journal article·2021-04

Biologically Plausible Artificial Synaptic Array: Replicating Ebbinghaus’ Memory Curve with Selective Attention

Dong Gue Roe, Seongchan Kim, Yoon Young Choi, Hwije Woo, Moon Sung Kang, Young Jae Song, Jong‐Hyun Ahn, Yoonmyung Lee, Jeong Ho Cho

Advanced Materials· Volume 33· Issue 14
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Abstract

Abstract The nature of repetitive learning and oblivion of memory enables humans to effectively manage vast amounts of memory by prioritizing information for long‐term storage. Inspired by the memorization process of the human brain, an artificial synaptic array is presented, which mimics the biological memorization process by replicating Ebbinghaus’ forgetting curve. To construct the artificial synaptic array, signal‐transmitting access transistors and artificial synaptic memory transistors are designed using indium–gallium–zinc‐oxide and poly(3‐hexylthiophene), respectively. To secure the desired performance of the access transistor in regulating the input signal to the synaptic transistor, the content of gallium in the access transistor is optimized. In addition, the operation voltage of the synaptic transistor is carefully selected to achieve memory‐state efficiency. Repetitive learning characterizing Ebbinghaus’ oblivion curves is realized using an artificial synaptic array with optimized conditions for both transistor components. This successfully demonstrates a biologically plausible memorization process. Furthermore, selective attention for information prioritization in the human brain is mimicked by selectively applying repetitive learning to a synaptic transistor with a high memory state. The demonstrated biologically plausible artificial synaptic array provides great scope for advancement in bioinspired electronics.
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Research context

OpenAlex topics: Advanced Memory and Neural Computing · Neural dynamics and brain function · Neuroscience and Neural Engineering

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea

SKKU Advanced Institute of Nanotechnology (SAINT) Sungkyunkwan University Suwon 16419 Republic of Korea

Department of Chemical and Biomolecular Engineering Yonsei University Seoul 03722 Republic of Korea

Department of Chemical and Biomolecular Engineering Institute of Emergent Materials Sogang University Seoul 04107 South Korea

Department of Electrical and Computer Engineering Sungkyunkwan University Suwon 16419 South Korea

Funding

National Research Foundation · National Science Foundation, United Arab Emirates · Ministry of Science, ICT and Future Planning

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