Action detection using a neural network elucidates the genetics of mouse grooming behavior.

ABSTRACT: Automated detection of complex animal behaviors remains a challenging problem in neuroscience, particularly for behaviors that consist of disparate sequential motions. Grooming is a prototypical stereotyped behavior that is often used as an endophenotype in psychiatric genetics. Here, we used mouse grooming behavior as an example and developed a general purpose neural network architecture capable of dynamic action detection at human observer-level performance and operating across dozens of mouse strains with high visual diversity. We provide insights into the amount of human annotated training data that are needed to achieve such performance. We surveyed grooming behavior in the open field in 2457 mice across 62 strains, determined its heritable components, conducted GWAS to outline its ge

SUBMITTER: Geuther BQ 

PROVIDER: S-EPMC8043749 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature