  Networks composed of nanoscale memristive components, such as nanowire and
nanoparticle networks, have recently received considerable attention because of
their potential use as neuromorphic devices. In this study, we explore the
connection between ergodicity in memristive and nanowire networks, showing that
the performance of reservoir devices improves when these networks are tuned to
operate at the edge between two global stability points. The lack of ergodicity
is associated with the emergence of memory in the system. We measure the level
of ergodicity using the Thirumalai-Mountain metric, and we show that in the
absence of ergodicity, two memristive systems show improved performance when
utilized as reservoir computers (RC). In particular, we highlight that it is
also important to let the system synchronize to the input signal in order for
the performance of the RC to exhibit improvements over the baseline.

Disordered Systems and Neural Networks;Statistical Mechanics