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      Predictability and hierarchy inDrosophilabehavior

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      Proceedings of the National Academy of Sciences
      Proceedings of the National Academy of Sciences

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          Abstract

          <p id="d6428739e192">How an animal chooses to order its activities—moving, grooming, resting, and so on—is essential to its ability to survive, adapt, and reproduce. Here we investigate the temporal pattern of behaviors performed by fruit flies, finding that their movements are organized in a hierarchical manner that exhibits long time scales. This organization is likely advantageous for adaptability and ease of neural representation and provides hints as to the form of the fly’s internal representations of behavioral programs. </p><p class="first" id="d6428739e195">Even the simplest of animals exhibit behavioral sequences with complex temporal dynamics. Prominent among the proposed organizing principles for these dynamics has been the idea of a hierarchy, wherein the movements an animal makes can be understood as a set of nested subclusters. Although this type of organization holds potential advantages in terms of motion control and neural circuitry, measurements demonstrating this for an animal’s entire behavioral repertoire have been limited in scope and temporal complexity. Here, we use a recently developed unsupervised technique to discover and track the occurrence of all stereotyped behaviors performed by fruit flies moving in a shallow arena. Calculating the optimally predictive representation of the fly’s future behaviors, we show that fly behavior exhibits multiple time scales and is organized into a hierarchical structure that is indicative of its underlying behavioral programs and its changing internal states. </p>

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          Most cited references17

          • Record: found
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          Computation of channel capacity and rate-distortion functions

          R. Blahut (1972)
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            • Record: found
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            • Article: not found

            Topographic distance and watershed lines

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              Hierarchical genetic organization of human cortical surface area.

              Surface area of the cerebral cortex is a highly heritable trait, yet little is known about genetic influences on regional cortical differentiation in humans. Using a data-driven, fuzzy clustering technique with magnetic resonance imaging data from 406 twins, we parceled cortical surface area into genetic subdivisions, creating a human brain atlas based solely on genetically informative data. Boundaries of the genetic divisions corresponded largely to meaningful structural and functional regions; however, the divisions represented previously undescribed phenotypes different from conventional (non-genetically based) parcellation systems. The genetic organization of cortical area was hierarchical, modular, and predominantly bilaterally symmetric across hemispheres. We also found that the results were consistent with human-specific regions being subdivisions of previously described, genetically based lobar regionalization patterns.
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                Author and article information

                Journal
                Proceedings of the National Academy of Sciences
                Proc Natl Acad Sci USA
                Proceedings of the National Academy of Sciences
                0027-8424
                1091-6490
                October 18 2016
                October 18 2016
                October 18 2016
                October 04 2016
                : 113
                : 42
                : 11943-11948
                Article
                10.1073/pnas.1607601113
                5081631
                27702892
                c32706fb-7d3e-4037-b968-8796f2de198a
                © 2016

                Free to read

                http://www.pnas.org/site/misc/userlicense.xhtml

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