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Structural phase transitions in neural networks.

Turova, Tatyana LU (2014) In Mathematical Biosciences and Engineering 11(1). p.139-148
Abstract
A model is considered for a neural network that is a stochastic process on a random graph. The neurons are represented by "integrate-and-fire" processes. The structure of the graph is determined by the probabilities of the connections, and it depends on the activity in the network. The dependence between the initial level of sparseness of the connections and the dynamics of activation in the network was investigated. A balanced regime was found between activity, i.e., the level of excitation in the network, and inhibition, that allows formation of synfire chains.
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Mathematical Biosciences and Engineering
volume
11
issue
1
pages
139 - 148
publisher
American Institute of Mathematical Sciences
external identifiers
  • wos:000326979900010
  • pmid:24245677
  • scopus:84889871635
ISSN
1547-1063
DOI
10.3934/mbe.2014.11.139
language
English
LU publication?
yes
id
e90c54df-88d7-41db-8951-a8d746e0678a (old id 4179209)
date added to LUP
2013-12-12 15:12:23
date last changed
2017-01-15 03:01:58
@article{e90c54df-88d7-41db-8951-a8d746e0678a,
  abstract     = {A model is considered for a neural network that is a stochastic process on a random graph. The neurons are represented by "integrate-and-fire" processes. The structure of the graph is determined by the probabilities of the connections, and it depends on the activity in the network. The dependence between the initial level of sparseness of the connections and the dynamics of activation in the network was investigated. A balanced regime was found between activity, i.e., the level of excitation in the network, and inhibition, that allows formation of synfire chains.},
  author       = {Turova, Tatyana},
  issn         = {1547-1063},
  language     = {eng},
  number       = {1},
  pages        = {139--148},
  publisher    = {American Institute of Mathematical Sciences},
  series       = {Mathematical Biosciences and Engineering},
  title        = {Structural phase transitions in neural networks.},
  url          = {http://dx.doi.org/10.3934/mbe.2014.11.139},
  volume       = {11},
  year         = {2014},
}