CC BY 4.0 UnportedKöster, FelixYanchuk, SerhiyLüdge, Kathy2023-02-132023-02-132022https://oa.tib.eu/renate/handle/123456789/11426http://dx.doi.org/10.34657/10460We show that many delay-based reservoir computers considered in the literature can be characterized by a universal master memory function (MMF). Once computed for two independent parameters, this function provides linear memory capacity for any delay-based single-variable reservoir with small inputs. Moreover, we propose an analytical description of the MMF that enables its efficient and fast computation. Our approach can be applied not only to single-variable delay-based reservoirs governed by known dynamical rules, such as the Mackey–Glass or Stuart–Landau-like systems, but also to reservoirs whose dynamical model is not available.enghttps://creativecommons.org/licenses/by/4.0004Machine learningnonlinear dynamicsreservoir computingArticle