Diameter of the spike-flow graphs of geometrical neural networks

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dc.contributor.author Piersa, Jarosław
dc.date.accessioned 2014-02-08T17:43:37Z
dc.date.available 2014-02-08T17:43:37Z
dc.date.issued 2012
dc.identifier.citation Lecture Notes in Computer Science, 7203, pp 511-520
dc.identifier.issn 0302-9743
dc.identifier.uri http://repozytorium.umk.pl/handle/item/1673
dc.description Full article is available at Springerlink: http://link.springer.com/chapter/10.1007%2F978-3-642-31464-3_52 DOI: 10.1007/978-3-642-31464-3_52
dc.description.abstract Average path length is recognised as one of the vital characteristics of random graphs and complex networks. Despite a rather sparse structure, some cases were reported to have a relatively short lengths between every pair of nodes, making the whole network available in just several hops. This small-worldliness was reported in metabolic, social or linguistic networks and recently in the Internet. In this paper we present results concerning path length distribution and the diameter of the spike-flow graph obtained from dynamics of geometrically embedded neural networks. Numerical results confirm both short diameter and average path length of resulting activity graph. In addition to numerical results, we also discuss means of running simulations in a concurrent environment.
dc.description.sponsorship The work was supported by Polish Ministry of Science and grant project UMO-2011/01/N/ST6/01931. The author is grateful to PL-Grid Project for providing a computing infrastructure for simulations.
dc.language.iso eng
dc.publisher Springer Berlin Heidelberg
dc.relation.ispartofseries Lecture Notes in Computer Science;
dc.rights info:eu-repo/semantics/openAccess
dc.subject geometrical neural networks
dc.subject path length distribution
dc.subject small world graph
dc.subject graph diameter
dc.title Diameter of the spike-flow graphs of geometrical neural networks
dc.type info:eu-repo/semantics/conferenceObject

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