On a phase diagram for random neural networks with embedded spike timing dependent plasticity
(2007) In BioSystems 89(13). p.280286 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 integrateandfire 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 integrateandfire 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:
http://lup.lub.lu.se/record/651151
 author
 Turova, Tatyana ^{LU} and Villa, Alessandro E. P.
 organization
 publishing date
 2007
 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
 13
 pages
 280  286
 publisher
 Elsevier
 external identifiers

 wos:000247057900037
 scopus:34247602151
 ISSN
 18728324
 DOI
 10.1016/j.biosystems.2006.05.019
 language
 English
 LU publication?
 yes
 id
 8b26c7f154674842a5d58c72e70b6904 (old id 651151)
 date added to LUP
 20080103 11:26:53
 date last changed
 20180107 09:04:08
@article{8b26c7f154674842a5d58c72e70b6904, 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 integrateandfire 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 = {18728324}, keyword = {neural network,graph theory,spiking,random network,spike timing dependent synaptic plasticity}, language = {eng}, number = {13}, pages = {280286}, 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}, }