Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing

Designing efficient neuromorphic systems for complex temporal tasks remains a challenge. Zhong et al. develop a parallel memristor-based reservoir computing system capable of tuning critical parameters, achieving classification accuracy of 99.6% in spoken-digit recognition and time-series prediction...

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Autores principales:	Yanan Zhong, Jianshi Tang, Xinyi Li, Bin Gao, He Qian, Huaqiang Wu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/581e839a8b204dbd986773af5e0fb4bc
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id	oai:doaj.org-article:581e839a8b204dbd986773af5e0fb4bc
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spelling	oai:doaj.org-article:581e839a8b204dbd986773af5e0fb4bc2021-12-02T14:09:00ZDynamic memristor-based reservoir computing for high-efficiency temporal signal processing10.1038/s41467-020-20692-12041-1723https://doaj.org/article/581e839a8b204dbd986773af5e0fb4bc2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-20692-1https://doaj.org/toc/2041-1723Designing efficient neuromorphic systems for complex temporal tasks remains a challenge. Zhong et al. develop a parallel memristor-based reservoir computing system capable of tuning critical parameters, achieving classification accuracy of 99.6% in spoken-digit recognition and time-series prediction error of 0.046 in the Hénon map.Yanan ZhongJianshi TangXinyi LiBin GaoHe QianHuaqiang WuNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Yanan Zhong Jianshi Tang Xinyi Li Bin Gao He Qian Huaqiang Wu Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing
description	Designing efficient neuromorphic systems for complex temporal tasks remains a challenge. Zhong et al. develop a parallel memristor-based reservoir computing system capable of tuning critical parameters, achieving classification accuracy of 99.6% in spoken-digit recognition and time-series prediction error of 0.046 in the Hénon map.
format	article
author	Yanan Zhong Jianshi Tang Xinyi Li Bin Gao He Qian Huaqiang Wu
author_facet	Yanan Zhong Jianshi Tang Xinyi Li Bin Gao He Qian Huaqiang Wu
author_sort	Yanan Zhong
title	Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing
title_short	Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing
title_full	Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing
title_fullStr	Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing
title_full_unstemmed	Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing
title_sort	dynamic memristor-based reservoir computing for high-efficiency temporal signal processing
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/581e839a8b204dbd986773af5e0fb4bc
work_keys_str_mv	AT yananzhong dynamicmemristorbasedreservoircomputingforhighefficiencytemporalsignalprocessing AT jianshitang dynamicmemristorbasedreservoircomputingforhighefficiencytemporalsignalprocessing AT xinyili dynamicmemristorbasedreservoircomputingforhighefficiencytemporalsignalprocessing AT bingao dynamicmemristorbasedreservoircomputingforhighefficiencytemporalsignalprocessing AT heqian dynamicmemristorbasedreservoircomputingforhighefficiencytemporalsignalprocessing AT huaqiangwu dynamicmemristorbasedreservoircomputingforhighefficiencytemporalsignalprocessing
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Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing

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