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 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.

Neuromorphic auditory classification based on a single dynamical electrochemical memristor

“…Specifically, the input signal was sampled at 10 Hz and the number of zero crossings within each sampling window were counted and used to produce a spike train with spike rate proportional to the number of zero crossings. The rate-based spike encoding process was done in software but could also be achieved by using an artificial neuron device [8]. The conductance change induced by the input signals was then directly used as tokens for different frequency-related classification tasks.…”

“…Therefore, it is a highly rewarding research thrust to build compact neural networks that embrace intrinsic network dynamics rather than monotonically upscaling network sizes (figure 1). Following this inspiration, spiking neural networks (SNNs) have been developed by encoding and processing information in both spatial and temporal domains [7][8][9][10]. Information processing inside SNNs changes from static, synchronized numerical computations into dynamical and event-driven process [11,12].…”

“…Static memristors based on two-terminal HfO 2 , Ta 2 O 5 devices have been used extensively in designing compute-in-memory hardware for acceleration of ANNs [19][20][21][22][23]. By changing switching material to phase change NbO 2 or VO 2 , volatile dynamic switching of these devices was observed and led to the development of memristor-based artificial neurons [6,8,[24][25][26][27]. Meanwhile, by inserting Ag clusters into the static memristors, dynamical switching was also achieved [28].…”

Reconfigurable Mott electronics for homogeneous neuromorphic platform

“…There was plenty of research about utilizing VO 2 as LIF neurons, [34][35][36] so we can also change the role of EC-VO 2 from synapses to neurons. As shown in Fig.…”

Fabrication of ZnO-encapsulated porous alumina structure utilizing a two-step anodizing technique for memristor applications

“…[1][2][3][4][5][6][7] In realizing an optimum hardware system for NC, the utilization of a memristor as the basic building block, which resembles the synapse cell of the human brain, is being pursued extensively due to its simplicity and versatility. [8][9][10][11] Up to now, various compound materials such as AlO x , 12) As 2 S 3 , 13) CuAg, 14) CuO x , 15) Ge 2 Se 3 , 16) HfO x , 17) MoO x , 18) NiO x , 19) SiO x , 20,21) SnS 2 , 22) TaO x , 23) TiO x , 24) VO 2 , 25) Y 2 O 3 , [26][27][28][29] ZnO, 30) and others are being exploited as the core part of the resistive changing medium of the memristors. However, reproducibility and stability of the device characteristic based on the memristor is still a challenging issue since the control of defects formation, which influences the electrical properties of the materials used for the resistive changing medium, is quite critical.…”