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Step-like dependence of memory function on pulse width in spintronics reservoir computing

Physical reservoir computing is a type of recurrent neural network that applies the dynamical response from physical systems to information processing. However, the relation between computation performance and physical parameters/phenomena still remains unclear. This study reports our progress regar...

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Autores principales: Yamaguchi, Terufumi, Akashi, Nozomi, Nakajima, Kohei, Kubota, Hitoshi, Tsunegi, Sumito, Taniguchi, Tomohiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7659325/
https://www.ncbi.nlm.nih.gov/pubmed/33177539
http://dx.doi.org/10.1038/s41598-020-76142-x
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author Yamaguchi, Terufumi
Akashi, Nozomi
Nakajima, Kohei
Kubota, Hitoshi
Tsunegi, Sumito
Taniguchi, Tomohiro
author_facet Yamaguchi, Terufumi
Akashi, Nozomi
Nakajima, Kohei
Kubota, Hitoshi
Tsunegi, Sumito
Taniguchi, Tomohiro
author_sort Yamaguchi, Terufumi
collection PubMed
description Physical reservoir computing is a type of recurrent neural network that applies the dynamical response from physical systems to information processing. However, the relation between computation performance and physical parameters/phenomena still remains unclear. This study reports our progress regarding the role of current-dependent magnetic damping in the computational performance of reservoir computing. The current-dependent relaxation dynamics of a magnetic vortex core results in an asymmetric memory function with respect to binary inputs. A fast relaxation caused by a large input leads to a fast fading of the input memory, whereas a slow relaxation by a small input enables the reservoir to keep the input memory for a relatively long time. As a result, a step-like dependence is found for the short-term memory and parity-check capacities on the pulse width of input data, where the capacities remain at 1.5 for a certain range of the pulse width, and drop to 1.0 for a long pulse-width limit. Both analytical and numerical analyses clarify that the step-like behavior can be attributed to the current-dependent relaxation time of the vortex core to a limit-cycle state.
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spelling pubmed-76593252020-11-13 Step-like dependence of memory function on pulse width in spintronics reservoir computing Yamaguchi, Terufumi Akashi, Nozomi Nakajima, Kohei Kubota, Hitoshi Tsunegi, Sumito Taniguchi, Tomohiro Sci Rep Article Physical reservoir computing is a type of recurrent neural network that applies the dynamical response from physical systems to information processing. However, the relation between computation performance and physical parameters/phenomena still remains unclear. This study reports our progress regarding the role of current-dependent magnetic damping in the computational performance of reservoir computing. The current-dependent relaxation dynamics of a magnetic vortex core results in an asymmetric memory function with respect to binary inputs. A fast relaxation caused by a large input leads to a fast fading of the input memory, whereas a slow relaxation by a small input enables the reservoir to keep the input memory for a relatively long time. As a result, a step-like dependence is found for the short-term memory and parity-check capacities on the pulse width of input data, where the capacities remain at 1.5 for a certain range of the pulse width, and drop to 1.0 for a long pulse-width limit. Both analytical and numerical analyses clarify that the step-like behavior can be attributed to the current-dependent relaxation time of the vortex core to a limit-cycle state. Nature Publishing Group UK 2020-11-11 /pmc/articles/PMC7659325/ /pubmed/33177539 http://dx.doi.org/10.1038/s41598-020-76142-x Text en © The Author(s) 2020 Open AccessThis 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/.
spellingShingle Article
Yamaguchi, Terufumi
Akashi, Nozomi
Nakajima, Kohei
Kubota, Hitoshi
Tsunegi, Sumito
Taniguchi, Tomohiro
Step-like dependence of memory function on pulse width in spintronics reservoir computing
title Step-like dependence of memory function on pulse width in spintronics reservoir computing
title_full Step-like dependence of memory function on pulse width in spintronics reservoir computing
title_fullStr Step-like dependence of memory function on pulse width in spintronics reservoir computing
title_full_unstemmed Step-like dependence of memory function on pulse width in spintronics reservoir computing
title_short Step-like dependence of memory function on pulse width in spintronics reservoir computing
title_sort step-like dependence of memory function on pulse width in spintronics reservoir computing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7659325/
https://www.ncbi.nlm.nih.gov/pubmed/33177539
http://dx.doi.org/10.1038/s41598-020-76142-x
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