Neural modulation is becoming a fundamental tool for understanding and treating neurological diseases and their implicated neural circuits. Given that neural modulation interventions have high dimensional parameter spaces, one of the challenges is selecting the stimulation parameters that induce the desired effect. Moreover, the effect of a given set of stimulation parameters may change depending on the underlying neural state. In this study, we investigate and address the state-dependent effect of medial septum optogenetic stimulation on the hippocampus. We found that pre-stimulation hippocampal gamma (33-50Hz) power influences the effect of medial septum optogenetic stimulation on during-stimulation hippocampal gamma power. We then construct a simulation platform that models this phenomenon for testing optimization approaches. We then compare the performance of a standard implementation of Bayesian optimization, along with an extension to the algorithm that incorporates pre-stimulation state to learn a state-dependent policy. The state-dependent algorithm outperformed the standard approach, suggesting that incorporating pre-stimulation can improve neural modulation interventions.
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