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      Nano-topography Enhances Communication in Neural Cells Networks

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          Abstract

          Neural cells are the smallest building blocks of the central and peripheral nervous systems. Information in neural networks and cell-substrate interactions have been heretofore studied separately. Understanding whether surface nano-topography can direct nerve cells assembly into computational efficient networks may provide new tools and criteria for tissue engineering and regenerative medicine. In this work, we used information theory approaches and functional multi calcium imaging (fMCI) techniques to examine how information flows in neural networks cultured on surfaces with controlled topography. We found that substrate roughness S a affects networks topology. In the low nano-meter range, S a  = 0–30  nm, information increases with S a . Moreover, we found that energy density of a network of cells correlates to the topology of that network. This reinforces the view that information, energy and surface nano-topography are tightly inter-connected and should not be neglected when studying cell-cell interaction in neural tissue repair and regeneration.

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          Most cited references38

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          The structure and function of complex networks

          M. Newman (2003)
          Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
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            Neural networks and physical systems with emergent collective computational abilities.

            J Hopfield (1982)
            Computational properties of use of biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.
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              Transmembrane crosstalk between the extracellular matrix--cytoskeleton crosstalk.

              Integrin-mediated cell adhesions provide dynamic, bidirectional links between the extracellular matrix and the cytoskeleton. Besides having central roles in cell migration and morphogenesis, focal adhesions and related structures convey information across the cell membrane, to regulate extracellular-matrix assembly, cell proliferation, differentiation, and death. This review describes integrin functions, mechanosensors, molecular switches and signal-transduction pathways activated and integrated by adhesion, with a unifying theme being the importance of local physical forces.
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                Author and article information

                Contributors
                francesco.gentile2@unina.it
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                29 August 2017
                29 August 2017
                2017
                : 7
                : 9841
                Affiliations
                [1 ]ISNI 0000 0001 2168 2547, GRID grid.411489.1, Department of Experimental and Clinical Medicine, , University of Magna Graecia, ; 88100 Catanzaro, Italy
                [2 ]ISNI 0000 0004 1764 2907, GRID grid.25786.3e, , Istituto Italiano di Tecnologia, ; Via Morego 30, 16163 Genova, Italy
                [3 ]ISNI 0000 0001 0790 385X, GRID grid.4691.a, Department of Electrical Engineering and Information Technology, , University of Naples, ; 80125 Naples, Italy
                [4 ]ISNI 0000 0001 1926 5090, GRID grid.45672.32, , King Abdullah University of Science and Technology, ; Thuwal, 23955-6900 Saudi Arabia
                Author information
                http://orcid.org/0000-0003-0566-057X
                http://orcid.org/0000-0002-9053-3139
                Article
                9741
                10.1038/s41598-017-09741-w
                5575309
                28851984
                20cd9808-3b1a-46d0-b16f-f074fec7ca63
                © The Author(s) 2017

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 30 May 2017
                : 28 July 2017
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