Reservoir Computing with Neuromorphic Nanowire Networks

Ruomin Zhu^a, Sam Lilak^b, Alon Loeffler^a, Joseph Lizier^c, Adam Stieg^b^,^d, James Gimzewski^b^,^d^,^e, Zdenka Kuncic^a^,^f

^aSchool of Physics, University of Sydney, Sydney, NSW, Australia

^bDepartment of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA, United States.

^cCentre for Complex Systems, Faculty of Engineering, The University of Sydney, Sydney, NSW, Australia.

^dWPI Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), Tsukuba, Japan.

^eResearch Center for Neuromorphic AI Hardware, Kyutech, Kitakyushu, Japan

^fUniversity of Sydney Nano Institute, Sydney, NSW, Australia

Proceedings of Neuromorphic Materials, Devices, Circuits and Systems (NeuMatDeCaS)

VALÈNCIA, Spain, 2023 January 23rd - 25th

Organizers: Rohit Abraham John, Irem Boybat, Jason Eshraghian and Simone Fabiano

Contributed talk, Ruomin Zhu, presentation 055
DOI: https://doi.org/10.29363/nanoge.neumatdecas.2023.055
Publication date: 9th January 2023

Nanowire Networks are a novel class of neuromorphic information processing hardware
devices, combining the advantage of synapse-like memristive cross-point junctions and
brain-like complex network topology. In addition to their low operating power, NWNs are
also easy to fabricate and scale up with bottom-up self-assembly. Using a reservoir
computing framework, NWNs have demonstrated the abilities to perform complex learning
tasks such as non-linear transformation, chaotic Mackey-Glass time-series prediction,
MNIST classification and MNIST digit reconstruction using significantly less amount of
training data. Moreover, we also investigated information theoretic metrics of mutual
information (MI), transfer entropy (TE) and active information storage (AIS) to help analyze
the dynamics in the NWNs during learning and optimize the performance. Overall, their
neural-like properties and information processing capabilities make them promising
candidate for neuromorphic computing systems.

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