Automated home-cage behavioural phenotyping of mice.

By Hueihan Jhuang, Estíbaliz Garrote, Jim Mutch, Xinlin Yu, Vinita Khilnani, Tomaso A. Poggio, Andrew D. Steele, Thomas Serre. Nature Communications, 2010

301 citations recorded by the source corpus.

Subfields: Cell Biology, Cellular and Molecular Neuroscience, Small Animals

View Paper: https://www.semanticscholar.org/paper/0d42b204c85668b4d6b59a89e8ab8c088d183e00

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

1. Fabio Anselmi
2. Tomaso A. Poggio
3. Joel Z. Leibo
4. Thomas Serre

Citations in the corpus

• Motion Interchange Patterns for Action Recognition in Unconstrained Videos (2012, background): “The last was recently shown to perform on-par with humans in recognizing rodent behaviors [33].”
• Unsupervised learning of invariant representations (2016, background): “Another HMAX variant added a time dimension for action recognition [60], outperforming both human annotators and a state-of-the-art commercial system on a mouse behavioral phenotyping task.”
• Unsupervised learning of invariant representations with low sample complexity: the magic of sensory cortex or a new framework for machine learning? (2013, background): “Another HMAX variant added a time dimension for action recognition [60], outper-forming both human annotators and a state-of-the-art commercial system on a mouse behavioral phenotyping task.”
• HMDB: A large video database for human motion recognition (2011, background): “[8], which is based on a model of the dorsal stream of the visual cortex and was recently shown to achieve on-par with humans for the recognition of rodent behaviors in the homecage environment [7].”
• Unsupervised Learning of Invariant Representations in Hierarchical Architectures (2013, background): “Another HMAX variant added a time dimension for action recognition [20], outperforming both human annotators and a state-of-the-art commercial system on a mouse behavioral phenotyping task.”
• Mapping Sub-Second Structure in Mouse Behavior. (2015, background): “Instead, traditional behavioral classification approaches have been instantiated in silico, enabling machine vision algorithms to replace tedious and unreliable human scoring of videotaped behavior (de Chaumont et al., 2012; Jhuang et al., 2010; Kabra et al., 2013; Weissbrod et al., 2013).”