Machine Learning and Brain Computational Modeling

Based on the unique data we obtain from novel neuromodulation, imaging, and recording technologies, we can model brain function computationally. Put another way, we can construct a sort of digital circuit map which approximates how information is passed around in the brain. For example, we can use dynamic causal modeling (DCM) of optogenetic fMRI experiments to parameterize the causal relationships among regions of a distributed brain network. Brain circuit models improve our understanding of brain function and will ultimately help us to systematically design novel therapeutics. Brain diseases have traditionally been treated in the context of pathological, molecular changes. However, such changes drive neurological disease symptoms (behavior) through resulting changes in brain circuit function. Therefore, for both diagnosis and therapy of brain disease, understanding circuit mechanisms is critical.

Our lab is interested in understanding how the brain works at the systems level.