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 *
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
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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
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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
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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
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CN_Valneuron.h
CN_Valneuron2.cc
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CN_VdPolneuron.cc
CN_VdPolneuron.h
hello.dat
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testCN
testCN.cc
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testCN.o
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tst.msg *
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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_POISSONNEURON_CC
#define CN_POISSONNEURON_CC

#include "CN_neuron.cc"

Poissonneuron::Poissonneuron(int inlabel, vector<int> inpos,
			     double *the_p= POI_p):
  neuron(inlabel, POI_IVARNO, POISSONNEURON,
	      inpos, the_p, NEUROSYNADAPT_PNO)
{
  firing= 0;
  refract= 0;
  tlast= -10000;
  fire_t= 0;
  myx= new double[1];
  myxn= new double[1];
  myx[0]= p[2];
  myxn[0]= p[2];
  spike = 0;
//  setIdx(1);
}

Poissonneuron::Poissonneuron(int inlabel, double *the_p= POI_p):
  neuron(inlabel, POI_IVARNO, POISSONNEURON,
	 the_p, NEUROSYNADAPT_PNO)
{
  firing= 0;
  refract= 0;
  tlast= -10000;
  fire_t= 0;
  myx= new double[1];
  myxn= new double[1];
  myx[0]= p[2];
  myxn[0]= p[2];
  spike=0;
 // setIdx(-1);
}

Poissonneuron::~Poissonneuron()
{
  delete[] myx;
  delete[] myxn;
}


double Poissonneuron::S(double *x)
{
  assert(enabled);

  return x[idx];
}

double Poissonneuron::E(double *x)
{
  return myx[0];
}
double Poissonneuron::F(double *x)
{
  return Isyn;
}

void Poissonneuron::validate_E(double *x, double ddt)
{

	 Isyn= 0.0;
	  forall(den, den_it) {
	    Isyn+= (*den_it)->Isyn(x);
	  }


	  if (firing) {
	    if (x[0] - fire_t > p[0]) { // remember: x[0] is the time
	      firing= 0;
	      refract= 1;
	    }
	  }
	  else {
	    if (refract) {
	      if (x[0] - fire_t > p[1]) refract=0;
	    }
	    else {
	      if (RG.n() <= Isyn*ddt) {
		firing= 1;
		fire_t= x[0];
	      }
	    }
	  }
	  if (firing) myxn[0]= p[3];
	  else myxn[0]= p[2];
}

void Poissonneuron::step()
{
  myx[0]= myxn[0];
}

void Poissonneuron::init(double *x, double *iniVars)
{
  myx[0]= -60.0;
  myxn[0]=-60.0;
  x[idx] =0;
}
void Poissonneuron::derivative(double *x, double *dx)
{


	double alpha = p[5];
	  double beta = p[6];
	  double tr = p[7];

	  static double dt;

	   dt= x[0] - tlast;
	   if ((dt >= 0) && (dt <= p[7])) {
	 	  dx[idx]= alpha- beta*x[idx];
	   } else {
	     if ((myx[0] > p[8]) && (dt > p[7])) {
	       // new spike ... start releasing
	       tlast= x[0];
	      dx[idx]= alpha - beta*x[idx];
	     }
	     else {
	       // no release
	       dx[idx]= -beta*x[idx];
	     }
	   }

}


#endif






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