AUTHOR=Lilak Sam , Woods Walt , Scharnhorst Kelsey , Dunham Christopher , Teuscher Christof , Stieg Adam Z. , Gimzewski James K. TITLE=Spoken Digit Classification by In-Materio Reservoir Computing With Neuromorphic Atomic Switch Networks JOURNAL=Frontiers in Nanotechnology VOLUME=3 YEAR=2021 URL=https://www.frontiersin.org/journals/nanotechnology/articles/10.3389/fnano.2021.675792 DOI=10.3389/fnano.2021.675792 ISSN=2673-3013 ABSTRACT=

Atomic Switch Networks comprising silver iodide (AgI) junctions, a material previously unexplored as functional memristive elements within highly interconnected nanowire networks, were employed as a neuromorphic substrate for physical Reservoir Computing This new class of ASN-based devices has been physically characterized and utilized to classify spoken digit audio data, demonstrating the utility of substrate-based device architectures where intrinsic material properties can be exploited to perform computation in-materio. This work demonstrates high accuracy in the classification of temporally analyzed Free-Spoken Digit Data These results expand upon the class of viable memristive materials available for the production of functional nanowire networks and bolster the utility of ASN-based devices as unique hardware platforms for neuromorphic computing applications involving memory, adaptation and learning.