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• W2163062501 abstract "Reservoir computing is a recently introduced brain-inspired machine learning paradigm capable of excellent performances in the processing of empirical data. We focus in a particular kind of time-delay based reservoir computers that have been physically implemented using optical and electronic systems and have shown unprecedented data processing rates. Reservoir computing is well-known for the ease of the associated training scheme but also for the problematic sensitivity of its performance to architecture parameters. This article addresses the reservoir design problem, which remains the biggest challenge in the applicability of this information processing scheme. More specifically, we use the information available regarding the optimal reservoir working regimes to construct a functional link between the reservoir parameters and its performance. This function is used to explore various properties of the device and to choose the optimal reservoir architecture, thus replacing the tedious and time consuming parameter scannings used so far in the literature." @default.
• W2163062501 created "2016-06-24" @default.
• W2163062501 creator A5072304172 @default.
• W2163062501 creator A5075053811 @default.
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• W2163062501 date "2015-09-11" @default.
• W2163062501 modified "2023-10-05" @default.
• W2163062501 title "Optimal nonlinear information processing capacity in delay-based reservoir computers" @default.
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• W2163062501 doi "https://doi.org/10.1038/srep12858" @default.
• W2163062501 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4566100" @default.
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• W2163062501 hasPublicationYear "2015" @default.
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