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
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
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CN_ECneuron.cc
CN_ECneuron.h
CN_ECneuron2.cc
CN_ECneuron2.h
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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
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CN_PNneuronM.cc
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CN_Poissoninput.cc
CN_Poissoninput.h
CN_Poissonneuron.cc
CN_Poissonneuron.h
CN_PopPoissonN.cc
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CN_PopPoissonN.h
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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
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CN_rk6n.h
CN_rk6n.o
CN_rk6n_noise.cc
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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
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CN_VdPolneuron.cc
CN_VdPolneuron.h
hello.dat
Makefile
testCN
testCN.cc
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testCN.o
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tst.msg *
tst.out *
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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 CN_ABSYNAPSE_CC
#define CN_ABSYNAPSE_CC

#include "CN_synapse.cc"

// This is the constructor to be used by derived classes passing the new
// internal var number, parameter number and type tag

absynapse::absynapse(neuron *insource, neuron *intarget,
		     double ingsyn, double inEsyn, double inEpre,
		     double inasyn, double inbsyn, double inVslope,
		     int inIVARNO, int inPNO, int inTYPE):
  synapse(insource, intarget, inIVARNO, inPNO, inTYPE)
{
  p[0]= ingsyn;           // gsyn strength of synapse
  p[1]= inEsyn;           // Esyn reversal potential in mV
  p[2]= inEpre;           // Epre presyn threshold potential in mV
  p[3]= inasyn;           // alpha timescale in 1/msec
  p[4]= inbsyn;           // beta timescale in 1/msec
  p[5]= inVslope;         // steepness of activation curve as func of Vpre  
} 

// This is the constructor to be used directly ...

absynapse::absynapse(neuron *insource, neuron *intarget,
		     double ingsyn, double inEsyn, double inEpre,
		     double inasyn, double inbsyn, double inVslope):
  synapse(insource, intarget, ABSYNIVARNO, ABSYNPNO, ABSYN)
{
  p[0]= ingsyn;           // gsyn strength of synapse
  p[1]= inEsyn;           // Esyn reversal potential in mV
  p[2]= inEpre;           // Epre presyn threshold potential in mV
  p[3]= inasyn;           // alpha timescale in 1/msec
  p[4]= inbsyn;           // beta timescale in 1/msec
  p[5]= inVslope;         // steepness of activation curve as func of Vpre  
} 

absynapse::absynapse(neuron *insource, neuron *intarget, double *inp):
  synapse(insource, intarget, ABSYNIVARNO, ABSYNPNO, ABSYN)
{
  set_p(inp);
} 

absynapse::~absynapse()
{
}

double absynapse::gsyn()
{
  return p[0];
}

void absynapse::set_gsyn(double ingsyn)
{
  p[0]= ingsyn;
}

double absynapse::Isyn(double *x)
{
  return -p[0]*x[idx]*(target->E(x)-p[1]);
}


void absynapse::derivative(double *x, double *dx)
{
  dx[idx]= p[3]*(1.0-x[idx])*(1.0+tanh((source->E(x)-p[2])/p[5]))/2.0
    -p[4]*x[idx];

//   if (source->E(x) > p[2]) {
//     dx[idx]= p[3]*(1.0-x[idx])-p[4]*x[idx];
//   }
//   else {
//     dx[idx]= -p[4]*x[idx];
//   }
}

// end of class implementation

#endif