Artificial Multimodal Neuron with Associative Learning Capabilities: Acquisition, Extinction, and Spontaneous Recovery

Sangheon Kim, Unhyeon Kang, Jiyoung Gu, Jaewook Kim, Jongkil Park, Gyu Weon Hwang, Seongsik Park, Hyun Jae Jang, Tae Yeon Seong, Suyoun Lee

    Research output: Contribution to journalArticlepeer-review

    1 Citation (Scopus)

    Abstract

    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.

    Original languageEnglish
    Pages (from-to)36519-36526
    Number of pages8
    JournalACS Applied Materials and Interfaces
    Volume16
    Issue number28
    DOIs
    Publication statusPublished - 2024 Jul 17

    Bibliographical note

    Publisher Copyright:
    © 2024 American Chemical Society.

    Keywords

    • artificial neuron
    • associative learning
    • classical conditioning
    • multimodal
    • neuromorphic device

    ASJC Scopus subject areas

    • General Materials Science

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