Reservoir Computing Benchmarks: a tutorial review and critique
This work addresses the challenge of evaluating computational capacity in reservoir computing, which is incremental as it synthesizes existing benchmarks without introducing new methods.
The paper reviews and critiques evaluation methods in reservoir computing, categorizing benchmark tasks and analyzing their strengths and shortcomings to suggest improvements for the community.
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.