<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Markov, Nikola T</style></author><author><style face="normal" font="default" size="100%">Ercsey-Ravasz, Mária</style></author><author><style face="normal" font="default" size="100%">Van Essen, David C</style></author><author><style face="normal" font="default" size="100%">Knoblauch, Kenneth</style></author><author><style face="normal" font="default" size="100%">Toroczkai, Zoltán</style></author><author><style face="normal" font="default" size="100%">Kennedy, Henry</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Cortical high-density counterstream architectures.</style></title><secondary-title><style face="normal" font="default" size="100%">Science</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Science</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Cerebral Cortex</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Mental Processes</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Neurological</style></keyword><keyword><style  face="normal" font="default" size="100%">Nerve Net</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2013 Nov 1</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/24179228</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">342</style></volume><pages><style face="normal" font="default" size="100%">1238406</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Small-world networks provide an appealing description of cortical architecture owing to their capacity for integration and segregation combined with an economy of connectivity. Previous reports of low-density interareal graphs and apparent small-world properties are challenged by data that reveal high-density cortical graphs in which economy of connections is achieved by weight heterogeneity and distance-weight correlations. These properties define a model that predicts many binary and weighted features of the cortical network including a core-periphery, a typical feature of self-organizing information processing systems. Feedback and feedforward pathways between areas exhibit a dual counterstream organization, and their integration into local circuits constrains cortical computation. Here, we propose a bow-tie representation of interareal architecture derived from the hierarchical laminar weights of pathways between the high-efficiency dense core and periphery.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">6158</style></issue><notes><style face="normal" font="default" size="100%">http://www.sciencemag.org/content/342/6158/1238406.full</style></notes><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/24179228?dopt=Abstract</style></custom1></record></records></xml>