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SUMMARY:High-throughput behavioral analysis for neural circuit understandi
 ng
DTSTART:20190328T100000
DTEND:20190328T110000
DTSTAMP:20260508T185058Z
UID:18306826465ae65c7a5f07780e07ccb57bf36e6fff7d56346ac43ec3
CATEGORIES:Conferences - Seminars
DESCRIPTION:Dr Alexander Mathis\, Harvard University\, USA.\nQuantifying b
 ehavior is crucial for many applications across the life sciences and engi
 neering. Videography provides easy methods for the observation and recordi
 ng of animal behavior in diverse settings\, yet extracting particular aspe
 cts of a behavior for further analysis can be highly time consuming. I wil
 l present an efficient method for markerless pose estimation based on tran
 sfer learning with deep neural networks that achieves excellent results wi
 th minimal training data. I will demonstrate the versatility of this frame
 work by tracking various body parts in multiple species across a broad col
 lection of behaviors from egg-laying fruits flies to hunting cheetahs. Fur
 thermore\, I will discuss new work for identifying fine-scale behaviors wi
 th deep neural networks. Lastly\, I will discuss computational modeling ap
 proaches I have developed that link behavior to neural circuits.\n\nBio\nA
 lexander Mathis is a Postdoctoral Fellow at Harvard University. He is inte
 rested in elucidating how the brain gives rise to adaptive behavior. For t
 hose purposes\, he develops deep learning methods to analyze animal behavi
 or\, neural data\, as well as creates experimentally testable computationa
 l models. His PhD thesis with Prof. Andreas Herz focused on deriving prope
 rties of grid cells from optimal coding assumptions\, and figuring out how
  the distributed population activity can be decoded by biophysically plaus
 ible models. He was awarded a Marie Curie-Sklodowska fellowship\, Human Fr
 ontiers Science Program postdoctoral fellowship\, and postdoctoral fellows
 hip by the DFG. His work was recently covered by The Atlantic & NVIDIA AI.
LOCATION:SV 1717 https://plan.epfl.ch/?room==SV%201717
STATUS:CONFIRMED
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