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

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