Journal
ELIFE
Volume 9, Issue -, Pages -Publisher
eLIFE SCIENCES PUBL LTD
DOI: 10.7554/eLife.61909
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Funding
- Chan Zuckerberg Initiative
- National Science Foundation [1309047]
- Rowland Institute at Harvard
- Harvard Brain Science Initiative
- Direct For Education and Human Resources
- Division Of Graduate Education [1309047] Funding Source: National Science Foundation
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The ability to control a behavioral task or stimulate neural activity based on animal behavior in real-time is an important tool for experimental neuroscientists. Ideally, such tools are noninvasive, low-latency, and provide interfaces to trigger external hardware based on posture. Recent advances in pose estimation with deep learning allows researchers to train deep neural networks to accurately quantify a wide variety of animal behaviors. Here, we provide a new DeepLabCut Live ! package that achieves low-latency real-time pose estimation (within 15 ms, >100 FPS), with an additional forward -prediction module that achieves zero-latency feedback, and a dynamic-cropping mode that allows for higher inference speeds. We also provide three options for using this tool with ease: (1) a stand-alone GUI (called DLC Live! GUI), and integration into (2) Bonsai, and (3) Autopilot. Lastly, we benchmarked performance on a wide range of systems so that experimentalists can easily decide what hardware is required for their needs.
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