GED: A flexible family of versatile methods for hypothesis-driven multivariate decompositions
Wednesday, June 16, 2021
17:30 UTC
Dr Mike X Cohen
Donders Centre for Medical Neuroscience
Recording
Open recording
Hosted by NERV70 minutes
Abstract
Does that title put you to sleep or pique your interest? The goal of my presentation is to introduce a powerful yet under-utilized mathematical equation that is surprisingly effective at uncovering spatiotemporal patterns that are embedded in data -- but that might be inaccessible in traditional analysis methods due to low SNR or sparse spatial distribution. If you flunked calculus, then don't worry: the math is really easy, and I'll spend most of the time discussing intuition, simulations, and applications in real data. I will also spend some time in the beginning of the talk providing a bird's-eye-view of the empirical research in my lab, which focuses on mesoscale brain dynamics associated with error monitoring and response competition.
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