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

Journal: Nature communications
Published Date: Jun 21, 2023

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 VO memristors. The volatile and positive/negative symmetric threshold switching characteristics of VO memristors are leveraged to construct a sparse-spiking yet high-fidelity asynchronous spike encoder for physiological signals. Besides, the dynamical behavior of VO 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.

Authors

  • Rui Yuan
    Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China.
  • Pek Jun Tiw
    Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China.
  • Lei Cai
    Department of Information Science and Technology, Beijing University of Chemical Technology, Beijing, People's Republic of China.
  • Zhiyu Yang
    School of Electronic and Computer Engineering, Peking University, Shenzhen, 518055, China.
  • Chang Liu
    Key Lab of Cell Differentiation and Apoptosis of Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Teng Zhang
    College of Veterinary Medicine, Hebei Agricultural University, Baoding, Hebei 071000, China.
  • Chen Ge
    Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, 100190, Beijing, China. gechen@iphy.ac.cn.
  • Ru Huang
    School of Information Science & Engineering, East China University of Science and Technology, Shanghai 200237, China.
  • Yuchao Yang
    Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China. yuchaoyang@pku.edu.cn.

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