Work

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

Year: 2025

Type: article

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.

Source: Artificial Life and Robotics

DOI: 10.1007/s10015-025-01045-x

Authors: Kota Nakada, Kengo Takahashi, Daisuke Uragami, Ramon Alonso-Sanz, Andrew Adamatzky, Yuta Nishiyama

Institutions: Nagaoka University of Technology, Nihon University, Universidad Politécnica de Madrid, University of the West of England

Language: English

Topic: Neural Networks and Reservoir Computing

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,190

OpenAlex ID: W4413065223