L. Appeltant... et al.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.This research was partially supported by the Belgian Science Policy Office, under grant IAP P6-10 'photonics@be', by FWO and FRS–FNRS (Belgium), MICINN (Spain) under projects FISICOS (FIS2007-60327) and DeCoDicA (TEC2009-14101) and by the European project PHOCUS (EU FET-Open grant: 240763). L.A. and G.VdS. are a PhD Fellow and a Postdoctoral Fellow of the Research Foundation-Flanders (FWO).Peer reviewe
