Highly Compact Artificial Memristive Neuron with Low Energy Consumption

• DOI: 10.1002/smll.201802188
• Publication Date: 2018-11-14T15:25:05Z
• Authors: Yishu Zhang; Wei He; Yujie Wu; Kejie Huang; Yangshu Shen; Jiasheng Su; Yaoyuan Wang; Ziyang Zhang; Xinglong Ji; Guoqi Li; Hongtao Zhang; Sen Song; Huanglong Li; Litao Sun; Rong Zhao; Luping Shi
• Keywords: Neuromorphic engineering; Memristor; Realization (probability); Artificial neuron

Abstract Neuromorphic systems aim to implement large‐scale artificial neural network on hardware ultimately realize human‐level intelligence. The recent development of nonsilicon nanodevices has opened the huge potential full memristive networks (FMNN), consisting neurons and synapses, for neuromorphic applications. Unlike widely reported devices less progress. Sophisticated dynamics is major obstacle behind lagging. Here a rich dynamics‐driven neuron demonstrated, which successfully emulates partial essential features processing, including leaky integration, automatic threshold‐driven fire, self‐recovery, in unified manner. realization bioplausible single device with ultralow power consumption paves way constructing energy‐efficient FMNN may boost high density, low power, fast speed.