We present the use of modern machine learning approaches to suppress self-sustained collective oscillations typically signaled by ensembles of degenerative neurons in the brain. The proposed hybrid model relies on two major components: an environment of oscillators and a policy-based reinforcement learning block. We report a model-agnostic synchrony control based on proximal policy optimization and two artificial neural networks in an Actor–Critic configuration. A class of physically meaningful reward functions enabling the suppression of collective oscillatory mode is proposed. The synchrony suppression is demonstrated for two models of neuronal populations—for the ensembles of globally coupled limit-cycle Bonhoeffer–van der Pol oscillators and for the bursting Hindmarsh–Rose neurons using rectangular and charge-balanced stimuli.
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March 2020
Research Article|
March 17 2020
Reinforcement learning for suppression of collective activity in oscillatory ensembles
Dmitrii Krylov;
Dmitrii Krylov
1
Skolkovo Institute of Science and Technology
, Bolshoy blvd. 30/1, Moscow 121205, Russia
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Dmitry V. Dylov
;
Dmitry V. Dylov
a)
1
Skolkovo Institute of Science and Technology
, Bolshoy blvd. 30/1, Moscow 121205, Russia
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Michael Rosenblum
Michael Rosenblum
b)
2
Institute of Physics and Astronomy, University of Potsdam
, Karl-Liebknecht-Str. 24/25, 14476 Potsdam-Golm, Germany
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a)
Electronic mail: d.dylov@skoltech.ru
b)
Author to whom correspondence should be addressed: mros@uni-potsdam.de
Note: This paper is part of the Focus Issue, “When Machine Learning Meets Complex Systems: Networks, Chaos and Nonlinear Dynamics.”
Chaos 30, 033126 (2020)
Article history
Received:
September 23 2019
Accepted:
February 26 2020
Citation
Dmitrii Krylov, Dmitry V. Dylov, Michael Rosenblum; Reinforcement learning for suppression of collective activity in oscillatory ensembles. Chaos 1 March 2020; 30 (3): 033126. https://doi.org/10.1063/1.5128909
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