Neural modeling case studies at biophysical, machine learning, and automation levels
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[EMBARGOED UNTIL 12/1/2024] This dissertation reports three case studies using machine learning, biophysical, and automation frameworks to study neural engineering challenges. The first study utilized machine learning with a clinical dataset to predict the risk of future opioid use disorder (OUD). The model achieved a high level of predictive accuracy and highlighted the most impactful variables that predicted the risk. The second study implemented recent tract- tracing data predicting the existence of a motif that generated the theta rhythm, similar to that in the hippocampus in the amygdala. This was done via the development of a biophysical model of the rodent amygdala that demonstrated how the theta rhythm could be engendered by an external theta-rhythmic inhibitory projection from the ventral pallidum and substantia innominata. The third study developed an automation pipeline using biophysical and machine learning schemes, to assist in the development of biophysical models of neurons. The approach implemented recent insights developed in our group related to currents being grouped into modules based on their neurocomputational signatures.
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Ph. D.