par Appeltant, Lennert;Soriano, Miguel Cornelles;Van Der Sande, Guy 
;Danckaert, Jan;Massar, Serge ;Dambre, Joni;Schrauwen, Benjamin;Mirasso, Claudio R;Fischer, Ingo
Référence Nature communications, 2, page (6)
Publication Publié, 2011
;Danckaert, Jan;Massar, Serge ;Dambre, Joni;Schrauwen, Benjamin;Mirasso, Claudio R;Fischer, IngoRéférence Nature communications, 2, page (6)
Publication Publié, 2011
Article révisé par les pairs
| Résumé : | 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. |
