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Nanowire networks (NWNs) mimic the brain’s neurosynaptic connectivity and emergent dynamics. Consequently, NWNs may also emulate the synaptic processes that enable higher-order cognitive functions such as learning and memory. A quintessential cognitive task used to measure human working memory is the n-back task. In this study, task variations inspired by the n-back task are implemented in a NWN device, and external feedback is applied to emulate brain-like supervised and reinforcement learning. NWNs are found to retain information in working memory to at least n = 7 steps back, remarkably similar to the originally proposed “seven plus or minus two” rule for human subjects. Simulations elucidate how synapse-like NWN junction plasticity depends on previous synaptic modifications, analogous to “synaptic metaplasticity” in the brain, and how memory is consolidated via strengthening and pruning of synaptic conductance pathways.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: Neuromorphic, Silver nanowire network, neurosynaptic connectivity, emergent dynamics, metaplasticity, learning, memory

刊行年月日: 2023-04-21

出版者: AAAS

掲載誌:

• Science Advances (ISSN: 23752548) vol. 9 p. 1-14

研究助成金:

原稿種別: 論文以外のデータ

MDR DOI:

公開URL: https://doi.org/10.1126/sciadv.adg3289

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更新時刻: 2024-01-05 22:11:55 +0900

MDRでの公開時刻: 2023-04-27 16:07:56 +0900