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Spectra of the Spike-Flow Graphs in Geometrically Embedded Neural Networks

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dc.contributor.author Piersa, Jarosław
dc.contributor.author Schreiber, Tomasz
dc.date.accessioned 2014-02-08T17:48:21Z
dc.date.available 2014-02-08T17:48:21Z
dc.date.issued 2012-04-29
dc.identifier.citation Lecture Notes in Computer Science Volume 7267, 2012, pp 143-151
dc.identifier.issn 0302-9743
dc.identifier.uri http://repozytorium.umk.pl/handle/item/1674
dc.description Full article available at Springerlink: http://link.springer.com/chapter/10.1007%2F978-3-642-29347-4_17 DOI: 10.1007/978-3-642-29347-4_17
dc.description.abstract In this work we study a simplified model of a neural activity flow in networks, whose connectivity is based on geometrical embedding, rather than being lattices or fully connected graphs. We present numerical results showing that as the spectrum (set of eigenvalues of adjacency matrix) of the resulting activity-based network develops a scale-free dependency. Moreover it strengthens and becomes valid for a wider segment along with the simulation progress, which implies a highly organised structure of the analysed graph.
dc.description.sponsorship The work has been partially supported by National Research Centre research grant UMO-2011/01/N/ST6/01931. The author is grateful to PL-GridProject staff and help-line for computing resources.
dc.language.iso eng
dc.publisher Springer Berlin Heidelberg
dc.relation.ispartofseries Artificial Intelligence and Soft Computing;
dc.rights info:eu-repo/semantics/openAccess
dc.subject geometric neural networks
dc.subject graph spectrum
dc.subject scale-freeness
dc.title Spectra of the Spike-Flow Graphs in Geometrically Embedded Neural Networks
dc.type info:eu-repo/semantics/conferenceObject


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