Ergodicity, lack thereof, and the performance of reservoir computing with memristive networks and nanowire
- Royal Melbourne Institute of Technology (RMIT), VIC (Australia)
- Univ. of Sydney, NSW (Australia)
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
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 ergodicity in memristive networks, showing that the performance on machine leaning tasks improves when these networks are tuned to operate at the edge between two global stability points. We find this 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 different memristive network systems show improved performance when utilized as reservoir computers (RC). 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.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)
- DOE Contract Number:
- 89233218CNA000001
- OSTI ID:
- 2386906
- Report Number(s):
- LA-UR--23-30482
- Country of Publication:
- United States
- Language:
- English
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Ergodicity, lack thereof, and the performance of reservoir computing with memristive networks
Journal Article
·
2024
· Nano Express
·
OSTI ID:2323572