How network structure affects the dynamics of a network of stochastic spiking neurons
Up to now, it still remains an open question about the relation between the structure of brain networks and their functions. The effects of structure on the dynamics of neural networks are usually investigated via extensive numerical simulations, while analytical analysis is always very difficult an...
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Veröffentlicht in: | Chaos (Woodbury, N.Y.) N.Y.), 2023-09, Vol.33 (9) |
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creator | Chen, Lei Yu, Chaojun Zhai, Jian |
description | Up to now, it still remains an open question about the relation between the structure of brain networks and their functions. The effects of structure on the dynamics of neural networks are usually investigated via extensive numerical simulations, while analytical analysis is always very difficult and thus rare. In this work, we explored the effects of a random regular graph on the dynamics of a neural network of stochastic spiking neurons, which has a bistable region when fully connected. We showed by numerical simulations that as the number of each neuron’s neighbors decreases, the bistable region shrinks and eventually seems to disappear, and a critical-like transition appears. In the meantime, we made analytical analysis that explains numerical results. We hope this would give some insights into how structure affects the dynamics of neural networks from a theoretical perspective, rather than merely by numerical simulations. |
doi_str_mv | 10.1063/5.0164207 |
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The effects of structure on the dynamics of neural networks are usually investigated via extensive numerical simulations, while analytical analysis is always very difficult and thus rare. In this work, we explored the effects of a random regular graph on the dynamics of a neural network of stochastic spiking neurons, which has a bistable region when fully connected. We showed by numerical simulations that as the number of each neuron’s neighbors decreases, the bistable region shrinks and eventually seems to disappear, and a critical-like transition appears. In the meantime, we made analytical analysis that explains numerical results. 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We hope this would give some insights into how structure affects the dynamics of neural networks from a theoretical perspective, rather than merely by numerical simulations.</description><subject>Computer simulation</subject><subject>Dynamic structural analysis</subject><subject>Dynamics</subject><subject>Neural networks</subject><subject>Neurons</subject><subject>Spiking</subject><issn>1054-1500</issn><issn>1089-7682</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp90E1LAzEQBuAgCtbqwX8Q8KLC1snXNnuUolYoeLHnJZtNbPqxqUmW0n9vSosHD55mhnkYhhehWwIjAiV7EiMgJacwPkMDArIqxqWk54de8IIIgEt0FeMSAAhlYoDmU7_DnUk7H1Y4ptDr1AeDlbVGp4jTwuB236mN0xF7i9WvzUNMXi9UTE7juHUr133lbR98F6_RhVXraG5OdYjmry-fk2kx-3h7nzzPCs2oSAWRjMqqlcZKIRtOFSeKAy1N07bWNoTqSnJaAbAGWlsx3QKXTVOqkjMNsmVDdH-8uw3-uzcx1RsXtVmvVWd8H2sqS-A5Fg6Z3v2hS9-HLn-XlajGlDFOsno4Kh18jMHYehvcRoV9TaA-BFyL-hRwto9HG7VLKjnf_YN_ABYrehM</recordid><startdate>202309</startdate><enddate>202309</enddate><creator>Chen, Lei</creator><creator>Yu, Chaojun</creator><creator>Zhai, Jian</creator><general>American Institute of Physics</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-6109-9206</orcidid><orcidid>https://orcid.org/0000-0002-7136-4894</orcidid><orcidid>https://orcid.org/0000-0002-0372-0770</orcidid></search><sort><creationdate>202309</creationdate><title>How network structure affects the dynamics of a network of stochastic spiking neurons</title><author>Chen, Lei ; Yu, Chaojun ; Zhai, Jian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c325t-183289d8ef858b42a41a4026ebddffb12c98429003b0df93cd048bb6a643c08d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Computer simulation</topic><topic>Dynamic structural analysis</topic><topic>Dynamics</topic><topic>Neural networks</topic><topic>Neurons</topic><topic>Spiking</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Lei</creatorcontrib><creatorcontrib>Yu, Chaojun</creatorcontrib><creatorcontrib>Zhai, Jian</creatorcontrib><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>MEDLINE - Academic</collection><jtitle>Chaos (Woodbury, N.Y.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Lei</au><au>Yu, Chaojun</au><au>Zhai, Jian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>How network structure affects the dynamics of a network of stochastic spiking neurons</atitle><jtitle>Chaos (Woodbury, N.Y.)</jtitle><date>2023-09</date><risdate>2023</risdate><volume>33</volume><issue>9</issue><issn>1054-1500</issn><eissn>1089-7682</eissn><coden>CHAOEH</coden><abstract>Up to now, it still remains an open question about the relation between the structure of brain networks and their functions. 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subjects | Computer simulation Dynamic structural analysis Dynamics Neural networks Neurons Spiking |
title | How network structure affects the dynamics of a network of stochastic spiking neurons |
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