神经形态工程学
人工神经元
计算机科学
内容寻址存储器
记忆电阻器
联想学习
人工神经网络
结合属性
人工智能
电气工程
神经科学
数学
生物
工程类
纯数学
作者
Sang‐Heon Kim,Unhyeon Kang,Jiyoung Gu,Jaewook Kim,Jongkil Park,Gyu Weon Hwang,Seongsik Park,Hyun Jae Jang,Tae‐Yeon Seong,Suyoun Lee
标识
DOI:10.1021/acsami.4c02343
摘要
Associative multimodal artificial intelligence (AMAI) has gained significant attention across various fields, yet its implementation poses challenges due to the burden on computing and memory resources. To address these challenges, researchers have paid increasing attention to neuromorphic devices based on novel materials and structures, which can implement classical conditioning behaviors with simplified circuitry. Herein, we introduce an artificial multimodal neuron device that shows not only the acquisition behavior but also the extinction and the spontaneous recovery behaviors for the first time. Being composed of an ovonic threshold switch (OTS)-based neuron device, a conductive bridge memristor (CBM)-based synapse device, and a few passive electrical elements, such observed behaviors of this neuron device are explained in terms of the electroforming and the diffusion of metallic ions in the CBM. We believe that the proposed associative learning neuron device will shed light on the way of developing large-scale AMAI systems by providing inspiration to devise an associative learning network with improved energy efficiency.
科研通智能强力驱动
Strongly Powered by AbleSci AI