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

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Article number2007782
JournalAdvanced Materials
Volume33
Issue number14
DOIs
Publication statusPublished - 2021 Apr 8

Bibliographical note

Funding Information:
D.G.R. and S.K. contributed equally this work. This work was supported by a grant from the Basic Science Research Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Science, ICT & Future Planning (2020R1A2C2007819, 2020R1A4A2002806, and 2020M3F3A2A01085756), and the Creative Materials Discovery Program (2019M3D1A1078299) through the NRF of Korea funded by the Ministry of Science and ICT, Korea.

Publisher Copyright:
© 2021 Wiley-VCH GmbH

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Mechanics of Materials
  • Mechanical Engineering

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