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Cerebral Cortex Advance Access published online on April 9, 2008
Cerebral Cortex, doi:10.1093/cercor/bhn047
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© 2008 The Authors
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

A Small World of Neuronal Synchrony

Shan Yu1, Debin Huang1,2, Wolf Singer1,3 and Danko Nikolic1,3

1 Department of Neurophysiology, Max-Planck Institute for Brain Research, D-60528 Frankfurt am Main, Germany, 2 Department of Mathematics, Shanghai University, Shanghai 200444, People's Republic of China, 3 Frankfurt Institute for Advanced Studies, D-60438 Frankfurt am Main, Germany

Address correspondence to Danko Nikolic, PhD, Max-Planck Institute for Brain Research, Deutschordenstrasse 46, D-60528 Frankfurt am Main, Germany. Email: danko{at}mpih-frankfurt.mpg.de.

A small-world network has been suggested to be an efficient solution for achieving both modular and global processing—a property highly desirable for brain computations. Here, we investigated functional networks of cortical neurons using correlation analysis to identify functional connectivity. To reconstruct the interaction network, we applied the Ising model based on the principle of maximum entropy. This allowed us to assess the interactions by measuring pairwise correlations and to assess the strength of coupling from the degree of synchrony. Visual responses were recorded in visual cortex of anesthetized cats, simultaneously from up to 24 neurons. First, pairwise correlations captured most of the patterns in the population's activity and, therefore, provided a reliable basis for the reconstruction of the interaction networks. Second, and most importantly, the resulting networks had small-world properties; the average path lengths were as short as in simulated random networks, but the clustering coefficients were larger. Neurons differed considerably with respect to the number and strength of interactions, suggesting the existence of "hubs" in the network. Notably, there was no evidence for scale-free properties. These results suggest that cortical networks are optimized for the coexistence of local and global computations: feature detection and feature integration or binding.

Key Words: Ising model • maximum entropy • orientation selectivity • parallel recording • scale free • visual cortex


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