Structural phase transitions in neural networks.
(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:
https://lup.lub.lu.se/record/4179209
- author
- Turova, Tatyana LU
- organization
- publishing date
- 2014
- 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
- 2016-04-01 09:54:26
- date last changed
- 2022-01-25 17:47:18
@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}},
doi = {{10.3934/mbe.2014.11.139}},
volume = {{11}},
year = {{2014}},
}