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A neuromorphic physiological signal processing system based on VO2 memristor for next-generation human-machine interface

Author

Listed:
  • Rui Yuan

    (Peking University)

  • Pek Jun Tiw

    (Peking University)

  • Lei Cai

    (Peking University)

  • Zhiyu Yang

    (Peking University)

  • Chang Liu

    (Peking University)

  • Teng Zhang

    (Peking University)

  • Chen Ge

    (Chinese Academy of Sciences)

  • Ru Huang

    (Peking University)

  • Yuchao Yang

    (Peking University
    Peking University
    Peking University
    Chinese Institute for Brain Research (CIBR), Beijing)

Abstract

Physiological signal processing plays a key role in next-generation human-machine interfaces as physiological signals provide rich cognition- and health-related information. However, the explosion of physiological signal data presents challenges for traditional systems. Here, we propose a highly efficient neuromorphic physiological signal processing system based on VO2 memristors. The volatile and positive/negative symmetric threshold switching characteristics of VO2 memristors are leveraged to construct a sparse-spiking yet high-fidelity asynchronous spike encoder for physiological signals. Besides, the dynamical behavior of VO2 memristors is utilized in compact Leaky Integrate and Fire (LIF) and Adaptive-LIF (ALIF) neurons, which are incorporated into a decision-making Long short-term memory Spiking Neural Network. The system demonstrates superior computing capabilities, needing only small-sized LSNNs to attain high accuracies of 95.83% and 99.79% in arrhythmia classification and epileptic seizure detection, respectively. This work highlights the potential of memristors in constructing efficient neuromorphic physiological signal processing systems and promoting next-generation human-machine interfaces.

Suggested Citation

  • Rui Yuan & Pek Jun Tiw & Lei Cai & Zhiyu Yang & Chang Liu & Teng Zhang & Chen Ge & Ru Huang & Yuchao Yang, 2023. "A neuromorphic physiological signal processing system based on VO2 memristor for next-generation human-machine interface," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39430-4
    DOI: 10.1038/s41467-023-39430-4
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    References listed on IDEAS

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    1. Wei Yi & Kenneth K. Tsang & Stephen K. Lam & Xiwei Bai & Jack A. Crowell & Elias A. Flores, 2018. "Biological plausibility and stochasticity in scalable VO2 active memristor neurons," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
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    Cited by:

    1. Chang Liu & Pek Jun Tiw & Teng Zhang & Yanghao Wang & Lei Cai & Rui Yuan & Zelun Pan & Wenshuo Yue & Yaoyu Tao & Yuchao Yang, 2024. "VO2 memristor-based frequency converter with in-situ synthesize and mix for wireless internet-of-things," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    2. Shengbo Wang & Shuo Gao & Chenyu Tang & Edoardo Occhipinti & Cong Li & Shurui Wang & Jiaqi Wang & Hubin Zhao & Guohua Hu & Arokia Nathan & Ravinder Dahiya & Luigi Giuseppe Occhipinti, 2024. "Memristor-based adaptive neuromorphic perception in unstructured environments," Nature Communications, Nature, vol. 15(1), pages 1-13, December.

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