Real-time, low-latency closed-loop feedback using markerless posture tracking

Alexander Mathis, Mackenzie Mathis
2020
Rapport ou document de travail

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 (1) noninvasive, (2) low-latency, and (3) provide interfaces to trigger external hardware based on posture (i.e., not just objectbased-tracking). Recent advances in pose estimation with deep learning allows researchers to train deep neural networks to accurately quantify a wide variety of animal behaviors. In extending our efforts towards the animal pose estimation toolbox DeepLabCut, here, we provide a new DeepLabCut-Live! package that achieves low-latency real-time pose estimation (within 15 ms, at >100 FPS), with an additional forwardprediction module that achieves zero-latency feedback. We also provide three options for using this tool with ease: a stand-alone GUI (called DLC-Live! GUI), integration into Bonsai and into 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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Alexander Mathis, Mackenzie Mathis
2020
Rapport ou document de travail

Résumé

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https://infoscience.epfl.ch/record/281437?ln=fr

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