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

 Download zip file 
Help downloading and running models
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]
Citations  Citation Browser
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;
/
Buckley2011
libraries
CNlib2
readme
CN_absynapse.cc
CN_absynapse.h
CN_absynapse_smSTDP.cc *
CN_absynapse_smSTDP.h *
CN_absynapse_smSTDP1.cc *
CN_absynapse_smSTDP1.h *
CN_absynapse_strange.cc
CN_absynapse_strange.h
CN_absynapse_strange2.cc
CN_absynapse_strange2.h *
CN_absynapse2.cc
CN_absynapse2.h *
CN_absynapseECHebb3.cc
CN_absynapseECHebb3.h
CN_absynapseECplast1.cc
CN_absynapseECplast1.h
CN_absynapseECplast2.cc
CN_absynapseECplast2.h *
CN_absynapseECplast3.cc
CN_absynapseECplast3.h
CN_base.h
CN_base.h~
CN_Colpitts.cc
CN_Colpitts.h
CN_Data.cc
CN_Data.h
CN_DCInput.cc *
CN_DCInput.h
CN_demiGapsynapse.cc
CN_demiGapsynapse.h
CN_ECAneuron.cc
CN_ECAneuron.h
CN_ECdemiGapsynapse.cc *
CN_ECdemiGapsynapse.cc~ *
CN_ECdemiGapsynapse.h
CN_ECdemiGapsynapse.h~
CN_ECneuron.cc
CN_ECneuron.h
CN_ECneuron2.cc
CN_ECneuron2.h
CN_ECneuron3.cc *
CN_ECneuron3.h
CN_ECneuron3NS.cc
CN_ECneuron3NS.cc~ *
CN_ECneuron3NS.h
CN_ECneuron3NS.h~
CN_HHneuron.cc *
CN_HHneuron.h
CN_HHneuron.h~ *
CN_HHneuronNS.cc
CN_HHneuronNS.cc~ *
CN_HHneuronNS.h
CN_HHneuronNS.h~ *
CN_InputFunction.cc
CN_InputFunction.h
CN_InputFunction2.cc
CN_InputFunction2.h
CN_InputFunctionNoise.cc
CN_InputFunctionNoise.h
CN_inputneuron.cc
CN_inputneuron.h
CN_legacy_absynapse.cc
CN_legacy_absynapse.h
CN_legacy_absynapse_smSTDP.cc *
CN_legacy_absynapse_smSTDP.h *
CN_legacy_absynapse_smSTDP1.cc *
CN_legacy_absynapse_smSTDP1.h *
CN_legacy_absynapseECplast1.cc
CN_legacy_absynapseECplast1.h
CN_legacy_absynapseECplast2.cc
CN_legacy_absynapseECplast2.h *
CN_legacy_absynapseECplast3.cc
CN_legacy_absynapseECplast3.h
CN_LPneuronAstrid.cc
CN_LPneuronAstrid.h
CN_LPneuronNT.cc *
CN_LPneuronNT.h
CN_LPneuronRafi4.cc
CN_LPneuronRafi4.h
CN_LPneuronRafi5.cc
CN_LPneuronRafi5.h
CN_LTVneuron.cc
CN_LTVneuron.h
CN_LTVsynapse.cc
CN_LTVsynapse.h
CN_multifire_inputneuron.cc
CN_multifire_inputneuron.h
CN_neuron.cc
CN_neuron.h
CN_NeuronModel.cc
CN_NeuronModel.h
CN_pNaNeuron.cc
CN_pNaNeuron.h
CN_PNneuron.cc
CN_PNneuron.cc~
CN_PNneuron.h
CN_PNneuron.h~
CN_PNneuronM.cc
CN_PNneuronM.cc~
CN_PNneuronM.h
CN_PNneuronM.h~
CN_Poissoninput.cc
CN_Poissoninput.h
CN_Poissonneuron.cc
CN_Poissonneuron.h
CN_PopPoissonN.cc
CN_PopPoissonN.cc~
CN_PopPoissonN.h
CN_PopPoissonN.h~
CN_Rallsynapse.cc
CN_Rallsynapse.h
CN_Rallsynapse_strange.cc
CN_Rallsynapse_strange.h
CN_RallsynapseECplast3.cc
CN_RallsynapseECplast3.h
CN_rk65n.cc *
CN_rk65n.h
CN_rk65n.o
CN_rk6n.cc
CN_rk6n.cc~
CN_rk6n.h
CN_rk6n.o
CN_rk6n_noise.cc
CN_rk6n_noise.cc~
CN_rk6n_noise.h
CN_S01synapse.cc
CN_S01synapse.h
CN_S01synapseECplast3.cc
CN_S01synapseECplast3.h
CN_simpleinput.cc
CN_simpleinput.h
CN_synapse.cc
CN_synapse.h
CN_synapseAstrid.cc *
CN_synapseAstrid.h
CN_t2Rallsynapse.cc
CN_t2Rallsynapse.h
CN_t2RallsynapseECplast3.cc
CN_t2RallsynapseECplast3.h
CN_TimeNeuron.cc *
CN_TimeNeuron.h
CN_ValAdaptneuron.cc
CN_ValAdaptneuron.h
CN_Valneuron.cc *
CN_Valneuron.h
CN_Valneuron2.cc
CN_Valneuron2.h
CN_Valneuron2cNS.cc
CN_Valneuron2cNS.cc~
CN_Valneuron2cNS.h
CN_Valneuron2cNS.h~
CN_ValneuronNS.cc
CN_ValneuronNS.cc~ *
CN_ValneuronNS.h
CN_ValneuronNS.h~
CN_VdPolneuron.cc
CN_VdPolneuron.h
hello.dat
Makefile
testCN
testCN.cc
testCN.cc~
testCN.o
todo_remarks
tst.dat
tst.msg *
tst.out *
tst2.msg *
tst2.out *
                            
//--------------------------------------------------------------------------
// 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_HHNEURON_H
#define CN_HHNEURON_H

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

// parameters of the HH neuron, they are identical for all neurons used
// (and therefore made global to save memory)

#define HH_IVARNO 4
#define HH_PNO 7

double stdHH_p[HH_PNO]= {
  120.0,         // 0 - gNa: Na conductance in 1/(mOhms * cm^2)
  55.0,          // 1 - ENa: Na equi potential in mV
  36.0,          // 2 - gK: K conductance in 1/(mOhms * cm^2)
  -72.0,         // 3 - EK: K equi potential in mV
  0.3,           // 4 - gl: leak conductance in 1/(mOhms * cm^2)
  -50.0,         // 5 - El: leak equi potential in mV
  1.0          // 6 - Cmem: membr. capacity density in muF/cm^2
};

double *HH_p= stdHH_p;

const char *HH_p_text[HH_PNO]= {
  "0 - gNa: Na conductance in 1/(mOhms * cm^2)",
  "1 - ENa: Na equi potential in mV",
  "2 - gK: K conductance in 1/(mOhms * cm^2)",
  "3 - EK: K equi potential in mV",
  "4 - gl: leak conductance in 1/(mOhms * cm^2)",
  "5 - El: leak equi potential in mV",
  "6 - Cmem: membr. capacity density in muF/cm^2"
};

double HH_INIVARS[HH_IVARNO]= {
  -60.0,                       // 0 - membrane potential E
  0.0529324,                   // 1 - prob. for Na channel activation m
  0.3176767,                   // 2 - prob. for not Na channel blocking h
  0.5961207                    // 3 - prob. for K channel activation n
};

const char *HH_INIVARSTEXT[HH_IVARNO]= {
  "0 - membrane potential E",
  "1 - prob. for Na channel activation m",
  "2 - prob. for not Na channel blocking h",
  "3 - prob. for K channel activation n"
};

// the HH neuron class itself

class HHneuron: public neuron
{
 private:
  double Isyn;
  double _a, _b;
 public:
  HHneuron(int, double *);
  HHneuron(int, vector<int>, double *);
  ~HHneuron() { }
  inline virtual double E(double *);
  void derivative(double *, double *);
};

#endif