Optimal nonlinear information processing capacity in delay-based reservoir computers
Journal
Scientific Reports
ISSN-Digital
2045-2322
Type
journal article
Date Issued
2015-09-11
Author(s)
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.
Language
English
HSG Classification
contribution to scientific community
HSG Profile Area
SEPS - Quantitative Economic Methods
Refereed
Yes
Publisher
Macmillan Publishers Limited
Publisher place
[London]
Volume
5
Start page
1
End page
11
Subject(s)
Eprints ID
249736
File(s)![Thumbnail Image]()
Name
RC2_PV_full.pdf
Size
9.92 MB
Format
Adobe PDF
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