This title appears in the Scientific Report :
2003
Please use the identifier:
http://dx.doi.org/10.1016/j.neunet.2003.06.002 in citations.
Network participation indices: characterizing component roles for information processing in neural networks
Network participation indices: characterizing component roles for information processing in neural networks
We propose a set of indices that characterize-on the basis of connectivity data-how a network node participates in a larger network and what roles it may take given the specific sub-network of interest. These Network Participation Indices are derived from simple graph theoretic measures and have the...
Saved in:
Personal Name(s): | Kötter, R. |
---|---|
Stephan, K. E. | |
Contributing Institute: |
Institut für Medizin; IME |
Published in: | Neural networks, 16 (2003) S. 1261 - 1275 |
Imprint: |
Amsterdam
Elsevier
2003
|
Physical Description: |
1261 - 1275 |
PubMed ID: |
14622883 |
DOI: |
10.1016/j.neunet.2003.06.002 |
Document Type: |
Journal Article |
Research Program: |
Neurowissenschaften |
Series Title: |
Neural Networks
16 |
Subject (ZB): | |
Publikationsportal JuSER |
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245 | |a Network participation indices: characterizing component roles for information processing in neural networks | ||
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520 | |a We propose a set of indices that characterize-on the basis of connectivity data-how a network node participates in a larger network and what roles it may take given the specific sub-network of interest. These Network Participation Indices are derived from simple graph theoretic measures and have the interesting property of linking local features of individual network components to distributed properties that arise within the network as a whole. We use connectivity data on large-scale cortical networks to demonstrate the virtues of this approach and highlight some interesting features that had not been brought up in previously published material. Some implications of our approach for defining network characteristics relevant to functional segregation and functional integration, for example, from functional imaging studies are discussed. | ||
588 | |a Dataset connected to Web of Science, Pubmed | ||
650 | 2 | |2 MeSH |a Mental Processes: physiology | |
650 | 2 | |2 MeSH |a Motor Cortex: physiology | |
650 | 2 | |2 MeSH |a Neural Networks (Computer) | |
650 | 2 | |2 MeSH |a Prefrontal Cortex: physiology | |
650 | 2 | |2 MeSH |a Visual Cortex: physiology | |
650 | 7 | |a J |2 WoSType | |
653 | 2 | 0 | |2 Author |a anatomical connectivity |
653 | 2 | 0 | |2 Author |a cerebral cortex |
653 | 2 | 0 | |2 Author |a cluster analysis |
653 | 2 | 0 | |2 Author |a connectivity matrix |
653 | 2 | 0 | |2 Author |a functional impact |
653 | 2 | 0 | |2 Author |a graph theory |
653 | 2 | 0 | |2 Author |a large-scale connectivity |
653 | 2 | 0 | |2 Author |a neural network |
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