  Atomic Switch Networks (ASN) 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 (RC). 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 (FSDD). 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.
