Spiking neurons with spatiotemporal dynamics and gain modulation for monolithically integrated memristive neural networks

Designing energy efficient and scalable artificial networks for neuromorphic computing remains a challenge. Here, the authors demonstrate online learning in a monolithically integrated 4 × 4 fully memristive neural network consisting of volatile NbOx memristor neurons and nonvolatile TaOx memristor...

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Autores principales:	Qingxi Duan, Zhaokun Jing, Xiaolong Zou, Yanghao Wang, Ke Yang, Teng Zhang, Si Wu, Ru Huang, Yuchao Yang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/500aa3c61def49a49934f77da350f567
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spelling	oai:doaj.org-article:500aa3c61def49a49934f77da350f5672021-12-02T15:39:43ZSpiking neurons with spatiotemporal dynamics and gain modulation for monolithically integrated memristive neural networks10.1038/s41467-020-17215-32041-1723https://doaj.org/article/500aa3c61def49a49934f77da350f5672020-07-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-17215-3https://doaj.org/toc/2041-1723Designing energy efficient and scalable artificial networks for neuromorphic computing remains a challenge. Here, the authors demonstrate online learning in a monolithically integrated 4 × 4 fully memristive neural network consisting of volatile NbOx memristor neurons and nonvolatile TaOx memristor synapses.Qingxi DuanZhaokun JingXiaolong ZouYanghao WangKe YangTeng ZhangSi WuRu HuangYuchao YangNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-13 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Qingxi Duan Zhaokun Jing Xiaolong Zou Yanghao Wang Ke Yang Teng Zhang Si Wu Ru Huang Yuchao Yang Spiking neurons with spatiotemporal dynamics and gain modulation for monolithically integrated memristive neural networks
description	Designing energy efficient and scalable artificial networks for neuromorphic computing remains a challenge. Here, the authors demonstrate online learning in a monolithically integrated 4 × 4 fully memristive neural network consisting of volatile NbOx memristor neurons and nonvolatile TaOx memristor synapses.
format	article
author	Qingxi Duan Zhaokun Jing Xiaolong Zou Yanghao Wang Ke Yang Teng Zhang Si Wu Ru Huang Yuchao Yang
author_facet	Qingxi Duan Zhaokun Jing Xiaolong Zou Yanghao Wang Ke Yang Teng Zhang Si Wu Ru Huang Yuchao Yang
author_sort	Qingxi Duan
title	Spiking neurons with spatiotemporal dynamics and gain modulation for monolithically integrated memristive neural networks
title_short	Spiking neurons with spatiotemporal dynamics and gain modulation for monolithically integrated memristive neural networks
title_full	Spiking neurons with spatiotemporal dynamics and gain modulation for monolithically integrated memristive neural networks
title_fullStr	Spiking neurons with spatiotemporal dynamics and gain modulation for monolithically integrated memristive neural networks
title_full_unstemmed	Spiking neurons with spatiotemporal dynamics and gain modulation for monolithically integrated memristive neural networks
title_sort	spiking neurons with spatiotemporal dynamics and gain modulation for monolithically integrated memristive neural networks
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/500aa3c61def49a49934f77da350f567
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Spiking neurons with spatiotemporal dynamics and gain modulation for monolithically integrated memristive neural networks

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