Reinforcement learning for suppression of collective activity in oscillatory ensembles

DOI10.1063/1.5128909zbMath1435.92005arXiv1909.12154OpenAlexW2976902166WikidataQ90828503 ScholiaQ90828503MaRDI QIDQ5112985

Dmitry V. Dylov, Dmitrii Krylov, Michael G. Rosenblum

Publication date: 9 June 2020

Published in: Chaos: An Interdisciplinary Journal of Nonlinear Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1909.12154

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Neural biology (92C20) Neural networks for/in biological studies, artificial life and related topics (92B20)

Related Items (3)

Control of chaos with time-delayed feedback based on deep reinforcement learning ⋮ Controlling collective synchrony in oscillatory ensembles by precisely timed pulses ⋮ Eliminating synchronization of coupled neurons adaptively by using feedback coupling with heterogeneous delays

Uses Software

• TensorFlow
• GitHub
• Stable Baselines

Cites Work

• Unnamed Item
• Chemical oscillations, waves, and turbulence
• Phase resetting in medicine and biology. Stochastic modelling and data analysis
• Desynchronizing double-pulse phase resetting and application to deep brain stimulation
• Long-term anti-kindling effects of desynchronizing brain stimulation: a theoretical study
• Synchrony suppression in ensembles of coupled oscillators via adaptive vanishing feedback
• Dynamics of globally coupled oscillators: Progress and perspectives
• Convolutional autoencoder and conditional random fields hybrid for predicting spatial-temporal chaos
• MULTISITE COORDINATED DELAYED FEEDBACK FOR AN EFFECTIVE DESYNCHRONIZATION OF NEURONAL NETWORKS

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