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10.1038/nphys4268 http://scihub22266oqcxt.onion/10.1038/nphys4268 C5798649!5798649!29422941     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Nat+Phys 2018 ; 14 (ä): 91-8 Nephropedia Template TP {{tp|p=29422941|t=2018. Role of Graph Architecture in Controlling Dynamical Networks with Applications to Neural Systems |pdf=|usr=}}{{29422941}} gab.com Text Role of Graph Architecture in Controlling Dynamical Networks with Applications to Neural Systems JO: Nat Phys http://www.kidney.de/pget.php?&tw=1&pmid=29422941 #FOAvip Networked systems display complex patterns of interactions between components. In physical networks, these interactions often occur along structural connections that link components in a hard-wired connection topology, supporting a variety of system-wide dynamical behaviors such as synchronization. While descriptions of these behaviors are important, they are only a first step towards understanding and harnessing the relationship between network topology and system behavior. Here, we use linear network control theory to derive accurate closed-form expressions that relate the connectivity of a subset of structural connections (those linking driver nodes to non-driver nodes) to the minimum energy required to control networked systems. To illustrate the utility of the mathematics, we apply this approach to high-resolution connectomes recently reconstructed from Drosophila, mouse, and human brains. We use these principles to suggest an advantage of the human brain in supporting diverse network dynamics with small energetic costs while remaining robust to perturbations, and to perform clinically accessible targeted manipulation of the brain?s control performance by removing single edges in the network. Generally, our results ground the expectation of a control system?s behavior in its network architecture, and directly inspire new directions in network analysis and design via distributed control. Twit Text FOAVip Role of Graph Architecture in Controlling Dynamical Networks with Applications to Neural Systems ????Nat Phys http://www.kidney.de/pget.php?&tw=1&pmid=29422941 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29422941 #FOAvip English Wikipedia <ref>{{Cite pmid|29422941}}</ref> Role of Graph Architecture in Controlling Dynamical Networks with Applications to Neural Systems #MMPMID29422941 Kim JZ; Soffer JM; Kahn AE; Vettel JM; Pasqualetti F; Bassett DS Nat Phys 2018[]; 14 (ä): 91-8 PMID29422941show ga Networked systems display complex patterns of interactions between components. In physical networks, these interactions often occur along structural connections that link components in a hard-wired connection topology, supporting a variety of system-wide dynamical behaviors such as synchronization. While descriptions of these behaviors are important, they are only a first step towards understanding and harnessing the relationship between network topology and system behavior. Here, we use linear network control theory to derive accurate closed-form expressions that relate the connectivity of a subset of structural connections (those linking driver nodes to non-driver nodes) to the minimum energy required to control networked systems. To illustrate the utility of the mathematics, we apply this approach to high-resolution connectomes recently reconstructed from Drosophila, mouse, and human brains. We use these principles to suggest an advantage of the human brain in supporting diverse network dynamics with small energetic costs while remaining robust to perturbations, and to perform clinically accessible targeted manipulation of the brain?s control performance by removing single edges in the network. Generally, our results ground the expectation of a control system?s behavior in its network architecture, and directly inspire new directions in network analysis and design via distributed control. äRole of Graph Architecture in Controlling Dynamical Networks with Appl(epmc).pdf DeepDyve Pubget Overpricing