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

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Accession:119159
"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. ..."
Reference:
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]
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;
Gene(s):
Transmitter(s):
Simulation Environment: C or C++ program;
Model Concept(s): STDP;
Implementer(s): Talathi Sachin [talathi at ufl.edu];
Search NeuronDB for information about:  GabaA; Gaba; I Na,t; I K;
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TalathiEtAl2008
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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_VALNEURON_H
#define CN_VALNEURON_H

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

// parameters of the HH neuron, they are identical for all neurons used
// (and therefore made global to save memory)

#define Val_IVARNO 4
#define Val_PNO 10

double stdVal_p[Val_PNO]= {
  7.15,          // 0 - gNa: Na conductance in 1/(mOhms * cm^2)
  50.0,          // 1 - ENa: Na equi potential in mV
  1.43,          // 2 - gK: K conductance in 1/(mOhms * cm^2)
  -95.0,         // 3 - EK: K equi potential in mV
  0.021,         // 4 - gl: leak conductance in 1/(mOhms * cm^2)
  -55.0,         // 5 - El: leak equi potential in mV
  0.00572,       // 6 - gKl: potassium leakage conductivity
  -95.0,         // 7 - EKl: potassium leakage equi pot in mV
  65.0,          // 8 - V0: ~ total equi potential (?)
  0.143        // 9 - Cmem: membr. capacity density in muF/cm^2
};

double *Val_p= stdVal_p;

char *Val_p_text[Val_PNO]= {
  "0 - gNa: Na conductance in 1/(mOhms * cm^2)",
  "1 - ENa: Na equi potential in mV",
  "2 - gK: K conductance in 1/(mOhms * cm^2)",
  "3 - EK: K equi potential in mV",
  "4 - gl: leak conductance in 1/(mOhms * cm^2)",
  "5 - El: leak equi potential in mV",
  "6 - gKl: potassium leakage conductivity",
  "7 - EKl: potassium leakage equi pot in mV",
  "8 - V0: ~ total equi potential (?)",
  "9 - Cmem: membr. capacity density in muF/cm^2"
};

double Val_INIVARS[Val_IVARNO]= {
  -60.0,                       // 0 - membrane potential E
  0.0529324,                   // 1 - prob. for Na channel activation m
  0.3176767,                   // 2 - prob. for not Na channel blocking h
  0.5961207                    // 3 - prob. for K channel activation n
};

char *Val_INIVARSTEXT[Val_IVARNO]= {
  "0 - membrane potential E",
  "1 - prob. for Na channel activation m",
  "2 - prob. for not Na channel blocking h",
  "3 - prob. for K channel activation n"
};


// Valentins HH neuron class itself

class Valneuron: public neuron
{
 private:
  double Isyn;
  double _a, _b;
 public:
  Valneuron(int, double *);
  Valneuron(int, tnvector<int>, double *);
  ~Valneuron() { }
  inline virtual double E(double *);
  virtual void derivative(double *, double *);
};

#endif




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