Optimal nonlinear information processing capacity in delay-based reservoir computers

Item Type Journal paper
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.
Authors Grigoryeva, Lyudmila; Henriques, Julie; Larger, Laurent & Ortega, Juan-Pablo
Journal or Publication Title Scientific Reports
Language English
Subjects other research area
HSG Classification contribution to scientific community
HSG Profile Area SEPS - Quantitative Economic Methods
Refereed Yes
Date 11 September 2015
Publisher Macmillan Publishers Limited
Place of Publication [London]
Volume 5
Page Range 1-11
ISSN-Digital 2045-2322
Publisher DOI https://doi.org/10.1038/srep12858
Depositing User Prof. Ph.D Juan-Pablo Ortega Lahuerta
Date Deposited 21 Nov 2016 18:24
Last Modified 20 Jul 2022 17:29
URI: https://www.alexandria.unisg.ch/publications/249736

Download

[img]
Preview
 Text
RC2_PV_full.pdf
Download (10MB) | Preview

Citation

Grigoryeva, Lyudmila; Henriques, Julie; Larger, Laurent & Ortega, Juan-Pablo (2015) Optimal nonlinear information processing capacity in delay-based reservoir computers. Scientific Reports, 5 1-11.

Statistics

https://www.alexandria.unisg.ch/id/eprint/249736
Edit item Edit item
Feedback?