Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders

Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders

Recent neuroscience studies demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral measurements are now often collected using video cameras, resulting in an increased need for computer vision algorithms that extract useful informati...

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Bibliographic Details
Main Authors: Biderman, D. (Author), Bonacchi, N. (Author), Buchanan, E.K (Author), Cunningham, J.P (Author), Dipoppa, M. (Author), Friedman, Y. (Author), Miska, N.J (Author), Noel, J.-P (Author), Paninski, L. (Author), Rodriguez, E. (Author), Salzman, C.D (Author), Schartner, M. (Author), Socha, K. (Author), The International Brain Laboratory (Author), Urai, A.E (Author), Whiteway, M.R (Author), Wu, A. (Author), Zhou, J. (Author)
Format: Article
Language:English
Published: Public Library of Science 2021
Subjects:
algorithm
Algorithms
animal
animal behavior
animal experiment
Animals
article
artificial intelligence
Artificial Intelligence
autoencoder
Behavior, Animal
biology
Computational Biology
computer simulation
Computer Simulation
dimensionality reduction
male
Markov chain
Markov Chains
Mice
Models, Statistical
mouse
Neural Networks, Computer
nonhuman
statistical model
supervised machine learning
Supervised Machine Learning
unsupervised machine learning
Unsupervised Machine Learning
Video Recording
videorecording
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