FreiPose: A Deep Learning Framework for Precise Animal Motion Capture in 3D Spaces
Zimmermann, C.; Schneider, A.; Alyahyay, M.; Brox, T. S.; Diester, I.
Show abstract
The increasing awareness of the impact of spontaneous movements on neuronal activity has raised the need to track behavior. We present FreiPose, a versatile learning-based framework to directly capture 3D motion of freely definable points with high precision (median error < 3.5% body length, 41.9% improvement compared to state-of-the-art) and high reliability (82.8% keypoints within < 5% body length error boundary, 72.0% improvement). The versatility of FreiPose is demonstrated in two experiments: (1) By tracking freely moving rats with simultaneous electrophysiological recordings in motor cortex, we identified neuronal tuning to behavioral states and individual paw trajectories. (2) We inferred time points of optogenetic stimulation in rat motor cortex from the measured pose across individuals and attributed the stimulation effect automatically to body parts. The versatility and accuracy of FreiPose open up new possibilities for quantifying behavior of freely moving animals and may lead to new ways of setting up experiments.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Conserved structures of neural activity in sensorimotor cortex of freely moving rats allow cross-subject decoding 95%
- Coregistration of heading to visual cues in retrosplenial cortex 94%
- Intrinsic dynamics enhance temporal stability of stimulus representation along rodent visual cortical hierarchies 94%
Similar papers in this journal
- Descending neuron population dynamics during odor-evoked and spontaneous limb-dependent behaviors 95%
- A conserved code for anatomy: Neurons throughout the brain embed robust signatures of their anatomical location into spike trains. 94%
- NINscope: a versatile miniscope for multi-region circuit investigations 94%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.