TY  - JOUR
A1  - Krylov, Dmitrii
A1  - Dylov, Dmitry V.
A1  - Rosenblum, Michael
T1  - Reinforcement learning for suppression of collective activity in oscillatory ensembles
JF  - Chaos : an interdisciplinary journal of nonlinear science
N2  - 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.
Y1  - 2020
U6  - https://doi.org/10.1063/1.5128909
SN  - 1054-1500
SN  - 1089-7682
VL  - 30
IS  - 3
PB  - American Institute of Physics
CY  - Melville
ER  -