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Control of criticality and computation in spiking neuromorphic networks with plasticity
The critical state is assumed to be optimal for any computation in recurrent neural networks, because criticality maximizes a number of abstract computational properties. We challenge this assumption by evaluating the performance of a spiking recurrent neural network on a set of tasks of varying com...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7275091/ https://www.ncbi.nlm.nih.gov/pubmed/32503982 http://dx.doi.org/10.1038/s41467-020-16548-3 |
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author | Cramer, Benjamin Stöckel, David Kreft, Markus Wibral, Michael Schemmel, Johannes Meier, Karlheinz Priesemann, Viola |
author_facet | Cramer, Benjamin Stöckel, David Kreft, Markus Wibral, Michael Schemmel, Johannes Meier, Karlheinz Priesemann, Viola |
author_sort | Cramer, Benjamin |
collection | PubMed |
description | The critical state is assumed to be optimal for any computation in recurrent neural networks, because criticality maximizes a number of abstract computational properties. We challenge this assumption by evaluating the performance of a spiking recurrent neural network on a set of tasks of varying complexity at - and away from critical network dynamics. To that end, we developed a plastic spiking network on a neuromorphic chip. We show that the distance to criticality can be easily adapted by changing the input strength, and then demonstrate a clear relation between criticality, task-performance and information-theoretic fingerprint. Whereas the information-theoretic measures all show that network capacity is maximal at criticality, only the complex tasks profit from criticality, whereas simple tasks suffer. Thereby, we challenge the general assumption that criticality would be beneficial for any task, and provide instead an understanding of how the collective network state should be tuned to task requirement. |
format | Online Article Text |
id | pubmed-7275091 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-72750912020-06-16 Control of criticality and computation in spiking neuromorphic networks with plasticity Cramer, Benjamin Stöckel, David Kreft, Markus Wibral, Michael Schemmel, Johannes Meier, Karlheinz Priesemann, Viola Nat Commun Article The critical state is assumed to be optimal for any computation in recurrent neural networks, because criticality maximizes a number of abstract computational properties. We challenge this assumption by evaluating the performance of a spiking recurrent neural network on a set of tasks of varying complexity at - and away from critical network dynamics. To that end, we developed a plastic spiking network on a neuromorphic chip. We show that the distance to criticality can be easily adapted by changing the input strength, and then demonstrate a clear relation between criticality, task-performance and information-theoretic fingerprint. Whereas the information-theoretic measures all show that network capacity is maximal at criticality, only the complex tasks profit from criticality, whereas simple tasks suffer. Thereby, we challenge the general assumption that criticality would be beneficial for any task, and provide instead an understanding of how the collective network state should be tuned to task requirement. Nature Publishing Group UK 2020-06-05 /pmc/articles/PMC7275091/ /pubmed/32503982 http://dx.doi.org/10.1038/s41467-020-16548-3 Text en © The Author(s) 2020 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Cramer, Benjamin Stöckel, David Kreft, Markus Wibral, Michael Schemmel, Johannes Meier, Karlheinz Priesemann, Viola Control of criticality and computation in spiking neuromorphic networks with plasticity |
title | Control of criticality and computation in spiking neuromorphic networks with plasticity |
title_full | Control of criticality and computation in spiking neuromorphic networks with plasticity |
title_fullStr | Control of criticality and computation in spiking neuromorphic networks with plasticity |
title_full_unstemmed | Control of criticality and computation in spiking neuromorphic networks with plasticity |
title_short | Control of criticality and computation in spiking neuromorphic networks with plasticity |
title_sort | control of criticality and computation in spiking neuromorphic networks with plasticity |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7275091/ https://www.ncbi.nlm.nih.gov/pubmed/32503982 http://dx.doi.org/10.1038/s41467-020-16548-3 |
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