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.

翻译：储层计算是一种非常规计算模型，可在多种不同基底上执行计算，例如循环神经网络或物理材料。该方法采用"黑箱"策略，仅训练所构建系统的输出部分。因此，评估此类系统的计算能力颇具挑战性。本文对储层计算领域采用的评估方法进行了系统性回顾与批判性分析。我们提出了基准测试任务的分类体系，综述了文献中应用于储层计算的多类基准测试案例，并指出其优势与局限。最后，我们提出了改进基准测试及其应用方式的若干建议，以促进储层计算领域的持续发展。