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]
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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
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CNlib2
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CN_absynapse.cc
CN_absynapse.h
CN_absynapse_smSTDP.cc *
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CN_Data.cc
CN_Data.h
CN_DCInput.cc *
CN_DCInput.h
CN_demiGapsynapse.cc
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CN_ECAneuron.h
CN_ECdemiGapsynapse.cc *
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CN_legacy_absynapse.cc
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CN_LTVsynapse.cc
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CN_multifire_inputneuron.cc
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CN_S01synapseECplast3.cc
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CN_simpleinput.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: 2005-08-17
//
//--------------------------------------------------------------------------


#ifndef DC_ECNEURON_H
#define DC_ECNEURON_H

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

// parameters of the Entorhinal cortex stellate cell

#define ECN_IVARNO 8
#define ECN_PNO 16

double stdECN_p[ECN_PNO]= {
  7.15,          // 0 - gNa: Na conductance in 1/(mOhms * cm^2)
  50.0,          // 1 - ENa: Na equi potential in mV
  1.43,          // 2 - gK: K conductance in 1/(mOhms * cm^2)
  -95.0,         // 3 - EK: K equi potential in mV
  0.021,         // 4 - gl: leak conductance in 1/(mOhms * cm^2)
  -55.0,         // 5 - El: leak equi potential in mV
  0.055, //0.00572,  // 6 - gKl: potassium leakage conductivity
  -95.0,         // 7 - EKl: potassium leakage equi pot in mV
  65.0,          // 8 - V0: ~ total equi potential (?)
  0.286, //0.143,    // 9 - Cmem: membr. capacity density in muF/cm^2
  0.074, //1.85,    // 10 - gh1
  0.04, // 1.0,      // 11 - gh2
  -20.0, // 20.0           // 12 - Vh
  0.1,   // 13 - gKs
  0.0,   // 14 - noise
  0.0    // 15 - IDC
};

double *ECN_p= stdECN_p;

const char *ECN_p_text[ECN_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 - gKl: potassium leakage conductivity",
  "7 - EKl: potassium leakage equi pot in mV",
  "8 - V0: ~ total equi potential (?)",
  "9 - Cmem: membr. capacity density in muF/cm^2",
  "10 - gh1",
  "11 - gh2",
  "12 - Vh",
  "13 - gKs",
  "14 - noise",
  "15 - IDC"
};

double ECN_INIVARS[ECN_IVARNO]= {
  -64.1251,                       // 0 - membrane potential E
  0.0176331,                   // 1 - prob. for Na channel activation m
  0.994931,                   // 2 - prob. for not Na channel blocking h
  0.0433969,                   // 3 - prob. for K channel activation n
  0.443961,                         // 4 - Ih1 activation
  0.625308,                         // 5 - Ih2 activation
  2.680168432e-05,                          // 6 - Ks activation
  0.9992359062                           // 7 - Ks inactivation
};

const char *ECN_INIVARSTEXT[ECN_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",
  "4 - Ih1 activation",
  "5 - Ih2 activation",
  "6 - Ks activation"
};


// ECneuron class itself

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

#endif