Alexander Mathis

Monday 20th May 2019

Time:4.00pm

Ground Floor Seminar Room

25 Howland Street, London, W1T 4JG

High-throughput behavioral analysis for neural circuit understanding

Quantifying behavior is crucial for many applications across neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. I will present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. I will demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors from egg-laying fruit flies to hunting cheetahs. Furthermore, I will discuss computational modeling approaches I have developed to link behavior to neural circuits with a specific focus on how the brain represents space as well as posture.

Biography:

Alexander Mathis is a Postdoctoral Fellow at Harvard University working with Prof. Venkatesh N. Murthy and Prof. Matthias Bethge. He is interested in elucidating how the brain gives rise to adaptive behavior. For those purposes, he develops deep learning methods to analyze animal behavior, neural data, as well as creates experimentally testable computational models. His PhD thesis with Prof. Andreas Herz focused on deriving properties of grid cells from optimal coding assumptions, and figuring out how the distributed population activity can be decoded by biophysically plausible models.