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A substrate-independent framework to characterize reservoir computers

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JournalProceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
DateAccepted/In press - 15 May 2019
DatePublished (current) - 19 Jun 2019
Issue number2226
Original languageEnglish


The Reservoir Computing (RC) framework states that any non-linear, input-driven dynamical system (the reservoir) exhibiting properties such as a fading memory and input separability can be trained to perform computational tasks. This broad inclusion of systems has led to many new physical substrates for RC. Properties essential for reservoirs to compute are tuned through reconfiguration of the substrate, such as change in virtual topology or physical morphology. As a result, each substrate possesses a unique "quality" - obtained through reconfiguration - to realise different reservoirs for different tasks. Here we describe an experimental framework to characterise the quality of potentially \textit{any} substrate for RC. Our framework reveals that a definition of quality is not only useful to compare substrates, but can help map the non-trivial relationship between properties and task performance. In the wider context, the framework offers a greater understanding as to what makes a dynamical system compute, helping improve the design of future substrates for RC.

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    Research areas

  • unconventional computing, evolution in materio, reservoir computing, Carbon Nanotubes (CNTs), Characterization, Physical computation, Reservoir computing

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