Online dynamical learning and sequence memory with neuromorphic nanowire networks

Ruomin Zhu (), Sam Lilak, Alon Loeffler, Joseph Lizier, Adam Stieg (), James Gimzewski () and Zdenka Kuncic ()
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Ruomin Zhu: The University of Sydney
Sam Lilak: University of California, Los Angeles
Alon Loeffler: The University of Sydney
Joseph Lizier: The University of Sydney
Adam Stieg: University of California, Los Angeles
James Gimzewski: University of California, Los Angeles
Zdenka Kuncic: The University of Sydney

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract Nanowire Networks (NWNs) belong to an emerging class of neuromorphic systems that exploit the unique physical properties of nanostructured materials. In addition to their neural network-like physical structure, NWNs also exhibit resistive memory switching in response to electrical inputs due to synapse-like changes in conductance at nanowire-nanowire cross-point junctions. Previous studies have demonstrated how the neuromorphic dynamics generated by NWNs can be harnessed for temporal learning tasks. This study extends these findings further by demonstrating online learning from spatiotemporal dynamical features using image classification and sequence memory recall tasks implemented on an NWN device. Applied to the MNIST handwritten digit classification task, online dynamical learning with the NWN device achieves an overall accuracy of 93.4%. Additionally, we find a correlation between the classification accuracy of individual digit classes and mutual information. The sequence memory task reveals how memory patterns embedded in the dynamical features enable online learning and recall of a spatiotemporal sequence pattern. Overall, these results provide proof-of-concept of online learning from spatiotemporal dynamics using NWNs and further elucidate how memory can enhance learning.

Date: 2023
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DOI: 10.1038/s41467-023-42470-5

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