The AG Schwarz developed a closed loop, deep learning-based real time posture detection tool allowing to autonomously conduct behavioral experiments.

The paper is published in Communications Biology. 


Jens F. Schweihoff, Matvey Loshakov, Irina Pavlova, Laura Kück, Laura A. Ewell, Martin K. Schwarz (2021) DeepLabStream: Closing the loop using deep learning-based markerless, real time posture detection, Commun. Biol. 


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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