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On a phase diagram for random neural networks with embedded spike timing dependent plasticity

Turova, Tatyana LU and Villa, Alessandro E. P. (2007) In BioSystems 89(1-3). p.280-286
Abstract
This paper presents an original mathematical framework based on graph theory which is a first attempt to investigate the dynamics of a model of neural networks with embedded spike timing dependent plasticity. The neurons correspond to integrate-and-fire units located at the vertices of a finite subset of 2D lattice. There are two types of vertices, corresponding to the inhibitory and the excitatory neurons. The edges are directed and labelled by the discrete values of the synaptic strength. We assume that there is an initial firing pattern corresponding to a subset of units that generate a spike. The number of activated externally vertices is a small fraction of the entire network. The model presented here describes how such pattern... (More)
This paper presents an original mathematical framework based on graph theory which is a first attempt to investigate the dynamics of a model of neural networks with embedded spike timing dependent plasticity. The neurons correspond to integrate-and-fire units located at the vertices of a finite subset of 2D lattice. There are two types of vertices, corresponding to the inhibitory and the excitatory neurons. The edges are directed and labelled by the discrete values of the synaptic strength. We assume that there is an initial firing pattern corresponding to a subset of units that generate a spike. The number of activated externally vertices is a small fraction of the entire network. The model presented here describes how such pattern propagates throughout the network as a random walk on graph. Several results are compared with computational simulations and new data are presented for identifying critical parameters of the model. (C) 2006 Elsevier Ireland Ltd. All rights reserved. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
neural network, graph theory, spiking, random network, spike timing dependent synaptic plasticity
in
BioSystems
volume
89
issue
1-3
pages
280 - 286
publisher
Elsevier
external identifiers
  • wos:000247057900037
  • scopus:34247602151
ISSN
1872-8324
DOI
10.1016/j.biosystems.2006.05.019
language
English
LU publication?
yes
id
8b26c7f1-5467-4842-a5d5-8c72e70b6904 (old id 651151)
date added to LUP
2008-01-03 11:26:53
date last changed
2017-09-24 04:24:06
@article{8b26c7f1-5467-4842-a5d5-8c72e70b6904,
  abstract     = {This paper presents an original mathematical framework based on graph theory which is a first attempt to investigate the dynamics of a model of neural networks with embedded spike timing dependent plasticity. The neurons correspond to integrate-and-fire units located at the vertices of a finite subset of 2D lattice. There are two types of vertices, corresponding to the inhibitory and the excitatory neurons. The edges are directed and labelled by the discrete values of the synaptic strength. We assume that there is an initial firing pattern corresponding to a subset of units that generate a spike. The number of activated externally vertices is a small fraction of the entire network. The model presented here describes how such pattern propagates throughout the network as a random walk on graph. Several results are compared with computational simulations and new data are presented for identifying critical parameters of the model. (C) 2006 Elsevier Ireland Ltd. All rights reserved.},
  author       = {Turova, Tatyana and Villa, Alessandro E. P.},
  issn         = {1872-8324},
  keyword      = {neural network,graph theory,spiking,random network,spike timing dependent synaptic plasticity},
  language     = {eng},
  number       = {1-3},
  pages        = {280--286},
  publisher    = {Elsevier},
  series       = {BioSystems},
  title        = {On a phase diagram for random neural networks with embedded spike timing dependent plasticity},
  url          = {http://dx.doi.org/10.1016/j.biosystems.2006.05.019},
  volume       = {89},
  year         = {2007},
}