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Neuromorphic Computing: Rectification‐Regulated Memristive Characteristics in Electron‐Type CuPc‐Based Element for Electrical Synapse (Adv. Electron. Mater. 7/2017)
Author(s) -
Wang Laiyuan,
Yang Jie,
Zhu Ying,
Yi Mingdong,
Xie Linghai,
Ju Ruolin,
Wang Zhiyong,
Liu Lutao,
Li Tengfei,
Zhang Chenxi,
Chen Yan,
Wu Yanan,
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.201770027
Subject(s) - neuromorphic engineering , memristor , synapse , materials science , rectification , electrical synapses , excitatory postsynaptic potential , artificial neural network , excitatory synapse , transmission (telecommunications) , computer science , neuroscience , nanotechnology , optoelectronics , artificial intelligence , electronic engineering , electrical engineering , engineering , chemistry , telecommunications , inhibitory postsynaptic potential , voltage , biology , biochemistry , intracellular , gap junction
Chemical and electrical synapses are the basis for synaptic transmission in a neural network. Presently, recreations of such networks focus on the plasticity of the chemical synapse. As a network cannot effectively function without the significant signal transmission of electrical synapses, it is thus necessary to construct an artificial electrical synapse before comprehensive neuromorphic computations are possible. To address this, in article number 1700063 , Mingdong Yi, Wei Huang, and co‐workers integrate the rectifying characteristics of an electron‐type organic CuPc‐based memristor into hysteretic behaviours, and construct an excitatory rectifying synapse.