Master Memory Function for Delay-Based Reservoir Computers With Single-Variable Dynamics
Felix Köster, Serhiy Yanchuk, Kathy Lüdge · IEEE Transactions on Neural Networks and Learning Systems · 2022
We 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.