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3 Janelia Publications

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    12/01/12 | JAABA: interactive machine learning for automatic annotation of animal behavior.
    Kabra M, Robie AA, Rivera-Alba M, Branson S, Branson K
    Nature Methods. 2012 Dec;10:64-7

    We present a machine learning–based system for automatically computing interpretable, quantitative measures of animal behavior. Through our interactive system, users encode their intuition about behavior by annotating a small set of video frames. These manual labels are converted into classifiers that can automatically annotate behaviors in screen-scale data sets. Our general-purpose system can create a variety of accurate individual and social behavior classifiers for different organisms, including mice and adult and larval Drosophila.

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    11/01/12 | Learning animal social behavior from trajectory features.
    Eyjolfsdottir E, Burgos-Artizzu XP, Branson S, Branson K, Anderson D, Perona P
    Workshop on Visual Observation and Analysis of Animal and Insect Behavior. 2012 Nov:
    06/20/12 | A simple strategy for detecting moving objects during locomotion revealed by animal-robot interactions.
    Zabala F, Polidoro P, Robie AA, Branson K, Perona P, Dickinson MH
    Current Biology. 2012 Jun 20;22(14):1344-50. doi: 10.1016/j.cub.2012.05.024

    An important role of visual systems is to detect nearby predators, prey, and potential mates [1], which may be distinguished in part by their motion. When an animal is at rest, an object moving in any direction may easily be detected by motion-sensitive visual circuits [2, 3]. During locomotion, however, this strategy is compromised because the observer must detect a moving object within the pattern of optic flow created by its own motion through the stationary background. However, objects that move creating back-to-front (regressive) motion may be unambiguously distinguished from stationary objects because forward locomotion creates only front-to-back (progressive) optic flow. Thus, moving animals should exhibit an enhanced sensitivity to regressively moving objects. We explicitly tested this hypothesis by constructing a simple fly-sized robot that was programmed to interact with a real fly. Our measurements indicate that whereas walking female flies freeze in response to a regressively moving object, they ignore a progressively moving one. Regressive motion salience also explains observations of behaviors exhibited by pairs of walking flies. Because the assumptions underlying the regressive motion salience hypothesis are general, we suspect that the behavior we have observed in Drosophila may be widespread among eyed, motile organisms.

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