| Literature DB >> 33644934 |
Dong Gue Roe1, Seongchan Kim2, Yoon Young Choi3, Hwije Woo2, Moon Sung Kang4, Young Jae Song2, Jong-Hyun Ahn1, Yoonmyung Lee5, Jeong Ho Cho3.
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.Entities:
Keywords: artificial synapses; bioinspiration; memorization; multi-states; repetitive learning; synapse arrays
Year: 2021 PMID: 33644934 DOI: 10.1002/adma.202007782
Source DB: PubMed Journal: Adv Mater ISSN: 0935-9648 Impact factor: 30.849