Reconstruction of Complex Dynamical Systems from Time Series using Reservoir Computing

Jüngling, Thomas; Lymburn, Thomas; Stemler, Thomas; Corrêa, Débora C.; Walker, David M.; Small, Michael

doi:10.1109/iscas.2019.8702137

Cited by 8 publications

(1 citation statement)

References 14 publications

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“…Chen et al, 2018;Rusch et al, 2022;Rusch & Mishra, 2021a;Salvi et al, 2022;Song et al, 2016). RNN-driven DS reconstruction methods, in particular, have been based, for instance, on Long-Short-Term-Memory (LSTM) networks (Vlachas et al, 2018), Reservoir Computing [RC; (Jüngling et al, 2019;Pathak et al, 2018)], ODE/PDE-based RNNs (Haußmann et al, 2021;Salvi et al, 2022), or piecewise-linear RNNs [PLRNNs; (Brenner et al, 2022;Durstewitz, 2017a;Koppe et al, 2019;Schmidt et al, 2021]. Let us take a closer look at a PLRNN, for example, defined as follows (Koppe et al, 2019;Schmidt et al, 2021):…”

Section: Machine Learning Models For Dynamical Systems Reconstructionsmentioning

confidence: 99%

Reconstructing Computational Dynamics from Neural Measurements with Recurrent Neural Networks

Durstewitz

Koppe

Thurm

2022

Preprint

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Mechanistic and computational models in neuroscience usually take the form of systems of differential or time-recursive equations. The spatio-temporal behavior of such systems is the subject of dynamical systems theory (DST). DST provides a powerful mathematical toolbox for describing and analyzing neurobiological processes at any level, from molecules to behavior, and has been a mainstay of computational neuroscience for decades. Recently, recurrent neural networks (RNNs) became a popular machine learning tool for studying the nonlinear dynamics underlying neural or behavioral observations. By training RNNs on the same behavioral tasks as employed for animal subjects and dissecting their inner workings, insights and hypotheses about the neuro-computational underpinnings of behavior could be generated. Alternatively, RNNs may be trained directly on the physiological and behavioral time series at hand. Ideally, the once trained RNN would then be able to generate data with the same temporal and geometrical properties as those observed. This is called dynamical systems reconstruction, a burgeoning field in machine learning and nonlinear dynamics. Through this more powerful approach the trained RNN becomes a surrogate for the experimentally probed system, as far as its dynamical and computational properties are concerned. The trained system can then be systematically analyzed, probed and simulated. Here we will review this highly exciting and rapidly expanding field, including recent trends in machine learning that may as yet be less well known in neuroscience. We will also discuss important validation tests, caveats, and requirements of RNN-based dynamical systems reconstruction. Concepts and applications will be illustrated with various examples from neuroscience.

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