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
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_ABSYNAPSE_SMSTDP_CC
#define CN_ABSYNAPSE_SMSTDP_CC

#include "CN_synapse.cc"

// There is no constructor to be used directly (class is abstract) ...

absynapse_smSTDP::absynapse_smSTDP(neuron *insource, neuron *intarget,
				     int inIVARNO, int inPNO, int inTYPE):
  synapse(insource, intarget, inIVARNO, inPNO, inTYPE)
{
  tpre= -1e10;
  tpost= -1e10;
  dt= 0.0;
} 

absynapse_smSTDP::~absynapse_smSTDP()
{
}

double absynapse_smSTDP::gsyn(double *x)
{
  return x[idx+1];
}

// this function does not work ...
double absynapse_smSTDP::gsyn()
{
  cerr << "using unsupported function!" << endl;
  exit(1);
}

void absynapse_smSTDP::set_gsyn(double ingsyn, double *x)
{
  x[idx+1]= ingsyn;
}

void absynapse_smSTDP::set_gsyn(double ingsyn)
{
  cerr << "using unsupported function!" << endl;
  exit(1);
}

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

void absynapse_smSTDP::calc_dg(double *x)
{
  if (source->start_spiking) {
    tpre= x[0];
    dt= tpost-tpre;
    x[idx+2]+= stdp_fn(dt);
  }
  if (target->start_spiking) {
    tpost= x[0];
    dt= tpost-tpre;
    x[idx+2]+= stdp_fn(dt);
  }
}

void absynapse_smSTDP::derivative(double *x, double *dx)
{
  dx[idx]= p[2]*(1.0-x[idx])*(1.0+tanh((source->E(x)-p[1])/p[4]))/2.0
    -p[3]*x[idx];
  dx[idx+1]= x[idx+2]*x[idx+1]*(p[6]-x[idx+1])-p[8]*(x[idx+1]-p[7]);
  dx[idx+2]= -p[5]*x[idx+2];
}

// end of class implementation

#endif