Information processing using a single dynamical node as complex system

TLDR

Novel methods for information processing are highly desired, and reservoir computing shows that complex networks can efficiently perform computation, inspired by the brain's ability to process information. The study introduces a novel architecture that reduces the typically large number of elements in reservoir computing to a single nonlinear node with delayed feedback. The authors demonstrate a simple delay dynamical system that can efficiently perform computations, replacing a complex network in reservoir computing. Experimental and numerical results show that a single nonlinear node with delayed feedback achieves excellent performance on speech recognition and time‑series prediction benchmarks, proving that even the simplest delay‑dynamical systems can perform efficient information processing and paving the way for resource‑efficient reservoir computing implementations.

Abstract

Novel methods for information processing are highly desired in our information-driven society. Inspired by the brain's ability to process information, the recently introduced paradigm known as 'reservoir computing' shows that complex networks can efficiently perform computation. Here we introduce a novel architecture that reduces the usually required large number of elements to a single nonlinear node with delayed feedback. Through an electronic implementation, we experimentally and numerically demonstrate excellent performance in a speech recognition benchmark. Complementary numerical studies also show excellent performance for a time series prediction benchmark. These results prove that delay-dynamical systems, even in their simplest manifestation, can perform efficient information processing. This finding paves the way to feasible and resource-efficient technological implementations of reservoir computing. The paradigm of reservoir computing shows that, like the human brain, complex networks can perform efficient information processing. Here, a simple delay dynamical system is demonstrated that can efficiently perform computations capable of replacing a complex network in reservoir computing.