TMM Abstract (Carlson/Dzirasa/Sapiro)

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The overarching goal of this proposal is to learn how neurons’ action potentials, long considered to be a fundamental unit of information, relate to whole-brain spatiotemporal voltage patterns and behavior. To uncover this relationship, we will develop novel computational methods capable of learning networks that relate voltage signals from multiple brain regions based upon our previously developed explainable machine learning approach. These networks will then be used to stratify neurons into subtypes consistent across a population of subjects to facilitate the statistical aggregation of data to uncover relationships between multiple scale of neural activity and behavior.

What analytical (Theories, Models and Methods) tools have you developed?

We are developing models of mesoscale network activity from implanted, multi-site electrodes to integrate whole brain activity.

What questions can you answer?

Our immediate goal is to use these techniques to gain a greater understanding of how mesoscale networks related to neuropsychiatric disorders and to more basic units of information (e.g., neural firing)

What input do you need? (e.g., cellular activity, sub-cellular, sensory input, complex behavior)

Validation of our methods requires multi-region Local Field Potential recordings (also amenable to EEG measurements), ideally with paired behaviors and neural activity.

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