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Covariance Features Improve Low-Resource Reservoir Computing Performance in Multivariate Time Series Classification

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Computational Vision and Bio-Inspired Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1420))

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Abstract

Biological systems exhibit tremendous performance and flexibility in learning for a broad diversity of inputs, which are in general time series. Inspired by their biological counterpart, artificial neural networks used in machine learning for classification aim to extract activity patterns within input signals to transform them into stereotypical output patterns that represent categories. For the vast majority, they rely on fixed target values in output to represent probabilities or implement winner-take-all decisions, which correspond in the case of time series to first-order statistics. In other words, the basis for such classification of time series is the transformation of input high-order statistics into output first-order statistics. However, the transformation of input statistics to second- or higher-order statistics has not been much explored yet. Here, we consider a computational scheme based on a reservoir that maps information engrained in input multivariate time series statistics to second-order statistics of its own activity, before being fed to a usual classifier (logistic regression). We compare this covariance decoding with the “classical” mean decoding applied to the reservoir for classification with both synthetic and real datasets of multivariate time series. We show that covariance decoding can extract a broader diversity of second-order statistics from the input signals, yielding higher performance with smaller resources (i.e., reservoir size). Our results pave the way for the characterization of elaborate input-output mappings between statistical orders to efficiently represent and process input signals with complex spatio-temporal structures.

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Acknowledgements

S.L. is supported by a FI fellowship from the Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR, 2021 FI-B2 00121). R.M.B is supported by the Howard Hughes Medical Institute (HHMI, ref 55008742), MINECO (Spain; BFU2017-85936-P) and ICREA Academia (2016). M.G acknowledges funding from the German Excellence Strategy of the Federal Government and the Länder (G:(DE-82)EXS-PF-JARA-SDS005) and the European Union’s Horizon 2020 research and innovation program under grant agreement No. 785907 (Human Brain Project SGA2).

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Authors and Affiliations

  1. Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain

    Sofía Lawrie, Rubén Moreno-Bote & Matthieu Gilson

  2. Serra Hunter Fellow Programme, Universitat Pompeu Fabra, Barcelona, Spain

    Rubén Moreno-Bote

  3. Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany

    Matthieu Gilson

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  1. Sofía Lawrie
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Correspondence to Sofía Lawrie .

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Editors and Affiliations

  1. Department of ECE, RVS Technical Campus, Coimbatore, Tamil Nadu, India

    S. Smys

  2. Departamento de Engenharia Mecanica, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

  3. Faculty of Engineering, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

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Lawrie, S., Moreno-Bote, R., Gilson, M. (2022). Covariance Features Improve Low-Resource Reservoir Computing Performance in Multivariate Time Series Classification. In: Smys, S., Tavares, J.M.R.S., Balas, V.E. (eds) Computational Vision and Bio-Inspired Computing. Advances in Intelligent Systems and Computing, vol 1420. Springer, Singapore. https://doi.org/10.1007/978-981-16-9573-5_42

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