Neuromorphic learning, working memory, and metaplasticity in nanowire networks
Alon Loeffler, Adrian Diaz‐Alvarez, Ruomin Zhu, Natesh Ganesh, James M. Shine, Tomonobu Nakayama, Zdenka Kuncic
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.