April 21, 2021

12:00 pm / 1:00 pm

Venue

https://wse.zoom.us/s/94623801186

Link for Live Seminar
Link for Recorded seminars ? 2020/2021 school year
 
Abstract:
When we think of animal behavior, what typically comes to mind are actions ? running, eating, swimming, grooming, flying, singing, resting. Behavior, however, is more than the catalogue of motions that an organism can perform. Animals organize their repertoireof actions into sequences and patterns whose underlying dynamics last much longer than any particular behavior. How an organism modulates these dynamics affects its success at accessing food, reproducing, and myriad other tasks essential for survival. Animals regulate these patterns of behavior via many interacting internal states (hunger, reproductive cycle, age, etc.) that we cannot directly measure. Studying these hidden states’ dynamics, accordingly, has proven challenging due to a lack of measurementtechniques and theoretical understanding. In this talk, I will outline our efforts to uncover the latent dynamics that underlie long timescale structure in animal behavior. Looking across a variety of organisms, we use a novel methodology to measure animals’ full behavioral repertoires to find the existence of a non-trivial form of long timescale dynamics that cannot be explained using standard mathematical frameworks. I will present howtemporal coarse-graining can be used to understand how these dynamics aregenerated and how the found course-grained states can be related to the internal states governing behavior through a combination of machine learning techniques and dynamical systems modeling.  Inferring these hidden dynamics presents a new opportunity to generate insights into the neural and physiological mechanisms that animals use to select actions.
Biography:
Gordon J. Berman, Ph.D., Assistant Professor of Biology, Emory UniversityCo-Director, Simons-Emory International Consortium on Motor Control Chair of Recruitment for the Emory Neuroscience Graduate Program . Our lab uses theoretical, computational, and data-driven approaches to gain quantitative insight into entire repertoires of animal behaviors, aiming to makeconnections to the neurobiology, genetics, and evolutionary histories and that underlie them. Get more information here.