Format

Send to

Choose Destination
Science. 2013 Nov 1;342(6158):1238406. doi: 10.1126/science.1238406.

Cortical high-density counterstream architectures.

Author information

1
Stem cell and Brain Research Institute, INSERM U846, 18 Avenue Doyen Lépine, 69500 Bron, France.
2
Université de Lyon, Université Lyon I, 69003 Lyon, France.
3
Yale University, Department of Neurobiology, New Haven, CT 06520, USA.
4
Faculty of Physics, Babeş-Bolyai University, Cluj-Napoca, 400084 Romania.
5
Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, MO 63110-1093, USA.
6
Department of Physics and Interdisciplinary Center for Network Science and Applications, University of Notre Dame, Notre Dame, IN 46556, USA.
7
Max Planck Institute for the Physics of Complex Systems, 01187 Dresden, Germany.
#
Contributed equally

Abstract

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.

PMID:
24179228
PMCID:
PMC3905047
DOI:
10.1126/science.1238406
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for HighWire Icon for PubMed Central
Loading ...
Support Center