Structural phase transitions in neural networks
A model is considered for a neural network that is a stochasticprocess on a random graph. The neurons are represented by``integrate-and-fire" processes. The structure of the graph isdetermined by the probabilities of the connections, and it depends on theactivity in the network. The depende...
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| Format: | Article |
| Language: | English |
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AIMS Press
2013-08-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2014.11.139 |
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| author | Tatyana S. Turova |
| author_facet | Tatyana S. Turova |
| author_sort | Tatyana S. Turova |
| collection | DOAJ |
| description | A model is considered for a neural network that is a stochasticprocess on a random graph. The neurons are represented by``integrate-and-fire" processes. The structure of the graph isdetermined by the probabilities of the connections, and it depends on theactivity in the network. The dependence between theinitial level ofsparseness of the connections and thedynamics of activation in the network was investigated. A balanced regime was foundbetween activity, i.e., the level of excitation in the network, andinhibition, that allows formation of synfire chains. |
| format | Article |
| id | doaj-art-0be19d9b2d634fabbe0cfc6d011d6f09 |
| institution | Kabale University |
| issn | 1551-0018 |
| language | English |
| publishDate | 2013-08-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj-art-0be19d9b2d634fabbe0cfc6d011d6f092025-01-24T02:26:48ZengAIMS PressMathematical Biosciences and Engineering1551-00182013-08-0111113914810.3934/mbe.2014.11.139Structural phase transitions in neural networksTatyana S. Turova0Mathematical Center, University of Lund, Box 118, Lund S-221 00A model is considered for a neural network that is a stochasticprocess on a random graph. The neurons are represented by``integrate-and-fire" processes. The structure of the graph isdetermined by the probabilities of the connections, and it depends on theactivity in the network. The dependence between theinitial level ofsparseness of the connections and thedynamics of activation in the network was investigated. A balanced regime was foundbetween activity, i.e., the level of excitation in the network, andinhibition, that allows formation of synfire chains.https://www.aimspress.com/article/doi/10.3934/mbe.2014.11.139bootstrap percolationneural networks.integrate-and-fire neuronsrandom graphs |
| spellingShingle | Tatyana S. Turova Structural phase transitions in neural networks Mathematical Biosciences and Engineering bootstrap percolation neural networks. integrate-and-fire neurons random graphs |
| title | Structural phase transitions in neural networks |
| title_full | Structural phase transitions in neural networks |
| title_fullStr | Structural phase transitions in neural networks |
| title_full_unstemmed | Structural phase transitions in neural networks |
| title_short | Structural phase transitions in neural networks |
| title_sort | structural phase transitions in neural networks |
| topic | bootstrap percolation neural networks. integrate-and-fire neurons random graphs |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2014.11.139 |
| work_keys_str_mv | AT tatyanasturova structuralphasetransitionsinneuralnetworks |