ArticleeNeuro
Real-Time Closed-Loop Feedback in Behavioral Time Scales Using DeepLabCut.
Article in eNeuro. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 28 citations in OpenAlex.
- Orofacial Movements: Individuality and Stereotypy When Mice Move a Single Whisker to Touch.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
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- Dog facial landmarks detection and its applications for facial analysis.Scientific reports · 2025Article
- Non-consummatory behavior signals predict aversion-resistant alcohol drinking in head-fixed mice.Neuropharmacology · 2024Article
- PiRATeMC: A highly flexible, scalable, and low-cost system for obtaining high quality video recordings for behavioral neuroscience.Addiction neuroscience · 2023Article
- Non-Consummatory Behavior Signals Predict Aversion-Resistant Alcohol Drinking in Head-Fixed Mice.bioRxiv : the preprint server for biology · 2023Article
- Evolution and adoption of contributor role ontologies and taxonomies.Learned publishing : journal of the Association of Learned and Professional Society Publishers · 2023Article
- Wide-angle, monocular head tracking using passive markers.Journal of neuroscience methods · 2022Article
- Integration of somatosensory and motor-related information in the auditory system.Frontiers in neuroscience · 2022Review
- Efficient training approaches for optimizing behavioral performance and reducing head fixation time.PloS one · 2022Article
- Article
- Closed-loop automated reaching apparatus (CLARA) for interrogating complex motor behaviors.Journal of neural engineering · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
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 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.