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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| Vydáno v: | Nature communications Ročník 6; číslo 1; s. 7522 |
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| Hlavní autoři: | , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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London
Nature Publishing Group UK
25.06.2015
Nature Publishing Group |
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| ISSN: | 2041-1723, 2041-1723 |
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| 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.
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 |
|---|---|
| AbstractList | 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. 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 |
| ArticleNumber | 7522 |
| Author | Hosaka, Sumio Chen, T.P. Liu, Z Deng, L.J. Yu, Q. Liu, Y. Yin, Y. Wang, J.J. Hu, S.G. |
| Author_xml | – sequence: 1 givenname: S.G. surname: Hu fullname: Hu, S.G. organization: State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China – sequence: 2 givenname: Y. surname: Liu fullname: Liu, Y. email: yliu1975@uestc.edu.cn organization: State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China – sequence: 3 givenname: Z surname: Liu fullname: Liu, Z organization: School of Materials and Energy, Guangdong University of Technology – sequence: 4 givenname: T.P. surname: Chen fullname: Chen, T.P. email: echentp@ntu.edu.sg organization: School of Electrical and Electronic Engineering, Nanyang Technological University – sequence: 5 givenname: J.J. surname: Wang fullname: Wang, J.J. organization: State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China – sequence: 6 givenname: Q. surname: Yu fullname: Yu, Q. organization: State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China – sequence: 7 givenname: L.J. surname: Deng fullname: Deng, L.J. organization: State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China – sequence: 8 givenname: Y. surname: Yin fullname: Yin, Y. organization: Graduate School of Engineering, Gunma University – sequence: 9 givenname: Sumio surname: Hosaka fullname: Hosaka, Sumio organization: Graduate School of Engineering, Gunma University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26108993$$D View this record in MEDLINE/PubMed |
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| Title | Associative memory realized by a reconfigurable memristive Hopfield neural network |
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