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...

Full description

Saved in:
Bibliographic Details
Published in:Scientific reports 2015-10, Vol.5 (1), p.14945-14945, Article 14945
Main Authors: Ortín, S, Soriano, M C, Pesquera, L, Brunner, D, San-Martín, D, Fischer, I, Mirasso, C R, Gutiérrez, J M
Format: Article
Language:English
Subjects:
Computers
Connectivity
Expected values
Feedback
Humans
Information processing
Learning
Machine Learning
Neural networks
Neural Networks (Computer)
Neurons
Neurons - physiology
Nonlinear Dynamics
Software
User-Computer Interface
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2045-2322
2045-2322
DOI:10.1038/srep14945