期刊
SCIENCE
卷 342, 期 6158, 页码 578-+出版社
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/science.1238406
关键词
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资金
- LabEx CORTEX [ANR-11-LABX-0042]
- National Institute of Mental Health [R01 MH60974]
- Notre Dame's Interdisciplinary Center for Network Science and Applications (iCeNSA)
- U.S. Air Force Office of Scientific Research and Defense Advanced Research Projects Agency [FA9550-12-1-0405]
- [FP6-2005 IST-1583]
- [FP7-2007 ICT-216593]
- [ANR-11-BSV4-501]
- [PN-II-RU-TE-2011-3-0121]
- [FP7-PEOPLE-2011-IIF-299915]
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.
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