Excitable networks for finite state computation with continuous time recurrent neural networks
dc.contributor.author | Ashwin, P | |
dc.contributor.author | Postlethwaite, CM | |
dc.date.accessioned | 2021-09-09T14:40:51Z | |
dc.date.issued | 2021-10-05 | |
dc.description.abstract | Continuous time recurrent neural networks (CTRNN) are systems of coupled ordinary differential equations that are simple enough to be insightful for describing learning and computation, from both biological and machine learning viewpoints. We describe a direct constructive method of realising finite state input-dependent computations on an arbitrary directed graph. The constructed system has an excitable network attractor whose dynamics we illustrate with a number of examples. The resulting CTRNN has intermittent dynamics: trajectories spend long periods of time close to steady-state, with rapid transitions between states. Depending on parameters, transitions between states can either be excitable (inputs or noise needs to exceed a threshold to induce the transition), or spontaneous (transitions occur without input or noise). In the excitable case, we show the threshold for excitability can be made arbitrarily sensitive. | en_GB |
dc.description.sponsorship | Royal Society Te Apārangi | en_GB |
dc.description.sponsorship | Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
dc.description.sponsorship | London Mathematical Laboratory | en_GB |
dc.identifier.citation | Vol. 115, pp. 519–538 | en_GB |
dc.identifier.doi | 10.1007/s00422-021-00895-5 | |
dc.identifier.grantnumber | EP/N014391/1 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/127028 | |
dc.language.iso | en | en_GB |
dc.publisher | Springer | en_GB |
dc.rights | © 2021 The Author(s). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | |
dc.subject | Continuous Time Recurrent Neural Network | en_GB |
dc.subject | Nonlinear Dynamics | en_GB |
dc.subject | Excitable Network Attractor | en_GB |
dc.title | Excitable networks for finite state computation with continuous time recurrent neural networks | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2021-09-09T14:40:51Z | |
dc.description | This is the final version. Available on open access from Springer via the DOI in this record | en_GB |
dc.identifier.eissn | 1432-0770 | |
dc.identifier.journal | Biological Cybernetics | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0 | en_GB |
dcterms.dateAccepted | 2021-09-08 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2021-09-08 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2021-09-09T14:35:27Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2022-02-18T14:41:08Z | |
refterms.panel | B | en_GB |
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Except where otherwise noted, this item's licence is described as © 2021 The Author(s). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.