Show simple item record

dc.contributor.authorSezener, C. E.
dc.contributor.authorÖztop, Erhan
dc.date.accessioned2018-05-07T12:09:10Z
dc.date.available2018-05-07T12:09:10Z
dc.date.issued2017
dc.identifier.isbn978-3-319-68599-1
dc.identifier.urihttp://hdl.handle.net/10679/5813
dc.identifier.urihttps://link.springer.com/chapter/10.1007/978-3-319-68600-4_18
dc.descriptionDue to copyright restrictions, the access to the full text of this article is only available via subscription.
dc.description.abstractMost neurons in the central nervous system exhibit all-or-none firing behavior. This makes Boolean Functions (BFs) tractable candidates for representing computations performed by neurons, especially at finer time scales, even though BFs may fail to capture some of the richness of neuronal computations such as temporal dynamics. One biologically plausible way to realize BFs is to compute a weighted sum of products of inputs and pass it through a heaviside step function. This representation is called a Higher Order Neuron (HON). A HON can trivially represent any n-variable BF with 2n product terms. There have been several algorithms proposed for obtaining representations with fewer product terms. In this work, we propose improvements over previous algorithms for obtaining parsimonious HON representations and present numerical comparisons. In particular, we improve the algorithm proposed by Sezener and Oztop [1] and cut down its time complexity drastically, and develop a novel hybrid algorithm by combining metaheuristic search and the deterministic algorithm of Oztop.en_US
dc.language.isoengen_US
dc.publisherSpringer International Publishingen_US
dc.relation.ispartofInternational Conference on Artificial Neural Networks ICANN 2017: Artificial Neural Networks and Machine Learning – ICANN 2017
dc.rightsrestrictedAccess
dc.titleAlgorithms for obtaining parsimonious higher order neuronsen_US
dc.typeConference paperen_US
dc.peerreviewedyes
dc.publicationstatusPublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID 0000-0002-3051-6038 & YÖK ID 45227) Öztop, Erhan
dc.contributor.ozuauthorÖztop, Erhan
dc.identifier.volume10613en_US
dc.identifier.startpage146en_US
dc.identifier.endpage154en_US
dc.identifier.doi10.1007/978-3-319-68600-4_18en_US
dc.subject.keywordsLearning systemsen_US
dc.subject.keywordsNeuronsen_US
dc.subject.keywordsCentral nervous systemsen_US
dc.subject.keywordsDeterministic algorithmsen_US
dc.subject.keywordsHeaviside step functionen_US
dc.subject.keywordsHybrid algorithmsen_US
dc.subject.keywordsMeta-heuristic searchen_US
dc.subject.keywordsNumerical comparisonen_US
dc.subject.keywordsTemporal dynamicsen_US
dc.subject.keywordsTime complexityen_US
dc.identifier.scopusSCOPUS:2-s2.0-85034256429
dc.contributor.authorMale1


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record


Share this page