The Dynamical Landscape of Reservoir Computing with Elementary Cellular Automata

Abstract

Reservoir Computing with Cellular Automata (ReCA) is a promising concept by virtue of its potential for efficient hardware implementation and theoretical understanding of Cellular Auotmata (CA). However, ReCA has so far only been studied in exploratory studies. In this work, we take a more in depth view of the landscape of Elementary Cellular Automata for Reservoir Computing. In this paper, the ReCA is applied to the X-bit memory benchmark with a thorough exploration for key parameters including number of random mappings (R), number of bits (Nb) and size of the vector that the random mapping is mapped to (Ld). Our evidence shows that the parameter space, including the full panoply of CA rules, is much richer then what previous evidence indicates. This suggests that some CA rules would require careful consideration and custom parameters setup.