Reservoir computing with delay in structured networks

André Röhm; Kathy Lüdge

doi:10.1117/12.2307159

22 May 2018 Reservoir computing with delay in structured networks

André Röhm, Kathy Lüdge

Proceedings Volume 10689, Neuro-inspired Photonic Computing; 1068905 (2018) https://doi.org/10.1117/12.2307159
Event: SPIE Photonics Europe, 2018, Strasbourg, France

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.

Citation Download Citation

André Röhm and Kathy Lüdge "Reservoir computing with delay in structured networks", Proc. SPIE 10689, Neuro-inspired Photonic Computing, 1068905 (22 May 2018); https://doi.org/10.1117/12.2307159

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