Reinforcement Learning Framework for Deep Brain Stimulation Study
This work addresses the challenge of controlling neuron synchrony for neuroscience applications, offering a simulation-based approach to reduce reliance on live human brain experiments, but it is incremental as it builds on existing RL methods.
The authors tackled the problem of suppressing collective synchronous neuron activity, which is linked to neurological diseases like Parkinson's, by developing a Reinforcement Learning gym framework to emulate neuron behavior and find suppression parameters; they successfully suppressed synchrony in three pathological signaling regimes and removed unwanted oscillations using multiple PPO agents.
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.