# Neurophysiologically Realistic Environment for Comparing Adaptive Deep Brain Stimulation Algorithms in Parkinson Disease

arXiv: https://arxiv.org/abs/2505.09624 · HTML: https://mlsanity.com/paper/2505.09624/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 134 references that are in the map.
- First posted on arXiv 26 Apr 2025.

## Builds on (most cited first, 16 of 16)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Offline Reinforcement Learning with Implicit Q-Learning](https://mlsanity.com/paper/2110.06169/) (2021)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [On Neural Differential Equations](https://mlsanity.com/paper/2202.02435/) (2022)
- [Offline Learning of Closed-Loop Deep Brain Stimulation Controllers for Parkinson Disease Treatment](https://mlsanity.com/paper/2302.02477/) (2023)
- [Reinforcement Learning Framework for Deep Brain Stimulation Study](https://mlsanity.com/paper/2002.10948/) (2020)
- [Mean-field modeling of the basal ganglia-thalamocortical system. I. Firing rates in healthy and parkinsonian states](https://mlsanity.com/paper/1801.01822/) (2018)
- [Phase synchronization of coupled bursting neurons and the generalized Kuramoto model](https://mlsanity.com/paper/1502.04067/) (2015)
- [Cortical Brain Computer Interface for Closed-Loop Deep Brain Stimulation](https://mlsanity.com/paper/1612.01229/) (2016)
- [Reinforcement learning for suppression of collective activity in oscillatory ensembles](https://mlsanity.com/paper/1909.12154/) (2019)
- [Deep brain stimulation for movement disorder treatment: Exploring frequency-dependent efficacy in a computational network model](https://mlsanity.com/paper/2010.07162/) (2020)
- [Coprocessor Actor Critic: A Model-Based Reinforcement Learning Approach For Adaptive Brain Stimulation](https://mlsanity.com/paper/2406.06714/) (2024)
- [Failure of Delayed Feedback Deep Brain Stimulation for Intermittent Pathological Synchronization in Parkinson's Disease](https://mlsanity.com/paper/1302.1758/) (2013)
- [A Data-Driven Biophysical Computational Model of Parkinson's Disease based on Marmoset Monkeys](https://mlsanity.com/paper/2107.12536/) (2021)
- [Novel Reinforcement Learning Algorithm for Suppressing Synchronization in Closed Loop Deep Brain Stimulators](https://mlsanity.com/paper/2212.13260/) (2022)

## Built on by (most cited first, 2 of 2)

- [Neuromorphic Energy-Aware Learning for Adaptive Deep Brain Stimulation](https://mlsanity.com/paper/2606.28600/) (2026)
- [Learning Transferable Policies from Action-free Time Series Through Dynamical Embeddings](https://mlsanity.com/paper/2610.03065/) (2026)

## Abstract (arXiv)

Adaptive deep brain stimulation (aDBS) has emerged as a promising treatment for Parkinson disease (PD). In aDBS, a surgically placed electrode sends dynamically altered stimuli to the brain based on neurophysiological feedback: an invasive gadget that limits the amount of data one could collect for optimizing the control offline. As a consequence, a plethora of synthetic models of PD and those of the control algorithms have been proposed. Herein, we introduce the first neurophysiologically realistic benchmark for comparing said models. Specifically, our methodology covers not only conventional basal ganglia circuit dynamics and pathological oscillations, but also captures 15 previously dismissed physiological attributes, such as signal instabilities and noise, neural drift, electrode conductance changes and individual variability - all modeled as spatially distributed and temporally registered features via beta-band activity in the brain and a feedback. Furthermore, we purposely built our framework as a structured environment for training and evaluating deep reinforcement learning (RL) algorithms, opening new possibilities for optimizing aDBS control strategies and inviting the machine learning community to contribute to the emerging field of intelligent neurostimulation interfaces.