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

doi:10.7554/eLife.61909

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

2020-12-08

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Mackenzie Weygandt Mathis

DOI：10.7554/eLife.61909

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Abstract

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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.

Keywords:

POSE ESTIMATION

BEHAVIOR

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

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