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

Yuki Minai, Joana Soldado-Magraner, Byron M. Yu, Matthew A. Smith

Keywords: closed-loop optimization, microstimulation, neural population activity, dimensionality reduction, latent variable models, reinforcement learning, behavior cloning, proximal policy optimization

TL;DR: OMiSO (Online MicroStimulation Optimization): a brain stimulation framework to drive neural population activity toward specified states by adaptively choosing stimulation parameters based on brain state information

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. To improve the accuracy by which one can manipulate neural activity, it is important to (1) take into account the pre-stimulation brain state, which can influence the brain’s response to stimulation, and (2) adaptively update stimulation parameters over time to compensate for changes in the brain’s response to 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) training a stimulation-response model that leverages the pre-stimulation brain state, and inverting this model to choose the stimulation parameters, and ii) updating this inverse model online using newly-observed responses to stimulation. We tested OMiSO using intracortical microstimulation with a ``Utah'' array 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.

Primary Area: Neuroscience and cognitive science (e.g., neural coding, brain-computer interfaces)

Submission Number: 6367