# Reinforcement Learning Framework for Deep Brain Stimulation Study

arXiv: https://arxiv.org/abs/2002.10948 · HTML: https://mlsanity.com/paper/2002.10948/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2020.

## Built on by (most cited first, 6 of 6)

- [Reinforcement Learning based Disease Progression Model for Alzheimer's Disease](https://mlsanity.com/paper/2106.16187/) (2021)
- [Neurophysiologically Realistic Environment for Comparing Adaptive Deep Brain Stimulation Algorithms in Parkinson Disease](https://mlsanity.com/paper/2505.09624/) (2025)
- [Sample-Efficient Reinforcement Learning Controller for Deep Brain Stimulation in Parkinson's Disease](https://mlsanity.com/paper/2507.06326/) (2025)
- [Novel Reinforcement Learning Algorithm for Suppressing Synchronization in Closed Loop Deep Brain Stimulators](https://mlsanity.com/paper/2212.13260/) (2022)
- [Learning Control Policies of Hodgkin-Huxley Neuronal Dynamics](https://mlsanity.com/paper/2311.07563/) (2023)
- [Suppressing Modulation Instability with Reinforcement Learning](https://mlsanity.com/paper/2404.04310/) (2024)

## Abstract (arXiv)

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.