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dc.contributor.authorAlva, Parimala
dc.contributor.authorDe Sousa, Giseli
dc.contributor.authorTorben-Nielsen, Ben
dc.contributor.authorMaex, Reinoud
dc.contributor.authorAdams, Roderick
dc.contributor.authorDavey, N.
dc.contributor.authorSteuber, Volker
dc.date.accessioned2013-11-25T12:59:55Z
dc.date.available2013-11-25T12:59:55Z
dc.date.issued2013
dc.identifier.citationAlva , P , De Sousa , G , Torben-Nielsen , B , Maex , R , Adams , R , Davey , N & Steuber , V 2013 , Evolution of Dendritic Morphologies Using Deterministic and Nondeterministic Genotype to Phenotype Mapping . in Artificial Neural Networks and Machine Learning : ICANN 2013 . Lecture Notes in Computer Science , vol. 8131 , Springer Nature , pp. 319-326 , 23rd Int Conf on Artifcial Neural Networks , Sofia , Bulgaria , 10/09/13 . https://doi.org/10.1007/978-3-642-40728-4_40
dc.identifier.citationconference
dc.identifier.isbn978-3-642-40727-7
dc.identifier.isbn978-3-642-40728-4
dc.identifier.urihttp://hdl.handle.net/2299/12178
dc.description.abstractIn this study, two morphological representations in the genotype, a deterministic and a nondeterministic representation, are compared when evolving a neuronal morphology for a pattern recognition task. The deterministic approach represents the dendritic morphology explicitly as a set of partitions in the genotype which can give rise to a single phenotype. The nondeterministic method used in this study encodes only the branching probability in the genotype which can produce multiple phenotypes. The main result is that the nondeterministic method instigates the selection of more symmetric dendritic morphologies which was not observed in the deterministic methoden
dc.language.isoeng
dc.publisherSpringer Nature
dc.relation.ispartofArtificial Neural Networks and Machine Learning
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.titleEvolution of Dendritic Morphologies Using Deterministic and Nondeterministic Genotype to Phenotype Mappingen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionDepartment of Computer Science
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionBiocomputation Research Group
rioxxterms.versionofrecord10.1007/978-3-642-40728-4_40
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


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