Generalization performance of reservoir computing implemented by asynchronously tuned elementary cellular automaton on parity task

Abstract Reservoir computing (RC) is a computer architecture allowing us to utilize any nonlinear dynamical system as a computing device called a reservoir. Exploiting elementary cellular automata (ECAs) as reservoirs is no exception. Recent studies have shown that the RC implemented by asynchronously tuned ECAs (AT_ECAs) has an advantage of enhancing the learning ability to identify multiple patterns, suggesting effects of critical spacetime patterns that the AT_ECAs create in a wide range of transition rules. However, the generalization capability of the AT_ECA-based RC remains unclear. This study evaluated the generalization performance of the AT_ECA-based RC using the temporal parity task in comparison with the ECA-based RC. We found that the AT_ECA-based RC demonstrated higher performance than ECA-based RC in most rules. This might have been a result of critical behaviors that the AT_ECAs universally generate with different mechanisms from the ECAs.