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| Content Provider | PubMed Central |
|---|---|
| Author | Daniele, Marinazzo Wu, Guorong Mario, Pellicoro Angelini, Leonardo Sebastiano, Stramaglia |
| Editor | Moreno, Yamir |
| Copyright Year | 2012 |
| Abstract | We analyze simple dynamical network models which describe the limited capacity of nodes to process the input information. For a proper range of their parameters, the information flow pattern in these models is characterized by exponential distribution of the incoming information and a fat-tailed distribution of the outgoing information, as a signature of the law of diminishing marginal returns. We apply this analysis to effective connectivity networks from human EEG signals, obtained by Granger Causality, which has recently been given an interpretation in the framework of information theory. From the distributions of the incoming versus the outgoing values of the information flow it is evident that the incoming information is exponentially distributed whilst the outgoing information shows a fat tail. This suggests that overall brain effective connectivity networks may also be considered in the light of the law of diminishing marginal returns. Interestingly, this pattern is reproduced locally but with a clear modulation: a topographic analysis has also been made considering the distribution of incoming and outgoing values at each electrode, suggesting a functional role for this phenomenon. |
| Related Links | http://dx.doi.org/10.1371/journal.pone.0045026 |
| Starting Page | 45026 |
| File Format | |
| ISSN | 19326203 |
| e-ISSN | 19326203 |
| Journal | PLoS ONE |
| Issue Number | 9 |
| Volume Number | 7 |
| Language | English |
| Publisher | Public Library of Science |
| Publisher Date | 2012-09-01 |
| Access Restriction | Open |
| Rights Holder | Public Library of Science |
| Subject Keyword | Biochemistry, Genetics and Molecular Biology(all) Agricultural and Biological Sciences(all) Medicine(all) Research in Higher Education |
| Content Type | Text |
| Resource Type | Article |
| Subject | Multidisciplinary |
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