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Artificial Multimodal Neuron with Associative Learning Capabilities: Acquisition, Extinction, and Spontaneous Recovery.
Kim, Sangheon; Kang, Unhyeon; Gu, Jiyoung; Kim, Jaewook; Park, Jongkil; Hwang, Gyu Weon; Park, Seongsik; Jang, Hyun Jae; Seong, Tae-Yeon; Lee, Suyoun.
Afiliación
  • Kim S; Center for Neuromorphic Engineering, Korea Institute of Science and Technology, Seoul 02792, Rep. of Korea.
  • Kang U; Department of Materials Science and Engineering, Korea University, Seoul 02841, Rep. of Korea.
  • Gu J; Center for Neuromorphic Engineering, Korea Institute of Science and Technology, Seoul 02792, Rep. of Korea.
  • Kim J; Materials Science & Engineering, Seoul National University, Seoul 08826, Rep. of Korea.
  • Park J; Center for Neuromorphic Engineering, Korea Institute of Science and Technology, Seoul 02792, Rep. of Korea.
  • Hwang GW; Department of Materials Science & Engineering, Seoul National University of Science and Technology, Seoul 01811, Rep. of Korea.
  • Park S; Center for Neuromorphic Engineering, Korea Institute of Science and Technology, Seoul 02792, Rep. of Korea.
  • Jang HJ; Center for Neuromorphic Engineering, Korea Institute of Science and Technology, Seoul 02792, Rep. of Korea.
  • Seong TY; Center for Neuromorphic Engineering, Korea Institute of Science and Technology, Seoul 02792, Rep. of Korea.
  • Lee S; Center for Neuromorphic Engineering, Korea Institute of Science and Technology, Seoul 02792, Rep. of Korea.
ACS Appl Mater Interfaces ; 16(28): 36519-36526, 2024 Jul 17.
Article en En | MEDLINE | ID: mdl-38950119
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.
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Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: ACS Appl Mater Interfaces Asunto de la revista: BIOTECNOLOGIA / ENGENHARIA BIOMEDICA Año: 2024 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: ACS Appl Mater Interfaces Asunto de la revista: BIOTECNOLOGIA / ENGENHARIA BIOMEDICA Año: 2024 Tipo del documento: Article