STDP promotes synchrony of inhibitory networks in the presence of heterogeneity (Talathi et al 2008)

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"Recently Haas et al. (J Neurophysiol 96: 3305–3313, 2006), observed a novel form of spike timing dependent plasticity (iSTDP) in GABAergic synaptic couplings in layer II of the entorhinal cortex. Depending on the relative timings of the presynaptic input at time tpre and the postsynaptic excitation at time tpost, the synapse is strengthened (delta_t = t(post) - t(pre) > 0) or weakened (delta_t < 0). The temporal dynamic range of the observed STDP rule was found to lie in the higher gamma frequency band (> or = 40 Hz), a frequency range important for several vital neuronal tasks. In this paper we study the function of this novel form of iSTDP in the synchronization of the inhibitory neuronal network. In particular we consider a network of two unidirectionally coupled interneurons (UCI) and two mutually coupled interneurons (MCI), in the presence of heterogeneity in the intrinsic firing rates of each coupled neuron. ..."
1 . Talathi SS, Hwang DU, Ditto WL (2008) Spike timing dependent plasticity promotes synchrony of inhibitory networks in the presence of heterogeneity. J Comput Neurosci 25:262-81 [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: Entorhinal cortex;
Cell Type(s):
Channel(s): I Na,t; I K;
Gap Junctions:
Receptor(s): GabaA; Gaba;
Simulation Environment: C or C++ program;
Model Concept(s): STDP;
Implementer(s): Talathi Sachin [talathi at];
Search NeuronDB for information about:  GabaA; Gaba; I Na,t; I K;
readme * *
CN_absynapse.h * *
CN_absynapseECplast1.h * *
CN_absynapseECplast2.h * *
CN_absynapseECplast3.h * *
CN_DCInput.h * *
CN_ECneuron.h * *
CN_HHneuron.h * *
CN_inputneuron.h * *
CN_LPneuronAstrid.h * *
CN_LPneuronRafi4.h * *
CN_multifire_inputneuron.h * *
CN_neuron.h * *
CN_NeuronModel.h * *
CN_Poissonneuron.h * *
CN_Rallsynapse.h * *
CN_rk65n.h * *
CN_synapse.h * *
CN_synapseAstrid.h * *
CN_TimeNeuron.h * *
CN_Valneuron.h * *
CN_Valneuron2.h *
ids.h *
Makefile *
testCN * *
   Author: Thomas Nowotny
   Institute: Institute for Nonlinear Dynamics
              University of California San Diego
              La Jolla, CA 92093-0402
   email to:
   initial version: 2005-08-17


  Implementation of a 6-5 Runge Kutta method with adaptive time step
  mostly taken from the book "The numerical analysis of ordinary differential
  equations - Runge-Kutta and general linear methods" by J.C. Butcher, Wiley,
  Chichester, 1987 and a free adaption to a 6 order Runge Kutta method
  of an ODE system with additive white noise


using namespace std;

#ifndef CN_RK65N_H
#define CN_RK65N_H

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

class rk65n
  double a[9][8];
  double b[9];

  double newdt, dtx, theEps;
  double *Y[9];
  double *F[9];
  double *y5;
  double aF;
  double delta;
  int i, j, k;

  int N;
  double maxdt, eps, abseps, releps; 

  rk65n(int, double, double, double, double);
  double integrate(double *, double *, NeuronModel *, double);