Characterizing the Pupillary Dynamics of Mice in a Virtual Reality 2-Lick Spout Accumulating Towers Task

Abstract

Head-fixed mice in a virtual reality (VR) environment assigned with perceptual decision-making tasks have been the basis for the study of many cognitive processes. This thesis is centered around a novel perceptual decision making task called the 2-lick spout accumulating towers task (2LS-ATT) which involves a linear track rather than a virtual T-maze, and we characterize pupillary dynamics of mice groups in various navigational task modes and across different cognitive levels that isolate discrimination, evidence accumulation and working memory within the 2LS-ATT. There has not yet been characterization of pupillometry of head-fixed mice in virtual reality in the field of research in 2LS-ATT tasks, virtual T-maze tasks, and other related perceptual decision-making tasks. By processing camera footage from the virtual reality rig through a deep neural network software (DeepLabCut) that tracks pupillary dynamics, and by visualizing and statistically analyzing the resultant distributions, we are able to provide novel research to the field of pupillometry and virtual reality perceptual decision-making rodent studies.