Information processing using a single dynamical node as complex system

L. Appeltant, M.C. Soriano, G. Van der Sande, J. Danckaert, S. Massar, J. Dambre, B. Schrauwen, C.R. Mirasso and I. Fischer ()
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L. Appeltant: Applied Physics Research Group (APHY), Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussel, Belgium.
M.C. Soriano: Instituto de Física Interdisciplinar y Sistemas Complejos, IFISC (UIB-CSIC), Campus Universitat de les Illes Balears
G. Van der Sande: Applied Physics Research Group (APHY), Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussel, Belgium.
J. Danckaert: Applied Physics Research Group (APHY), Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussel, Belgium.
S. Massar: Laboratoire d'Information Quantique, CP 225, Université Libre de Bruxelles, Boulevard du Triomphe
J. Dambre: Ghent University, St Pietersnieuwstraat 41, B-9000 Ghent, Belgium.
B. Schrauwen: Ghent University, St Pietersnieuwstraat 41, B-9000 Ghent, Belgium.
C.R. Mirasso: Instituto de Física Interdisciplinar y Sistemas Complejos, IFISC (UIB-CSIC), Campus Universitat de les Illes Balears
I. Fischer: Instituto de Física Interdisciplinar y Sistemas Complejos, IFISC (UIB-CSIC), Campus Universitat de les Illes Balears

Nature Communications, 2011, vol. 2, issue 1, 1-6

Abstract: 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.

Date: 2011
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DOI: 10.1038/ncomms1476

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