Reservoir Computing Using Non-Uniform Binary Cellular Automata

TL;DR

This paper explores the use of non-uniform binary cellular automata as reservoirs in reservoir computing, demonstrating that rule selection, reservoir size, and rule combinations significantly impact performance.

Contribution

It investigates non-uniform CA reservoirs, showing their potential for improved performance and design insights in reservoir computing systems.

Findings

Certain CA rules outperform others in reservoir tasks.

Increasing CA reservoir size enhances performance.

Non-uniform CA rule combinations can be highly effective.

Abstract

The Reservoir Computing (RC) paradigm utilizes a dynamical system, i.e., a reservoir, and a linear classifier, i.e., a read-out layer, to process data from sequential classification tasks. In this paper the usage of Cellular Automata (CA) as a reservoir is investigated. The use of CA in RC has been showing promising results. In this paper, selected state-of-the-art experiments are reproduced. It is shown that some CA-rules perform better than others, and the reservoir performance is improved by increasing the size of the CA reservoir itself. In addition, the usage of parallel loosely coupled CA-reservoirs, where each reservoir has a different CA-rule, is investigated. The experiments performed on quasi-uniform CA reservoir provide valuable insights in CA reservoir design. The results herein show that some rules do not work well together, while other combinations work remarkably well. This suggests that non-uniform CA could represent a powerful tool for novel CA reservoir implementations.