Matches in SemOpenAlex for { <https://semopenalex.org/work/W4366603664> ?p ?o ?g. }
- W4366603664 abstract "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." @default.
- W4366603664 created "2023-04-23" @default.
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- W4366603664 date "2023-04-21" @default.
- W4366603664 modified "2023-10-04" @default.
- W4366603664 title "Neuromorphic learning, working memory, and metaplasticity in nanowire networks" @default.
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- W4366603664 doi "https://doi.org/10.1126/sciadv.adg3289" @default.
- W4366603664 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37083527" @default.
- W4366603664 hasPublicationYear "2023" @default.
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