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Reservoir Computing Benchmarks: a tutorial review and critique
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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.
Forward citations
Cited by 2 Pith papers
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7-Methylquinolinium Iodobismuthate Memristor: Exploring Plasticity and Memristive Properties for Digit Classification in Physical Reservoir Computing
A lead-free bismuth-halide memristor is reported as a physical reservoir, with claimed MNIST accuracy of 82.26%, but the classification equations omit the reservoir outputs.
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Re-purposing a modular origami manipulator into an adaptive physical computer for machine learning and robotic perception
A modular origami manipulator can act as a trainable physical reservoir computer, and its task performance tracks two simple spectral and spatial correlation metrics.
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