Abstract
Elementary cellular automata (ECAs) generate critical spacetime patterns in a few local rules, which are expected to have advantages in reservoir computing (RC). However, previous studies have not revealed the advantages of critical spacetime patterns in RC. In this paper, we focus on the distractor’s length in the time series data for learning and clarify the advantages of the critical spacetime patterns. Furthermore, we propose asynchronously tuned ECAs (AT_ECAs) to generate universally critical spacetime patterns in many local rules. Based on the results achieved in this study, we propose RC based on AT_ECAs. Moreover, we show that the universal criticality of AT_ECAs is effective for learning time series data.
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