Deep learning-based behavioral analysis reaches human accuracy and is capable of outperforming commercial solutions.

ABSTRACT: To study brain function, preclinical research heavily relies on animal monitoring and the subsequent analyses of behavior. Commercial platforms have enabled semi high-throughput behavioral analyses by automating animal tracking, yet they poorly recognize ethologically relevant behaviors and lack the flexibility to be employed in variable testing environments. Critical advances based on deep-learning and machine vision over the last couple of years now enable markerless tracking of individual body parts of freely moving rodents with high precision. Here, we compare the performance of commercially available platforms (EthoVision XT14, Noldus; TSE Multi-Conditioning System, TSE Systems) to cross-verified human annotation. We provide a set of videos-carefully annotated by several human raters-

SUBMITTER: Sturman O 

PROVIDER: S-EPMC7608249 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature