Evolution of Dendritic Morphologies Using Deterministic and Nondeterministic Genotype to Phenotype Mapping

Parimala Alva, Giseli De Sousa, Ben Torben-Nielsen, Reinoud Maex, Roderick Adams, N. Davey, Volker Steuber

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In 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 method
Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning
Subtitle of host publicationICANN 2013
PublisherSpringer Nature Link
Pages319-326
ISBN (Electronic)978-3-642-40728-4
ISBN (Print)978-3-642-40727-7
DOIs
Publication statusPublished - 2013
Event23rd Int Conf on Artifcial Neural Networks - Sofia, Bulgaria
Duration: 10 Sept 201313 Sept 2013

Publication series

NameLecture Notes in Computer Science
Volume8131

Conference

Conference23rd Int Conf on Artifcial Neural Networks
Country/TerritoryBulgaria
CitySofia
Period10/09/1313/09/13

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