Real-Time Closed-Loop Feedback in Behavioral Time Scales Using DeepLabCut.

ABSTRACT: Computer vision approaches have made significant inroads into offline tracking of behavior and estimating animal poses. In particular, because of their versatility, deep-learning approaches have been gaining attention in behavioral tracking without any markers. Here, we developed an approach using DeepLabCut for real-time estimation of movement. We trained a deep-neural network (DNN) offline with high-speed video data of a mouse whisking, then transferred the trained network to work with the same mouse, whisking in real-time. With this approach, we tracked the tips of three whiskers in an arc and converted positions into a TTL output within behavioral time scales, i.e., 10.5 ms. With this approach, it is possible to trigger output based on movement of individual whiskers, or on the distanc

SUBMITTER: Sehara K 

PROVIDER: S-EPMC8174057 | biostudies-literature | 2021 Mar-Apr

REPOSITORIES: biostudies-literature