PT  - JOURNAL ARTICLE
AU  - Keisuke Sehara
AU  - Paul Zimmer-Harwood
AU  - Matthew E. Larkum
AU  - Robert N. S. Sachdev
TI  - Real-Time Closed-Loop Feedback in Behavioral Time Scales Using DeepLabCut
AID  - 10.1523/ENEURO.0415-20.2021
DP  - 2021 Mar 01
TA  - eneuro
PG  - ENEURO.0415-20.2021
VI  - 8
IP  - 2
4099  - http://www.eneuro.org/content/8/2/ENEURO.0415-20.2021.short
4100  - http://www.eneuro.org/content/8/2/ENEURO.0415-20.2021.full
SO  - eNeuro2021 Mar 01; 8
AB  - 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 distance between adjacent whiskers. Flexible closed-loop systems like the one we have deployed here can complement optogenetic approaches and can be used to directly manipulate the relationship between movement and neural activity.