Reinforcement learning for suppression of collective activity in oscillatory ensembles

Dmitrii Krylov, Dmitry V. Dylov, Michael Rosenblum

    Research output: Contribution to journalArticlepeer-review

    11 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Article number033126
    JournalChaos
    Volume30
    Issue number3
    DOIs
    Publication statusPublished - 1 Mar 2020

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