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.

中文翻译：

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强化学习以抑制振荡乐团中的集体活动

我们目前使用现代机器学习方法来抑制自我维持的集体振荡，这种振荡通常由大脑中的退化性神经元的集成所发出。提出的混合模型依赖于两个主要组件：振荡器环境和基于策略的强化学习块。我们报告了基于Actor-Critic配置的近端策略优化和两个人工神经网络的模型不可知同步控制。提出了一类具有物理意义的奖励函数，可以抑制集体振荡模式。两种神经元模型的同步抑制都得到了证明：全局耦合的极限循环Bonhoeffer-van der Pol振荡器的合奏以及使用矩形和电荷平衡刺激的欣德马什-罗斯神经元的爆发。