Time-multiplexed Reservoir Computing with Percolating Networks of Nanoparticles
Joshua B. Mallinson, S. A. Brown · 2023
Percolating Networks of Nanoparticles (PNNs) are under investigation as candidates for physical implementations of reservoir computing (RC). Several promising features have been identified in PNNs, such as brain-like critical dynamics, long-range spatiotemporal correlations and nonlinear I-V characteristics. However, the information processing capability of PNNs remains to be demonstrated. Here we present detailed modelling of PNNs operating in the tunneling regime as delayed-dynamical reservoirs (DDRs). The computational capacity of these reservoirs is successfully demonstrated by their performance in two benchmark tasks: waveform discrimination and tenth-order nonlinear auto-regressive moving average (NARMA10) time series prediction. Furthermore, the interplay between the PNN response time and the delayed feedback is elucidated, providing valuable insight for future DDR design.