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A Unified Framework for Reservoir Computing and Extreme Learning Machines based on a Single Time-delayed Neuron

Abstract: In this paper we present a unified framework for extreme learning machines and reservoir computing (echo state networks), which can be physically implemented using a single nonlinear neuron subject to delayed feedback. The reservoir is built within the delay-line, employing a number of “virtual” neurons. These virtual neurons receive random projections from the input layer containing the information to be processed. One key advantage of this approach is that it can be implemented efficiently in hardware. We show that the reservoir computing implementation, in this case optoelectronic, is also capable to realize extreme learning machines, demonstrating the unified framework for both schemes in software as well as in hardware.

Otras publicaciones de la misma revista o congreso con autores/as de la Universidad de Cantabria

 Fuente: Scientific Reports 5, Article number: 14945 (2015)

Editorial: Nature Publishing Group

 Fecha de publicación: 01/10/2015

Nº de páginas: 11

Tipo de publicación: Artículo de Revista

 DOI: 10.1038/srep14945

ISSN: 2045-2322

Url de la publicación: https://doi.org/10.1038/srep14945

Autores/as

SILVIA ORTIN GONZALEZ

SORIANO, M. C.

FISCHER, I.

MIRASSO, C. R.