ARTICLE

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 effi ciently 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 effi cient information processing. This fi nding paves the way to feasible and resource-effi cient technological implementations of reservoir computing