Associative memory realized by a reconfigurable memristive Hopfield neural network

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 th...

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Veröffentlicht in:Nature communications Jg. 6; H. 1; S. 7522
Hauptverfasser: Hu, S.G., Liu, Y., Liu, Z, Chen, T.P., Wang, J.J., Yu, Q., Deng, L.J., Yin, Y., Hosaka, Sumio
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 25.06.2015
Nature Publishing Group
Schlagworte:
639/766/25
Algorithms
Animals
Association Learning - physiology
Central Nervous System - physiology
Computer Simulation
Humanities and Social Sciences
Memory
multidisciplinary
Neural Networks (Computer)
Pattern Recognition, Automated
Science
Science (multidisciplinary)
ISSN:2041-1723, 2041-1723
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Zusammenfassung: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. Memristors are passive electrical components that can act like simple memories. Here, the authors use an array of hafnium oxide memristors to create a type of artificial neural network, known as a Hopfield network, that is capable of retrieving data from partial information
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ISSN:2041-1723
2041-1723
DOI:10.1038/ncomms8522