神经形态工程学
记忆电阻器
计算机科学
接口(物质)
信号(编程语言)
信号处理
Spike(软件开发)
编码器
人工神经网络
人工智能
计算机体系结构
电子工程
计算机硬件
数字信号处理
工程类
最大气泡压力法
软件工程
操作系统
气泡
并行计算
程序设计语言
作者
Yuan Rui,Pek Jun Tiw,Lei Cai,Zhiyu Yang,Chang Liu,Teng Zhang,Ge Chen,Ru Huang,Yuchao Yang
标识
DOI:10.1038/s41467-023-39430-4
摘要
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 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.
科研通智能强力驱动
Strongly Powered by AbleSci AI