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This paper presents a novel approach to Echo State Networks (ESNs) by integrating state-feedback with switching systems theory and the computational efficiency of Reservoir Computing architectures. We introduce an innovative architecture for ESNs, featuring a switching input weight and state-feedback gain, applicable to both linear and non-linear reservoirs. The stability of this architecture is rigorously analysed using the standard Lyapunov function construction method. Additionally, we explore the dynamical properties of a state-feedback ESN architecture used in the literature. The advantages of the proposed switching ESN is demonstrated using electrophysiological recordings in fruitfly photoreceptors.
Lupascu et al. (Thu,) studied this question.