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Associative memory realized by a reconfigurable memristive Hopfield neural network

Author

Listed:
  • 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)

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.

Suggested Citation

  • S.G. Hu & Y. Liu & Z Liu & T.P. Chen & J.J. Wang & Q. Yu & L.J. Deng & Y. Yin & Sumio Hosaka, 2015. "Associative memory realized by a reconfigurable memristive Hopfield neural network," Nature Communications, Nature, vol. 6(1), pages 1-8, November.
    • Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms8522
    DOI: 10.1038/ncomms8522
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    Cited by:

    1. Avcı, İbrahim & Lort, Hüseyin & Tatlıcıoğlu, Buğce E., 2023. "Numerical investigation and deep learning approach for fractal–fractional order dynamics of Hopfield neural network model," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    2. Yongxiang Li & Shiqing Wang & Ke Yang & Yuchao Yang & Zhong Sun, 2024. "An emergent attractor network in a passive resistive switching circuit," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    3. Lin, Hairong & Wang, Chunhua & Sun, Jingru & Zhang, Xin & Sun, Yichuang & Iu, Herbert H.C., 2023. "Memristor-coupled asymmetric neural networks: Bionic modeling, chaotic dynamics analysis and encryption application," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).

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