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The identification of animal behavior in video is a critical but time-consuming task in many areas of research. Here, we introduce DeepAction, a deep learning-based toolbox for automatically annotating animal behavior in video. Our approach uses features extracted from raw video frames by a pretrained convolutional neural network to train a recurrent neural network classifier. We evaluate the classifier on two benchmark rodent datasets and one octopus dataset. We show that it achieves high accuracy, requires little training data, and surpasses both human agreement and most comparable existing methods. We also create a confidence score for classifier output, and show that our method provides an accurate estimate of classifier performance and reduces the time required by human annotators to review and correct automatically-produced annotations. We release our system and accompanying annotation interface as an open-source MATLAB toolbox.

Learning and recognition can be improved by sorting novel items into categories and subcategories. Such hierarchical categorization is easy when it can be performed according to learned rules (e.g., “if car, then automatic or stick shift” or “if boat, then motor or sail”). Here, we present results showing that human participants acquire categorization rules for new visual hierarchies rapidly, and that, as they do, corresponding hierarchical representations of the categorized stimuli emerge in patterns of neural activation in the dorsal striatum and in posterior frontal and parietal cortex. Participants learned to categorize novel visual objects into a hierarchy with superordinate and subordinate levels based on the objects' shape features, without having been told the categorization rules for doing so. On each trial, participants were asked to report the category and subcategory of the object, after which they received feedback about the correctness of their categorization responses. Participants trained over the course of a one‐hour‐long session while their brain activation was measured using functional magnetic resonance imaging. Over the course of training, significant hierarchy learning took place as participants discovered the nested categorization rules, as evidenced by the occurrence of a learning trial, after which performance suddenly increased. This learning was associated with increased representational strength of the newly acquired hierarchical rules in a corticostriatal network including the posterior frontal and parietal cortex and the dorsal striatum. We also found evidence suggesting that reinforcement learning in the dorsal striatum contributed to hierarchical rule learning.