A machine learning method for action detection is developed and applied toward mouse grooming behavior.

Scratch-AID, a deep learning-based system for automatic quantification of mouse scratching behavior, could replace labor-intensive manual quantification and facilitate high through-put anti-itch drug screening.

The Mouse Action Recognition System is a computational pipeline for automated classification of social behaviors in freely interacting mice, accompanied by a graphical interface for analysis of multimodal neuroscience datasets.

The three main types of inhibitory neurons in mouse primary visual cortex respond differently to locomotion in darkness and during visual stimulation, revealing context-dependent responses to changes in behavioral state.

Electrophysiology and optogenetic circuit manipulation in the mouse visual system reveal that corticothalamic feedback and behavioral state both have robust modulatory, yet largely independent influences on thalamic responses to naturalistic movies.

A fully automated workflow for high-throughput mouse behavioral and optogenetic experiments in homecage reveals involvement of brain regions in tactile decision-making.

Normalizing Tcf4 expression during early postnatal development improves behavioral outcomes in Pitt-Hopkins syndrome (PTHS) model mice, suggesting that genetic therapies are feasible in individuals with PTHS.

Neurexins are presynaptic adhesion molecules in the serotonin system which regulate neuron survival and serotonin neuromodulation through active zone formation and serotonin release.

A mouse mutant in which nearly all dentate gyrus granule cells fail to develop provides a new window on the role of the dentate gyrus in spatial learning and memory.

The brain processes the same multisensory stimulus differently when the way it sounds, as opposed to the way it looks, is useful for making a decision.