  Reservoir Computing is an Unconventional Computation model to perform
computation on various different substrates, such as RNNs 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
