Xiaowen Chen is a theoretical biophysicist interested in understanding collective behavior in living systems. She is currently a postdoctoral researcher in Laboratoire de physique de l'école normale supérieure / CNRS in Paris, working in the statistical biophysics research group led by Aleksandra Walczak and Thierry Mora. She obtained her PhD in 2020 from Princeton University under the supervision of William Bialek, where she studied collective behaviors in neuronal networks. She studies the statistical physics of collective behavior with a combination of data-driven and analytic approaches, and she has a special interest in applications in neuroscience and collective animal behavior.
Thursday April 20th
Inferring collective dynamics in groups of social mice
Social interactions are a crucial aspect of behavior in many animal species. Nonetheless, it is often difficult to distinguish the effect of interactions from independent animal behavior (e.g. non-Markovian dynamics, response to environmental cues, etc.). In this talk, I will address this question in social mice, where we infer statistical physics models for the collective dynamics for groups of 15 mice, housed and location-tracked over multiple days in a controlled environment. We reproduce the distribution for the co-localization patterns using pairwise maximum entropy models, and find that the resulting local fields successfully predict the transition rates. To capture the long-tailed waiting time distributions, we develop a novel inference method that can tune the dynamics while keeping the steady state distribution fixed. These models are biologically meaningful, and are able to distinguish the effect of social-impairment drugs on autism in the mice model.