Machine learning evaluates changes in functional connectivity under a prolonged cognitive load-Reference-Cited by-同舟云学术

Machine learning evaluates changes in functional connectivity under a prolonged cognitive load

Published:2021-10 Issue:10 Volume:31 Page:101106
ISSN:1054-1500
Container-title:Chaos: An Interdisciplinary Journal of Nonlinear Science
language:en
Short-container-title:Chaos

Author:

Frolov Nikita¹²^ORCID,Kabir Muhammad Salman³^ORCID,Maksimenko Vladimir¹²^ORCID,Hramov Alexander¹²⁴^ORCID

Affiliation:

1. Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, 420500 Innopolis, Russia

2. Center for Neurotechnology and Machine Learning, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia

3. Department of Robotics and Computer Vision, Innopolis University, 420500 Innopolis, Russia

4. Department of Theoretical Cybernetics, Saint Petersburg State University, 199034 St. Petersburg, Russia

Funder

Russian Science Foundation

Publisher

AIP Publishing

Subject

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

Link

https://aip.scitation.org/doi/pdf/10.1063/5.0070493

Reference47 articles.

1. Machine learning: Trends, perspectives, and prospects

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3. Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach

4. Attractor reconstruction by machine learning

5. Combining machine learning with knowledge-based modeling for scalable forecasting and subgrid-scale closure of large, complex, spatiotemporal systems

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