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

• Published in: Nature communications Vol. 14; no. 1; pp. 3695 - 14
• Main Authors: Yuan, Rui, Tiw, Pek Jun, Cai, Lei, Yang, Zhiyu, Liu, Chang, Zhang, Teng, Ge, Chen, Huang, Ru, Yang, Yuchao
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 21.06.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 142/126; 147/143; 639/301/1005/1007; 639/925/927/1007; Arrhythmia; Cardiac arrhythmia; Coders; Cognition; Decision making; Epilepsy; Firing pattern; Humanities and Social Sciences; Interfaces; Long short-term memory; Man-machine interfaces; Memristors; multidisciplinary; Neural networks; Physiology; Science; Science (multidisciplinary); Seizures; Signal generation; Signal processing; Spiking; Vanadium oxides
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-023-39430-4

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 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. Next-generation human-machine interfaces require efficient physiological signal processing systems. Here, the authors propose a hardware system that uses VO 2 memristors to perform brain-like encoding and analysis of physiological signals, and is capable of identifying arrhythmia and epileptic seizures.