Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders.

ABSTRACT: Recent neuroscience studies demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral measurements are now often collected using video cameras, resulting in an increased need for computer vision algorithms that extract useful information from video data. Here we introduce a new video analysis tool that combines the output of supervised pose estimation algorithms (e.g. DeepLabCut) with unsupervised dimensionality reduction methods to produce interpretable, low-dimensional representations of behavioral videos that extract more information than pose estimates alone. We demonstrate this tool by extracting interpretable behavioral features from videos of three different head-fixed mouse preparations, as well as a freely moving mou

SUBMITTER: Whiteway MR 

PROVIDER: S-EPMC8489729 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature