16
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Evolution of network architecture in a granular material under compression

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Related collections

          Most cited references53

          • Record: found
          • Abstract: not found
          • Article: not found

          Granular solids, liquids, and gases

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Multilayer networks

              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Dynamic reconfiguration of human brain networks during learning.

              Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. These two attributes--flexibility and selection--must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development, and optimal network function. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we investigate the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales. Our results indicate that flexibility, which we measure by the allegiance of nodes to modules, in one experimental session predicts the relative amount of learning in a future session. We also develop a general statistical framework for the identification of modular architectures in evolving systems, which is broadly applicable to disciplines where network adaptability is crucial to the understanding of system performance.
                Bookmark

                Author and article information

                Journal
                PLEEE8
                Physical Review E
                Phys. Rev. E
                American Physical Society (APS)
                2470-0045
                2470-0053
                September 2016
                September 23 2016
                : 94
                : 3
                Article
                10.1103/PhysRevE.94.032908
                27739788
                faf01961-8040-42db-b9c8-f66385805a43
                © 2016

                http://creativecommons.org/licenses/by/3.0/

                History

                Comments

                Comment on this article