Workshop on Methods of Information Theory in Computational NeuroscienceMaryam ShanechiUniversity of Southern California
Dynamical modeling, decoding, and control of multiscale brain networksIn this talk, I first discuss our recent work on modeling, decoding, and controlling multisite human brain dynamics underlying mood states. I present a multiscale dynamical modeling framework that allows us to decode mood variations for the first time and identify brain sites that are most predictive of mood. I then develop a system identification approach that can predict multiregional brain network dynamics (output) in response to electrical stimulation (input) toward enabling closed-loop control of brain network activity. Further, I demonstrate that our framework can uncover multiscale behaviorally relevant neural dynamics from hybrid spike-field recordings in monkeys performing naturalistic movements. Finally, the framework can combine information from multiple scales of activity and model their different time-scales and statistics. These dynamical models, decoders, and controllers can advance our understanding of neural mechanisms and facilitate future closed-loop therapies for neurological and neuropsychiatric disorders.
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