Embedding theory of reservoir computing and reducing reservoir network using time delays

Abstract

The dynamics of reservoir computing (RC), a compact recurrent neural network, is validated as a higher-dimensional embedding of the input nonlinear dynamics. Based on this rigorous validation, a delayed RC is established with a significantly reduced network size and a promoted memory capacity, which can achieve dynamics reconstruction even in the reservoir with a single neuron.