DOI: https://doi.org/10.29363/nanoge.neumatdecas.2023.034
Publication date: 9th January 2023
Nanowire networks (NWNs) mimic the brain’s neuro-synaptic connectivity and emergent dynamics. Consequently, NWNs may also emulate the synaptic processes that enable higher-order cognitive functions such as learning and memory. Conductive filaments in NWNs form and decay at a different rate, enabling memory for previously activated junctions and pathways within the network. Here, we exploit this behaviour to train selective network pathways via a learning mechanism inspired by supervised learning in the brain. Using gradient descent to adjust voltages and current outputs of output drain electrodes, we train NWNs to associate inputs with a target drain electrode. Capitalizing on filament decay, we then test the network’s working memory performance on a simple cognitive task. Findings demonstrate the ability of NWNs to recall previously trained pathways, even after a series of interference patterns are presented to the network.
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