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

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 2 memristors. The volatile and positive/negative symmetric threshold switching characteristics of VO 2 memristors are leveraged to construct a sparse-spiking yet high-fidelity asynchronous spike encoder for physiological signals. Besides, the dynamical behavior of VO 2 memristors is utilized in compact Leaky Integrate and Fire (LIF) and…

• T
  Tencent
• NN
  National Natural Science Foundation of China

  Awards: 92064004, B18001, 61927901, 92164302, 61925401, 61925401, 92064004, 61927901, 92164302
• HE
  Higher Education Discipline Innovation Project

  Award: B18001