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
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
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CN_PNneuronM.h
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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
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
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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.dat
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_RK6N_CC
#define CN_RK6N_CC

#include "CN_rk6n.h"
#include <iostream>

rk6n::rk6n(int dim)
{
  N= dim;
  for (int i= 0; i < 9; i++) {
    Y[i]= new double[N];
    F[i]= new double[N];
    for (int j= i; j < 8; j++) {
      a[i][j]= 0.0;
    }
  }
  
  a[1][0]= 0.111111111111111;
  a[2][0]= 0.555555555555556e-1;
  a[2][1]= 0.555555555555556e-1;
  a[3][0]= 0.416666666666667e-1;
  a[3][1]= 0.0;
  a[3][2]= 0.125;
  a[4][0]= 0.166666666666667;
  a[4][1]= 0.0;
  a[4][2]= -0.5;
  a[4][3]= 0.666666666666667;
  a[5][0]= 0.1875e+1;
  a[5][1]= 0.0;
  a[5][2]= -0.7875e+1;
  a[5][3]= 0.7e+1;
  a[5][4]= -0.5;
  a[6][0]= -0.4227272727272727e+1;
  a[6][1]= 0.0;
  a[6][2]= 0.176995738636364e+2;
  a[6][3]= -0.142883522727273e+2;
  a[6][4]= 0.522017045454545;
  a[6][5]= 0.104403409090909e+1;
  a[7][0]= 0.840622673179752e+1;
  a[7][1]= 0.0;
  a[7][2]= -0.337303717185049e+2;
  a[7][3]= 0.271460231129622e+2;
  a[7][4]= 0.342046929709216;
  a[7][5]= -0.184653767923258e+1;
  a[7][6]= 0.577349465373733;
  a[8][0]= 0.128104575163399;
  a[8][1]= 0.0;
  a[8][2]= 0.0;
  a[8][3]= -0.108433734939759;
  a[8][4]= 0.669375;
  a[8][5]= -0.146666666666667;
  a[8][6]= 0.284444444444444;
  a[8][7]= 0.173176381998583;

  b[0]= 0.567119155354449e-1;
  b[1]= 0.0;
  b[2]= 0.0;
  b[3]= 0.210909572355356;
  b[4]= 0.141490384615385;
  b[5]= 0.202051282051282;
  b[6]= 0.253186813186813;
  b[7]= 0.843679809736684e-1;
  b[8]= 0.512820512820513e-1;
}

rk6n::~rk6n()
{
  delete[] Y;
  delete[] F;
}

void rk6n::integrate(double *y, double *yn,
			NeuronModel *model, double dt)
{
  // calculate iterative terms rk6_Y[__i] and rk6_F[__i] (to sixth order)
  yn[0]= y[0]+dt;
  for (i= 0; i < 9; i++)
  {
    for (k= 1; k < N; k++)
    {
      aF= 0.0;
      for (j= 0; j < i; j++)
	aF+= a[i][j]*F[j][k];
      Y[i][k]= y[k]+dt*aF;
    }
    model->derivative(Y[i], F[i]);
  }

  // no build of 5th order scheme

  // sum up rk6_Y[__i] and rk6_F[__i] to build 6th order scheme -> yn
  for (k= 1; k < N; k++)
  {
    aF= 0.0;
    for (j= 0; j < 9; j++) aF+= b[j]*F[j][k];
    yn[k]= y[k]+ dt*aF;
  }
}

#endif

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