Reinforcement Learning Framework for Deep Brain Stimulation Study

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Authors Michael Rosenblum, Remi Tachet, Dmitry V. Dylov, Dmitrii Krylov, Romain Laroche
Journal/Conference Name Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Paper Category
Paper Abstract Malfunctioning neurons in the brain sometimes operate synchronously, reportedly causing many neurological diseases, e.g. Parkinson's. Suppression and control of this collective synchronous activity are therefore of great importance for neuroscience, and can only rely on limited engineering trials due to the need to experiment with live human brains. We present the first Reinforcement Learning gym framework that emulates this collective behavior of neurons and allows us to find suppression parameters for the environment of synthetic degenerate models of neurons. We successfully suppress synchrony via RL for three pathological signaling regimes, characterize the framework's stability to noise, and further remove the unwanted oscillations by engaging multiple PPO agents.
Date of publication 2020
Code Programming Language Jupyter Notebook
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