A Moth MGC Model-A HH network with quantitative rate reduction (Buckley & Nowotny 2011)

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Accession:144403
We provide the model used in Buckley & Nowotny (2011). It consists of a network of Hodgkin Huxley neurons coupled by slow GABA_B synapses which is run alongside a quantitative reduction described in the associated paper.
Reference:
1 . Buckley CL, Nowotny T (2011) Multiscale model of an inhibitory network shows optimal properties near bifurcation. Phys Rev Lett 106:238109 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Hodgkin-Huxley neuron;
Channel(s): I K; I K,leak; I M; I K,Ca; I Q; I Na, leak;
Gap Junctions:
Receptor(s): GabaB;
Gene(s):
Transmitter(s): Gaba;
Simulation Environment: C or C++ program;
Model Concept(s): Activity Patterns; Bifurcation; Multiscale;
Implementer(s): Buckley, Christopher [chrisbuckley at brain.riken.jp];
Search NeuronDB for information about:  GabaB; I K; I K,leak; I M; I K,Ca; I Q; I Na, leak; Gaba;
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Buckley2011
libraries
CNlib2
readme
CN_absynapse.cc
CN_absynapse.h
CN_absynapse_smSTDP.cc *
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CN_absynapseECplast3.h
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CN_DCInput.cc *
CN_DCInput.h
CN_demiGapsynapse.cc
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CN_InputFunctionNoise.cc
CN_InputFunctionNoise.h
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CN_legacy_absynapse.cc
CN_legacy_absynapse.h
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CN_legacy_absynapseECplast1.cc
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CN_multifire_inputneuron.cc
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CN_Poissoninput.cc
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CN_Poissonneuron.cc
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CN_PopPoissonN.cc
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CN_Rallsynapse.cc
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CN_S01synapse.cc
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CN_S01synapseECplast3.cc
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CN_simpleinput.cc
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CN_synapse.cc
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CN_synapseAstrid.h
CN_t2Rallsynapse.cc
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CN_t2RallsynapseECplast3.cc
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//--------------------------------------------------------------------------
// 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: 2003-06-18
//
//--------------------------------------------------------------------------


#ifndef POISSONINPUT_H
#define POISSONINPUT_H

#include <cmath>
#include "randomGen.h"
#include "randomGen.cc"

randomGen R;
#define POISSONINPUT 101

#define POI_IVARNO 0
#define POI_PNO 18

double stdPOI_p[POI_PNO]= {
  0.1,           // 0 - Lambda: firing rate
  0.0,           // 1 - refratory period
  20.0,          // 2 - Vspike
  -60.0 ,         // 3 - Vrest
  0.021, // 4 - gl: leak conductance in 1/(mOhms * cm^2)
  		-55.0, // 5 - El: leak equi potential in mV
  		0.00572, // 6 - gKl: potassium leakage conductivity
  		-95.0, // 7 - EKl: potassium leakage equi pot in mV
  		65.0, // 8 - V0: ~ total equi potential (?)
  		0.143, // 9 - Cmem: membr. capacity density in muF/cm^2
  		0,//0.715, // 10 - gM: conductance of the M current
  		0.0, // 11- IDC: baseline offset current
  		-60, //  12 inEsyn %reversal potential (-95 = inhibitory)
  		-20, // 13  inEpre %threshold for pre synaptic spike detection
  		2, //14 inasyn
  				0.05, // 15 inbsyn
  				1, // 16 inrtime
  		0 //17 noise
};

double *POI_p= stdPOI_p;

const char *POI_p_text[POI_PNO]= {
  "0 - Lmabda: firing rate",
  "1 - refractory period",
  "2 - Vspike",
  "3 - Vrest"
};

// the POI neuron class itself

class Poissoninput:public neuron
{
 public:
  int Evalid;
  int Isynvalid;
  int refract;
  double mS,mSLast;

  Poissoninput(int, double *);
  ~Poissoninput();
  void set_input(double);
  void init();
  void advance(double *, double);
  double E(double *);
  void derivative(double *, double *) { }

  inline virtual double F(double *){return 0;}
  double S(double *);
};

#endif

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