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Neural Computing and Applications

Erschienen in:

01.11.2012 | Original Article

Application of connected iterative scan in biological neural network

verfasst von: Xiao-Dan Yan, Stephen Kelley, Mark Goldberg

Erschienen in: Neural Computing and Applications | Ausgabe 8/2012

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Abstract

The current study applied connected iterative scan (CIS), a graph-theoretical-based approach that was previously developed to detect clusters among social networks, in detecting the functional clusters among the biological neural network of human brain extracted from resting-state functional MRI data. Unlike traditional clustering or modularity methods, CIS allows for overlapping among the detected clusters. CIS was tested on a simulation dataset as well as a biological dataset. CIS was able to detect the overlaps designed in the simulation dataset; CIS was also able to detect the designed overlap in the simulation dataset, and it also detected the overlap between the two networks in the biological dataset, which provided new knowledge about the architecture of the biological neural network. In conclusion, CIS is shown to be useful for revealing the delicate structures among the functional clusters in the biological neural network.

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Titel: Application of connected iterative scan in biological neural network
verfasst von: Xiao-Dan Yan
Stephen Kelley
Mark Goldberg
Publikationsdatum: 01.11.2012
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 8/2012
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-011-0633-3

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