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Neuromorphic Computing: Controllable Multiple Depression in a Graphene Oxide Artificial Synapse (Adv. Electron. Mater. 1/2017)
Author(s) -
Wang Laiyuan,
Wang Zhiyong,
Zhao Wei,
Hu Bo,
Xie Linghai,
Yi Mingdong,
Ling Haifeng,
Zhang Chenxi,
Chen Yan,
Lin Jinyi,
Zhu Jialu,
Huang Wei
Publication year - 2017
Publication title -
advanced electronic materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.25
H-Index - 56
ISSN - 2199-160X
DOI - 10.1002/aelm.201770001
Subject(s) - neuromorphic engineering , graphene , materials science , oxide , synapse , planar , nanotechnology , polarization (electrochemistry) , ionic bonding , computer science , artificial neural network , artificial intelligence , optoelectronics , computer architecture , neuroscience , ion , psychology , physics , chemistry , quantum mechanics , computer graphics (images) , metallurgy
Interdisciplinary ideas have been implemented in diversified memristive elements to emulate synaptic activities for application in neuromorphic computing, as reported by L. Wang et al. in article number 1600244. They used graphene oxide (GO), with its abundant ionic polarization characteristics, to construct a planar artificial synapse. Based on controllable inner polarization, advanced activity‐dependent depression and their transforms were implemented effectively.