k;

protected:
  int N;
  double maxdt, eps, abseps, releps; 

public:
  rk65n(int, double, double, double, double);
  double integrate(double *, double *, NeuronModel *, double);
};

#endif

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