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10.1007/s10827-015-0581-5 http://scihub22266oqcxt.onion/10.1007/s10827-015-0581-5 C4762935!4762935!26560334     free     free     free Deprecated: Implicit conversion from float 205.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 205.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 205.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 205.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 205.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 205.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\26560334.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       J+Comput+Neurosci 2016 ; 40 (ä): 1-26 Nephropedia Template TP {{tp|p=26560334|t=2016. Dynamic effective connectivity in cortically embedded systems of recurrently coupled synfire chains |pdf=|usr=}}{{26560334}} gab.com Text Dynamic effective connectivity in cortically embedded systems of recurrently coupled synfire chains JO: J Comput Neurosci http://www.kidney.de/pget.php?&tw=1&pmid=26560334 #FOAvip As a candidate mechanism of neural representation, large numbers of synfire chains can efficiently be embedded in a balanced recurrent cortical network model. Here we study a model in which multiple synfire chains of variable strength are randomly coupled together to form a recurrent system. The system can be implemented both as a large-scale network of integrate-and-fire neurons and as a reduced model. The latter has binary-state pools as basic units but is otherwise isomorphic to the large-scale model, and provides an efficient tool for studying its behavior. Both the large-scale system and its reduced counterpart are able to sustain ongoing endogenous activity in the form of synfire waves, the proliferation of which is regulated by negative feedback caused by collateral noise. Within this equilibrium, diverse repertoires of ongoing activity are observed, including meta-stability and multiple steady states. These states arise in concert with an effective connectivity structure (ECS). The ECS admits a family of effective connectivity graphs (ECGs), parametrized by the mean global activity level. Of these graphs, the strongly connected components and their associated out-components account to a large extent for the observed steady states of the system. These results imply a notion of dynamic effective connectivity as governing neural computation with synfire chains, and related forms of cortical circuitry with complex topologies.Electronic supplementary material The online version of this article (doi:10.1007/s10827-015-0581-5) contains supplementary material, which is available to authorized users. Twit Text FOAVip Dynamic effective connectivity in cortically embedded systems of recurrently coupled synfire chains ????J Comput Neurosci http://www.kidney.de/pget.php?&tw=1&pmid=26560334 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26560334 #FOAvip English Wikipedia <ref>{{Cite pmid|26560334}}</ref> Dynamic effective connectivity in cortically embedded systems of recurrently coupled synfire chains #MMPMID26560334 Trengove C; Diesmann M; Leeuwen Cv J Comput Neurosci 2016[]; 40 (ä): 1-26 PMID26560334show ga As a candidate mechanism of neural representation, large numbers of synfire chains can efficiently be embedded in a balanced recurrent cortical network model. Here we study a model in which multiple synfire chains of variable strength are randomly coupled together to form a recurrent system. The system can be implemented both as a large-scale network of integrate-and-fire neurons and as a reduced model. The latter has binary-state pools as basic units but is otherwise isomorphic to the large-scale model, and provides an efficient tool for studying its behavior. Both the large-scale system and its reduced counterpart are able to sustain ongoing endogenous activity in the form of synfire waves, the proliferation of which is regulated by negative feedback caused by collateral noise. Within this equilibrium, diverse repertoires of ongoing activity are observed, including meta-stability and multiple steady states. These states arise in concert with an effective connectivity structure (ECS). The ECS admits a family of effective connectivity graphs (ECGs), parametrized by the mean global activity level. Of these graphs, the strongly connected components and their associated out-components account to a large extent for the observed steady states of the system. These results imply a notion of dynamic effective connectivity as governing neural computation with synfire chains, and related forms of cortical circuitry with complex topologies.Electronic supplementary material The online version of this article (doi:10.1007/s10827-015-0581-5) contains supplementary material, which is available to authorized users. äDynamic effective connectivity in cortically embedded systems of recur(epmc).pdf DeepDyve Pubget Overpricing