bioRxiv · 10.1101/2019.12.20.884478
DeepLabStream: Closing the loop using deep learning-based markerless, real-time posture detec-tion
Abstract
In general, animal behavior can be described as the neuronal-driven sequence of reoccurring postures through time. Current technologies enable offline pose estimation with high spatio-temporal resolution, however to understand complex behaviors, it is necessary to correlate the behavior with neuronal activity in real-time. Here we present DeepLabStream, a highly versatile, closed-loop solution for freely moving mice that can autonomously conduct behavioral experiments ranging from behavior-based learning tasks to posture-dependent optogenetic stimulation. DeepLabStream has a temporal resolution in the millisecond range, can operate with multiple devices and can be easily tailored to a wide range of species and experimental designs. We employ DeepLabStream to autonomously run a second-order olfactory conditioning task for freely moving mice and to deliver optogenetic stimuli based on mouse head-direction.
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Schweihoff, J. F., Loshakov, M., Pavlova, I., Kück, L., Ewell, L. A., Schwarz, M. K.. 2019-12-23. DeepLabStream: Closing the loop using deep learning-based markerless, real-time posture detec-tion. https://doi.org/10.1101/2019.12.20.884478
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