Bio-plausible memristive neural components towards hardware implementation of brain-like intelligence. (January 2023)

• Record Type: Journal Article
• Authors: Sung, Sang Hyun; Jeong, Yujin; Oh, Jung Won; Shin, Hyeon-Jin; Lee, Jae Hee; Lee, Keon Jae
• Is Part Of: Materials today. Volume 62(2023)
• Page Start: 251
• Page End: 270
• Publication Date: 2023-01
• Subjects: Neuromorphic computing -- Bio-plausible mimicry -- Memristor -- Artificial synapse -- Artificial neuron; Materials science -- Periodicals; Metallurgy -- Periodicals; Metal-work -- Periodicals; Biomedical and Dental Materials -- Periodicals; Manufactured Materials -- Periodicals; Metals -- Periodicals; 620.11
• DOI: 10.1016/j.mattod.2022.11.022
• Languages: English
• ISSNs: 1369-7021

Abstract

Graphical abstract: Abstract: A memristor can comprehensively emulate the neural components rather than imitating a single characteristic superficially due to its analog and hysteretic resistive switching. Bio-plausible mimicry aims to emulate biological working mechanisms to implement the complicated functional characteristics of a neural network for artificial intelligence (AI). Bio-plausible neuromorphic device using memristor is a direct and efficient approach for the emulation of biological systems, contributing to the realization of brain-like intelligence beyond limited AI applications. In this article, we review recent progress in bio-plausible mimicry of neural components using memristive devices. Memristor-based artificial neurons, synapses, and nerve systems are discussed focusing on the analogy in the operation mechanisms. In addition, we explore the neurological and interdisciplinary approaches in neuronal mapping and brain-computer interfaces to place an emphasis on the relationship between memristive neuromorphic system and biological neural network.