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dc.contributor.author Hülse, Martin
dc.date.accessioned 2008-08-01T15:30:52Z
dc.date.available 2008-08-01T15:30:52Z
dc.date.issued 2008-09
dc.identifier.citation Hülse , M 2008 , ' Generating complex connectivity structures for large-scale neural models ' pp. 849 . en
dc.identifier.other PURE: 77315
dc.identifier.other dspace: 2160/613
dc.identifier.uri http://hdl.handle.net/2160/613
dc.description Martin Huelse: Generating complex connectivity structures for large-scale neural models. In: V. Kurkova, R. Neruda, and J. Koutnik (Eds.): ICANN 2008, Part II, LNCS 5164, pp. 849–858, 2008. Sponsorship: EPSRC en
dc.description.abstract Biological neural systems and the majority of other real-world networks have topologies significant different from fully or randomly connected structures, which are frequently applied for the definition of artificial neural networks (ANN). In this work we introduce a deterministic process generating strongly connected directed graphs of fractal dimension having connectivity structures very distinct compared with random or fully connected graphs. A sufficient criterion for the generation of strongly connected directed graphs is given and we indicate how the degree-distribution is determined. This allows a targeted generation of strongly connected directed graphs. Two methods for transforming directed graphs into ANN are introduced. A discussion on the importance of strongly connected digraphs and their fractal dimension in the context of artificial adaptive neural systems concludes this work. en
dc.format.extent 849 en
dc.language.iso eng
dc.relation.ispartof en
dc.title Generating complex connectivity structures for large-scale neural models en
dc.type Text en
dc.type.publicationtype Conference paper en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Intelligent Robotics Group en
dc.description.status Non peer reviewed en


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