Temporal Pattern Coding in Ionic Memristor‐Based Spiking Neurons for Adaptive Tactile Perception. (6th July 2022)

• Record Type: Journal Article
• Authors: Xie, Zhuolin; Zhu, Xiaojian; Wang, Wei; Guo, Zhecheng; Zhang, Yuejun; Liu, Huiyuan; Sun, Cui; Tang, Minghua; Gao, Shuang; Li, Run‐Wei
• Is Part Of: Advanced Electronic Materials. Volume 8:Number 10(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-07-06
• Subjects: ionic dynamics -- memristors -- neuronal firing patterns -- spike frequency adaptation -- tactile perception; Materials -- Electric properties -- Periodicals; Materials science -- Periodicals; Magnetic materials -- Periodicals; Electronic apparatus and appliances -- Periodicals; 537
• DOI: 10.1002/aelm.202200334
• Languages: English
• ISSNs: 2199-160X

Abstract

Abstract: Biological neurons encode signals through firing voltage spike trains having unique temporal patterns, enabling efficient information representation and processing. Realization of these rich neuronal firing characteristics in a single electronic device, without circuitry and software assistance, promise compact and functional neuromorphic hardware for advanced artificial intelligence applications. Here, a Pt/Co3 O4‐x /ITO‐based ionic memristor is reported that can faithfully produce voltage spike trains exhibiting diverse temporal patterns of biological neurons, under electric current stimulation. The spiking behaviors stem from the redistribution of ions in the device, governed by the current induced electric field and Joule heating effects. Tonic, phasic, burst, and adaptive firing patterns of neurons are demonstrated. Particularly, the adaptive firing characteristics allow the memristor to reduce the response to invariant current stimulation and to respond to current changes with enhanced sensitivity, implementing neuronal adaptive coding function. Integrating such memristors with pressure sensors yields an artificial tactile sensory system that can adaptively perceive small pressure variations in the presence of strong static pressure backgrounds, enabling accurate identification of touched objects in ever‐changing environments. This work opens up an avenue toward advanced neuromorphic hardware for smart neural prosthetics and bionic robotics applications. Abstract : A Pt/Co3 O4‐x /ITO‐based ionic memristor with built‐in capabilities to generate voltage spikes exhibiting diverse neuronal firing patterns under electric current stimulation, is developed. The memristor naturally implements adaptive coding functions of biological neurons, endowing artificial tactile sensory systems with the ability to adaptively perceive and identify touched objects in ever‐changing environments.