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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Main Authors:	Qingxi Duan, Zhaokun Jing, Xiaolong Zou, Yanghao Wang, Ke Yang, Teng Zhang, Si Wu, Ru Huang, Yuchao Yang
Format:	Article
Language:	English
Published:	Nature Publishing Group 2020-07-01
Series:	Nature Communications
Online Access:	https://doi.org/10.1038/s41467-020-17215-3

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spelling	doaj-500aa3c61def49a49934f77da350f5672021-07-11T11:44:59ZengNature Publishing GroupNature Communications2041-17232020-07-0111111310.1038/s41467-020-17215-3Spiking neurons with spatiotemporal dynamics and gain modulation for monolithically integrated memristive neural networksQingxi Duan0Zhaokun Jing1Xiaolong Zou2Yanghao Wang3Ke Yang4Teng Zhang5Si Wu6Ru Huang7Yuchao Yang8Key Laboratory of Microelectronic Devices and Circuits (MOE), Department of Micro/nanoelectronics, Peking UniversityKey Laboratory of Microelectronic Devices and Circuits (MOE), Department of Micro/nanoelectronics, Peking UniversitySchool of Electronics Engineering and Computer Science, Peking UniversityKey Laboratory of Microelectronic Devices and Circuits (MOE), Department of Micro/nanoelectronics, Peking UniversityKey Laboratory of Microelectronic Devices and Circuits (MOE), Department of Micro/nanoelectronics, Peking UniversityKey Laboratory of Microelectronic Devices and Circuits (MOE), Department of Micro/nanoelectronics, Peking UniversitySchool of Electronics Engineering and Computer Science, Peking UniversityKey Laboratory of Microelectronic Devices and Circuits (MOE), Department of Micro/nanoelectronics, Peking UniversityKey Laboratory of Microelectronic Devices and Circuits (MOE), Department of Micro/nanoelectronics, Peking UniversityDesigning 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.https://doi.org/10.1038/s41467-020-17215-3
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Qingxi Duan Zhaokun Jing Xiaolong Zou Yanghao Wang Ke Yang Teng Zhang Si Wu Ru Huang Yuchao Yang
spellingShingle	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 Nature Communications
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 Publishing Group
series	Nature Communications
issn	2041-1723
publishDate	2020-07-01
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.
url	https://doi.org/10.1038/s41467-020-17215-3
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Spiking neurons with spatiotemporal dynamics and gain modulation for monolithically integrated memristive neural networks

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