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Reservoir computing with delay in structured networks
Author(s): André Röhm; Kathy Lüdge
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Paper Abstract

Reservoir computing is a machine-learning scheme that solves computational problems with the power of dynamical systems. In this contribution we investigate and quantitatively compare the two reservoir systems that are predominantly used nowadays: Delay and network models. Additionally, we also investigate hybrid concepts called 'multiplexed networks', that incorporate elements of both of these approaches. By constructing reservoir computers with identical numbers of readout dimensions, we can quantitatively compare the performance. We find that the time-multiplexing procedure of the classical delay-approach can be extended to hybrid delay-network systems without loss of computational power, which enables the construction of faster reservoir computers.

Paper Details

Date Published: 22 May 2018
PDF: 6 pages
Proc. SPIE 10689, Neuro-inspired Photonic Computing, 1068905 (22 May 2018); doi: 10.1117/12.2307159
Show Author Affiliations
André Röhm, Technische Univ. Berlin (Germany)
Kathy Lüdge, Technische Univ. Berlin (Germany)

Published in SPIE Proceedings Vol. 10689:
Neuro-inspired Photonic Computing
Marc Sciamanna; Peter Bienstman, Editor(s)

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