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Predicting slow and fast neuronal dynamics with machine learning

Published:2019-11 Issue:11 Volume:29 Page:113119
ISSN:1054-1500
Container-title:Chaos: An Interdisciplinary Journal of Nonlinear Science
language:en
Short-container-title:Chaos

Author:

Follmann Rosangela¹²,Rosa Epaminondas²³

Affiliation:

1. School of Information Technology, Illinois State University, Normal, Illinois 61790, USA

2. Department of Physics, Illinois State University, Normal, Illinois 61790, USA

3. School of Biological Sciences, Illinois State University, Normal, Illinois 61790, USA

Publisher

AIP Publishing

Subject

Applied Mathematics,General Physics and Astronomy,Mathematical Physics,Statistical and Nonlinear Physics

Link

http://aip.scitation.org/doi/pdf/10.1063/1.5119723

Reference27 articles.

1. Recent advances in physical reservoir computing: A review

2. High-Speed Photonic Reservoir Computing Using a Time-Delay-Based Architecture: Million Words per Second Classification

3. A theoretical and experimental study of neuromorphic atomic switch networks for reservoir computing

4. Computing with networks of nonlinear mechanical oscillators

5. Minimal approach to neuro-inspired information processing

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2. Reconstructing bifurcation diagrams of chaotic circuits with reservoir computing;Physical Review E;2024-02-13

3. Using reservoir computing for dynamics forecast of noise-perturbed FitzHugh-Nagumo system;2023 7th Scientific School Dynamics of Complex Networks and their Applications (DCNA);2023-09-18

4. Using machine learning to anticipate tipping points and extrapolate to post-tipping dynamics of non-stationary dynamical systems;Chaos: An Interdisciplinary Journal of Nonlinear Science;2023-02-01

5. Predicting solutions of the Lotka‐Volterra equation using hybrid deep network;Theoretical and Applied Mechanics Letters;2022-11

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