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Eigenvalue Spectra of Functional Networks in fMRI Data and Artificial Models

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dc.contributor.author Piersa, Jarosław
dc.contributor.author Zając, Katarzyna
dc.date.accessioned 2014-02-11T08:35:20Z
dc.date.available 2014-02-11T08:35:20Z
dc.date.issued 2013-06-09
dc.identifier.citation Artificial Intelligence and Soft Computing : 12th International Conference, ICAISC 2013, Zakopane, Poland, June 9-13, 2013, Proceedings, Part I, pp 205-214
dc.identifier.isbn 978-3-642-38657-2
dc.identifier.uri http://repozytorium.umk.pl/handle/item/1682
dc.description Full paper available at Springerlink: http://link.springer.com/chapter/10.1007%2F978-3-642-38658-9_19
dc.description.abstract In this work we provide a spectral comparison of functional networks in fMRI data of brain activity and artificial energy-based neural model. The spectra (set of eigenvalues of the graph adjacency matrix) of both networks turn out to obey similar decay rate and characteristic power-law scaling in their middle parts. This extends the set of statistics, which are already confirmed to be similar for both neural models and medical data, by the graph spectrum.
dc.description.sponsorship The work has been supported by Polish National Research Centre research grant no UMO-2011/01/N/ST6/0193
dc.language.iso eng
dc.publisher Springer Berlin Heidelberg
dc.relation.ispartofseries Lecture Notes in Computer Science;Vol. 7894
dc.rights info:eu-repo/semantics/openAccess
dc.subject fMRI
dc.subject functional networks
dc.subject neural networks
dc.subject graph spectrum
dc.title Eigenvalue Spectra of Functional Networks in fMRI Data and Artificial Models
dc.type info:eu-repo/semantics/conferenceObject


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