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

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.This work was supported by MINECO (Spain), Comunitat Autónoma de les Illes Balears, FEDER under Projects TEC2012-36335 (TRIPHOP), FIS2012-30634 (Intense@cosyp) and Grups Competitius. This work has been funded by the Ministerio de Economa y Competitividad, Spain, under project TEC2012-38864-C03-03 and co-financed by FEDER funds. The work of M.C.S. was supported by the Conselleria d’Educació, Cultura i Universitats del Govern de les Illes Balears and the European Social Fund.Peer Reviewe