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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Research Article| March 17 2020
Reinforcement learning for suppression of collective activity in oscillatory ensembles
Dmitry V. Dylov ;
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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