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10.1371/journal.pone.0184367 http://scihub22266oqcxt.onion/10.1371/journal.pone.0184367 C5603180!5603180!28922366     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       PLoS+One 2017 ; 12 (9): ä Nephropedia Template TP {{tp|p=28922366|t=2017. Optimizing information processing in neuronal networks beyond critical states |pdf=|usr=}}{{28922366}} gab.com Text Optimizing information processing in neuronal networks beyond critical states JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=28922366 #FOAvip Critical dynamics have been postulated as an ideal regime for neuronal networks in the brain, considering optimal dynamic range and information processing. Herein, we focused on how information entropy encoded in spatiotemporal activity patterns may vary in critical networks. We employed branching process based models to investigate how entropy can be embedded in spatiotemporal patterns. We determined that the information capacity of critical networks may vary depending on the manipulation of microscopic parameters. Specifically, the mean number of connections governed the number of spatiotemporal patterns in the networks. These findings are compatible with those of the real neuronal networks observed in specific brain circuitries, where critical behavior is necessary for the optimal dynamic range response but the uncertainty provided by high entropy as coded by spatiotemporal patterns is not required. With this, we were able to reveal that information processing can be optimized in neuronal networks beyond critical states. Twit Text FOAVip Optimizing information processing in neuronal networks beyond critical states ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=28922366 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28922366 #FOAvip English Wikipedia <ref>{{Cite pmid|28922366}}</ref> Optimizing information processing in neuronal networks beyond critical states #MMPMID28922366 Ferraz MSA; Melo-Silva HLC; Kihara AH PLoS One 2017[]; 12 (9): ä PMID28922366show ga Critical dynamics have been postulated as an ideal regime for neuronal networks in the brain, considering optimal dynamic range and information processing. Herein, we focused on how information entropy encoded in spatiotemporal activity patterns may vary in critical networks. We employed branching process based models to investigate how entropy can be embedded in spatiotemporal patterns. We determined that the information capacity of critical networks may vary depending on the manipulation of microscopic parameters. Specifically, the mean number of connections governed the number of spatiotemporal patterns in the networks. These findings are compatible with those of the real neuronal networks observed in specific brain circuitries, where critical behavior is necessary for the optimal dynamic range response but the uncertainty provided by high entropy as coded by spatiotemporal patterns is not required. With this, we were able to reveal that information processing can be optimized in neuronal networks beyond critical states. äOptimizing information processing in neuronal networks beyond critical(epmc).pdf DeepDyve Pubget Overpricing