Associative memory realized by a reconfigurable memristive Hopfield neural network

S.G. Hu, Y. Liu (), Z Liu, T.P. Chen (), J.J. Wang, Q. Yu, L.J. Deng, Y. Yin and Sumio Hosaka
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S.G. Hu: State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China
Y. Liu: State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China
Z Liu: School of Materials and Energy, Guangdong University of Technology
T.P. Chen: School of Electrical and Electronic Engineering, Nanyang Technological University
J.J. Wang: State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China
Q. Yu: State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China
L.J. Deng: State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China
Y. Yin: Graduate School of Engineering, Gunma University
Sumio Hosaka: Graduate School of Engineering, Gunma University

Nature Communications, 2015, vol. 6, issue 1, 1-8

Abstract: Abstract Although synaptic behaviours of memristors have been widely demonstrated, implementation of an even simple artificial neural network is still a great challenge. In this work, we demonstrate the associative memory on the basis of a memristive Hopfield network. Different patterns can be stored into the memristive Hopfield network by tuning the resistance of the memristors, and the pre-stored patterns can be successfully retrieved directly or through some associative intermediate states, being analogous to the associative memory behaviour. Both single-associative memory and multi-associative memories can be realized with the memristive Hopfield network.

Date: 2015
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DOI: 10.1038/ncomms8522

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