OMiSO: Adaptive optimization of state-dependent brain stimulation to shape neural population states

2025/07/10 by Yuki Minai, Joana Soldado-Magraner, Minai, Yuki +5 · 1 citation
Medicine · Neuroscience · #FOS: Biological sciences #Neurological disorders and treatments #Neurons and Cognition (q-bio.NC) #Neuroscience and Neural Engineering #Transcranial Magnetic Stimulation Studies

paper · pdf · doi:10.48550/arxiv.2507.07858

openalex publication_date 2025/07/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The coordinated activity of neural populations underlies myriad brain functions. Manipulating this activity using brain stimulation techniques has great potential for scientific and clinical applications, as it provides a tool to causally influence brain function. The state of the brain affects how neural populations respond to incoming sensory stimuli. Thus, taking into account pre-stimulation neural population activity may be crucial to achieve a desired causal manipulation using stimulation. In this work, we propose Online MicroStimulation Optimization (OMiSO), a brain stimulation framework that leverages brain state information to find stimulation parameters that can drive neural population activity toward specified states. OMiSO includes two key advances: i) it leverages the pre-stimulation brain state to choose optimal stimulation parameters, and ii) it adaptively refines the choice of those parameters by considering newly-observed stimulation responses. We tested OMiSO by applying intracortical electrical microstimulation in a monkey and found that it outperformed competing methods that do not incorporate these advances. Taken together, OMiSO provides greater accuracy in achieving specified activity states, thereby advancing neuromodulation technologies for understanding the brain and for treating brain disorders.

Citations

• Offline Learning of Closed-Loop Deep Brain Stimulation Controllers for Parkinson Disease Treatment
• The population doctrine in cognitive neuroscience
• Computation Through Neural Population Dynamics
• Neuromodulation of Brain State and Behavior
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Optuna: A Next-generation Hyperparameter Optimization Framework
• Proximal Policy Optimization Algorithms
• Playing Atari with Deep Reinforcement Learning
• A Generalized Solution of the Orthogonal Procrustes Problem

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• Brain–computer interfaces as a causal probe for scientific inquiry

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