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      Impact of modular organization on dynamical richness in cortical networks

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          Abstract

          Balance of functional integrability and spatial segregation mediates dynamical richness in modular cortical networks.

          Abstract

          As in many naturally formed networks, the brain exhibits an inherent modular architecture that is the basis of its rich operability, robustness, and integration-segregation capacity. However, the mechanisms that allow spatially segregated neuronal assemblies to swiftly change from localized to global activity remain unclear. Here, we integrate microfabrication technology with in vitro cortical networks to investigate the dynamical repertoire and functional traits of four interconnected neuronal modules. We show that the coupling among modules is central. The highest dynamical richness of the network emerges at a critical connectivity at the verge of physical disconnection. Stronger coupling leads to a persistently coherent activity among the modules, while weaker coupling precipitates the activity to be localized solely within the modules. An in silico modeling of the experiments reveals that the advent of coherence is mediated by a trade-off between connectivity and subquorum firing, a mechanism flexible enough to allow for the coexistence of both segregated and integrated activities. Our results unveil a new functional advantage of modular organization in complex networks of nonlinear units.

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          Most cited references39

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          Community detection in graphs

          The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.
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            Structural and functional brain networks: from connections to cognition.

            How rich functionality emerges from the invariant structural architecture of the brain remains a major mystery in neuroscience. Recent applications of network theory and theoretical neuroscience to large-scale brain networks have started to dissolve this mystery. Network analyses suggest that hierarchical modular brain networks are particularly suited to facilitate local (segregated) neuronal operations and the global integration of segregated functions. Although functional networks are constrained by structural connections, context-sensitive integration during cognition tasks necessarily entails a divergence between structural and functional networks. This degenerate (many-to-one) function-structure mapping is crucial for understanding the nature of brain networks. The emergence of dynamic functional networks from static structural connections calls for a formal (computational) approach to neuronal information processing that may resolve this dialectic between structure and function.
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              Efficient Behavior of Small-World Networks

              We introduce the concept of efficiency of a network, measuring how efficiently it exchanges information. By using this simple measure small-world networks are seen as systems that are both globally and locally efficient. This allows to give a clear physical meaning to the concept of small-world, and also to perform a precise quantitative a nalysis of both weighted and unweighted networks. We study neural networks and man-made communication and transportation systems and we show that the underlying general principle of their construction is in fact a small-world principle of high efficiency.
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                Author and article information

                Journal
                Sci Adv
                Sci Adv
                SciAdv
                advances
                Science Advances
                American Association for the Advancement of Science
                2375-2548
                November 2018
                14 November 2018
                : 4
                : 11
                : eaau4914
                Affiliations
                [1 ]WPI–Advanced Institute for Materials Research (WPI-AIMR), Tohoku University, Sendai 980-8577, Japan.
                [2 ]Research Institute for Electrical Communication, Tohoku University, Sendai 980-8577, Japan.
                [3 ]Graduate School of Science and Engineering, Yamagata University, Yamagata 992-8510, Japan.
                [4 ]Faculty of Science and Engineering, Waseda University, Tokyo 169-8555, Japan.
                [5 ]Departament de Física de la Matèria Condensada, Universitat de Barcelona, Barcelona 08028, Catalonia, Spain.
                [6 ]Universitat de Barcelona Institute of Complex Systems (UBICS), Barcelona 08028, Catalonia, Spain.
                Author notes
                [* ]Corresponding author. Email: hideaki.yamamoto.e3@ 123456tohoku.ac.jp (H.Y.); jordi.soriano@ 123456ub.edu (J.S.)
                [†]

                Present address: Kansei Fukushi Research Institute, Tohoku Fukushi University, Sendai 989-3201, Japan.

                Author information
                http://orcid.org/0000-0003-3362-5376
                http://orcid.org/0000-0002-7764-5806
                http://orcid.org/0000-0003-3912-357X
                http://orcid.org/0000-0003-2676-815X
                http://orcid.org/0000-0002-3043-7698
                Article
                aau4914
                10.1126/sciadv.aau4914
                6235526
                30443598
                8e44a3e4-163c-4131-9412-d0ba311536dc
                Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

                This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

                History
                : 21 June 2018
                : 16 October 2018
                Funding
                Funded by: doi http://dx.doi.org/10.13039/100010664, H2020 Future and Emerging Technologies;
                Award ID: 713140 MESOBRAIN
                Funded by: doi http://dx.doi.org/10.13039/501100001691, Japan Society for the Promotion of Science;
                Award ID: 15K17449, 18H03325
                Funded by: doi http://dx.doi.org/10.13039/501100002809, Generalitat de Catalunya;
                Award ID: 2014-SGR-878
                Funded by: doi http://dx.doi.org/10.13039/501100003329, Ministerio de Economía y Competitividad;
                Award ID: FIS2016-78507-C2-2-P
                Funded by: doi http://dx.doi.org/10.13039/501100006467, Research Institute of Electrical Communication, Tohoku University;
                Categories
                Research Article
                Research Articles
                SciAdv r-articles
                Network Science
                Neuroscience
                Neuroscience
                Custom metadata
                Jeanelle Ebreo

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