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      Sparse short-distance connections enhance calcium wave propagation in a 3D model of astrocyte networks

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          Abstract

          Traditionally, astrocytes have been considered to couple via gap-junctions into a syncytium with only rudimentary spatial organization. However, this view is challenged by growing experimental evidence that astrocytes organize as a proper gap-junction mediated network with more complex region-dependent properties. On the other hand, the propagation range of intercellular calcium waves (ICW) within astrocyte populations is as well highly variable, depending on the brain region considered. This suggests that the variability of the topology of gap-junction couplings could play a role in the variability of the ICW propagation range. Since this hypothesis is very difficult to investigate with current experimental approaches, we explore it here using a biophysically realistic model of three-dimensional astrocyte networks in which we varied the topology of the astrocyte network, while keeping intracellular properties and spatial cell distribution and density constant. Computer simulations of the model suggest that changing the topology of the network is indeed sufficient to reproduce the distinct ranges of ICW propagation reported experimentally. Unexpectedly, our simulations also predict that sparse connectivity and restriction of gap-junction couplings to short distances should favor propagation while long–distance or dense connectivity should impair it. Altogether, our results provide support to recent experimental findings that point toward a significant functional role of the organization of gap-junction couplings into proper astroglial networks. Dynamic control of this topology by neurons and signaling molecules could thus constitute a new type of regulation of neuron-glia and glia-glia interactions.

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          Most cited references79

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          Statistical mechanics of complex networks

          Complex networks describe a wide range of systems in nature and society, much quoted examples including the cell, a network of chemicals linked by chemical reactions, or the Internet, a network of routers and computers connected by physical links. While traditionally these systems were modeled as random graphs, it is increasingly recognized that the topology and evolution of real networks is governed by robust organizing principles. Here we review the recent advances in the field of complex networks, focusing on the statistical mechanics of network topology and dynamics. After reviewing the empirical data that motivated the recent interest in networks, we discuss the main models and analytical tools, covering random graphs, small-world and scale-free networks, as well as the interplay between topology and the network's robustness against failures and attacks.
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            The self-tuning neuron: synaptic scaling of excitatory synapses.

            Homeostatic synaptic scaling is a form of synaptic plasticity that adjusts the strength of all of a neuron's excitatory synapses up or down to stabilize firing. Current evidence suggests that neurons detect changes in their own firing rates through a set of calcium-dependent sensors that then regulate receptor trafficking to increase or decrease the accumulation of glutamate receptors at synaptic sites. Additional mechanisms may allow local or network-wide changes in activity to be sensed through parallel pathways, generating a nested set of homeostatic mechanisms that operate over different temporal and spatial scales.
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              Voronoi diagrams---a survey of a fundamental geometric data structure

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                Author and article information

                Contributors
                Journal
                Front Comput Neurosci
                Front Comput Neurosci
                Front. Comput. Neurosci.
                Frontiers in Computational Neuroscience
                Frontiers Media S.A.
                1662-5188
                16 April 2014
                2014
                : 8
                : 45
                Affiliations
                [1] 1EPI Beagle, INRIA Rhône-Alpes Villeurbanne, France
                [2] 2LIRIS, UMR 5205 CNRS-INSA, Université de Lyon Villeurbanne, France
                [3] 3School of Physics and Astronomy, Tel Aviv University Ramat Aviv, Israel
                [4] 4Center for Theoretical Biological Physics, Rice University Houston, TX, USA
                Author notes

                Edited by: David Hansel, University of Paris, France

                Reviewed by: Laurent Venance, Collège de France, France; Julijana Gjorgjieva, Harvard University, USA

                *Correspondence: Hugues Berry, EPI Beagle, LIRIS, INRIA Rhône-Alpes, UMR5205, Université de Lyon, Batiment CEI-1, 66 Blvd Niels Bohr, 69100 Villeurbanne, France e-mail: hugues.berry@ 123456inria.fr

                This article was submitted to the journal Frontiers in Computational Neuroscience.

                Article
                10.3389/fncom.2014.00045
                3997029
                24795613
                2f73d9c3-eeaf-4343-9233-6afd1564b1db
                Copyright © 2014 Lallouette, De Pittà, Ben-Jacob and Berry.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 18 December 2013
                : 27 March 2013
                Page count
                Figures: 8, Tables: 2, Equations: 7, References: 101, Pages: 18, Words: 14351
                Categories
                Neuroscience
                Original Research Article

                Neurosciences
                glial cells,astrocytes,gap-junctions,wave propagation,network topology
                Neurosciences
                glial cells, astrocytes, gap-junctions, wave propagation, network topology

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