Cargando…

Emerging memristive neurons for neuromorphic computing and sensing

Inspired by the principles of the biological nervous system, neuromorphic engineering has brought a promising alternative approach to intelligence computing with high energy efficiency and low consumption. As pivotal components of neuromorphic system, artificial spiking neurons are powerful informat...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Zhiyuan, Tang, Wei, Zhang, Beining, Yang, Rui, Miao, Xiangshui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10120469/
https://www.ncbi.nlm.nih.gov/pubmed/37090846
http://dx.doi.org/10.1080/14686996.2023.2188878
_version_ 1785029186641985536
author Li, Zhiyuan
Tang, Wei
Zhang, Beining
Yang, Rui
Miao, Xiangshui
author_facet Li, Zhiyuan
Tang, Wei
Zhang, Beining
Yang, Rui
Miao, Xiangshui
author_sort Li, Zhiyuan
collection PubMed
description Inspired by the principles of the biological nervous system, neuromorphic engineering has brought a promising alternative approach to intelligence computing with high energy efficiency and low consumption. As pivotal components of neuromorphic system, artificial spiking neurons are powerful information processing units and can achieve highly complex nonlinear computations. By leveraging the switching dynamic characteristics of memristive device, memristive neurons show rich spiking behaviors with simple circuit. This report reviews the memristive neurons and their applications in neuromorphic sensing and computing systems. The switching mechanisms that endow memristive devices with rich dynamics and nonlinearity are highlighted, and subsequently various nonlinear spiking neuron behaviors emulated in these memristive devices are reviewed. Then, recent development is introduced on neuromorphic system with memristive neurons for sensing and computing. Finally, we discuss challenges and outlooks of the memristive neurons toward high-performance neuromorphic hardware systems and provide an insightful perspective for the development of interactive neuromorphic electronic systems.
format Online
Article
Text
id pubmed-10120469
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Taylor & Francis
record_format MEDLINE/PubMed
spelling pubmed-101204692023-04-22 Emerging memristive neurons for neuromorphic computing and sensing Li, Zhiyuan Tang, Wei Zhang, Beining Yang, Rui Miao, Xiangshui Sci Technol Adv Mater Focus on Materials and Technologies for Memristors and Neuromorphic Devices Inspired by the principles of the biological nervous system, neuromorphic engineering has brought a promising alternative approach to intelligence computing with high energy efficiency and low consumption. As pivotal components of neuromorphic system, artificial spiking neurons are powerful information processing units and can achieve highly complex nonlinear computations. By leveraging the switching dynamic characteristics of memristive device, memristive neurons show rich spiking behaviors with simple circuit. This report reviews the memristive neurons and their applications in neuromorphic sensing and computing systems. The switching mechanisms that endow memristive devices with rich dynamics and nonlinearity are highlighted, and subsequently various nonlinear spiking neuron behaviors emulated in these memristive devices are reviewed. Then, recent development is introduced on neuromorphic system with memristive neurons for sensing and computing. Finally, we discuss challenges and outlooks of the memristive neurons toward high-performance neuromorphic hardware systems and provide an insightful perspective for the development of interactive neuromorphic electronic systems. Taylor & Francis 2023-04-19 /pmc/articles/PMC10120469/ /pubmed/37090846 http://dx.doi.org/10.1080/14686996.2023.2188878 Text en © 2023 The Author(s). Published by National Institute for Materials Science in partnership with Taylor & Francis Group. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
spellingShingle Focus on Materials and Technologies for Memristors and Neuromorphic Devices
Li, Zhiyuan
Tang, Wei
Zhang, Beining
Yang, Rui
Miao, Xiangshui
Emerging memristive neurons for neuromorphic computing and sensing
title Emerging memristive neurons for neuromorphic computing and sensing
title_full Emerging memristive neurons for neuromorphic computing and sensing
title_fullStr Emerging memristive neurons for neuromorphic computing and sensing
title_full_unstemmed Emerging memristive neurons for neuromorphic computing and sensing
title_short Emerging memristive neurons for neuromorphic computing and sensing
title_sort emerging memristive neurons for neuromorphic computing and sensing
topic Focus on Materials and Technologies for Memristors and Neuromorphic Devices
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10120469/
https://www.ncbi.nlm.nih.gov/pubmed/37090846
http://dx.doi.org/10.1080/14686996.2023.2188878
work_keys_str_mv AT lizhiyuan emergingmemristiveneuronsforneuromorphiccomputingandsensing
AT tangwei emergingmemristiveneuronsforneuromorphiccomputingandsensing
AT zhangbeining emergingmemristiveneuronsforneuromorphiccomputingandsensing
AT yangrui emergingmemristiveneuronsforneuromorphiccomputingandsensing
AT miaoxiangshui emergingmemristiveneuronsforneuromorphiccomputingandsensing