Reservoir Computing Benchmarks: a tutorial review and critique

TL;DR

This paper reviews and critiques the evaluation methods for reservoir computing, categorizing benchmark tasks, analyzing existing benchmarks, and proposing improvements to enhance the assessment of these systems.

Contribution

It provides a comprehensive review of reservoir computing benchmarks, introduces a categorization scheme, and offers suggestions for improving evaluation practices.

Findings

Existing benchmarks have notable strengths and shortcomings.

A categorization of benchmark tasks clarifies evaluation approaches.

Proposed improvements aim to better assess reservoir computing systems.

Abstract

Reservoir Computing is an Unconventional Computation model to perform computation on various different substrates, such as recurrent neural networks or physical materials. The method takes a 'black-box' approach, training only the outputs of the system it is built on. As such, evaluating the computational capacity of these systems can be challenging. We review and critique the evaluation methods used in the field of reservoir computing. We introduce a categorisation of benchmark tasks. We review multiple examples of benchmarks from the literature as applied to reservoir computing, and note their strengths and shortcomings. We suggest ways in which benchmarks and their uses may be improved to the benefit of the reservoir computing community.