Please use this persistent identifier to cite or link to this item: doi:10.24405/386
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dc.contributor.advisorKluwe, Rainer H.de_DE
dc.contributor.authorLuna-Rodriguez, Aquiles-
dc.date.accessioned2017-10-24T14:02:30Z-
dc.date.available2017-10-24T14:02:30Z-
dc.date.issued2009-
dc.identifier.otherhttp://edoc.sub.uni-hamburg.de/hsu/volltexte/2009/2137/-
dc.identifier.urihttps://doi.org/10.24405/386-
dc.descriptionDer Zugriff auf das dazugehörige ZIP-Archiv ist nur auf Anfrage möglich.de_DE
dc.description.abstractIn recent years, some areas of cognitive psychology have proposed formal models in the form of computer simulations, using Back-Propagation Artificial Neural Networks (BP-ANNs). Such models represent an improvement in plausibility, and they allow quantitative results to be compared with empirical data.--- After learning, BP-ANN's cells exhibit a fixed input/output behavior. Using the black box method, this is shown to be a fundamental problem. Cells without an internal state to represent short-time memory cannot account for the sequence of stimuli, nor for the time elapsed between stimuli. As a consequence, BP-ANNs -as well as other neural networks without state-dependant input/output- are inadequate as models of some important cognitive processes. These include classical conditioning, operant conditioning, and sequence effects in cognitive control.--- Another major methodological problem is the use of free parameters. BP-ANNs cognitive models frequently use arbitrary amounts of cells, amounts of layers, connection structure, learning parameter value and other characteristics, without giving a theoretical justification.--- Three methods are proposed here to solve these problems: first, the black box method is used to produce a cell's input/output behavior more similar to that of neurons. Second, the reverse engineering method is used to simulate as many neural features as possible. And, third, a genetic algorithm is used to eliminate arbitrary free parameters.--- The use of these methods is illustrated through a series of spiking neural network models, implementing state-dependant input/output, spikes, refractory period, temporal summation, axon delay and synchronization of neuron groups. A genetic algorithm is used to choose the parameter values in another series of models.--- Finally, the feasibility of following this research strategy using parallel computer hardware is discussed.de_DE
dc.description.sponsorshipAllgemeine und Biologische Psychologiede_DE
dc.formatapplication/pdf-
dc.language.isoengde_DE
dc.publisherUniversitätsbibliothek der HSU / UniBwHde_DE
dc.subjectCellular Automatede_DE
dc.subjectSpiking Neural Networkde_DE
dc.subjectJellyfish Simulationde_DE
dc.subject.ddc150 Psychologiede_DE
dc.titleNeural Network Models of Cognitive Conflict Paradigmsde_DE
dc.typeThesisde_DE
dcterms.dateAccepted2009-02-23-
dc.identifier.urnurn:nbn:de:gbv:705-opus-21379-
dcterms.bibliographicCitation.originalpublisherplaceHamburgde_DE
dc.contributor.grantorHSU Hamburgde_DE
dc.type.thesisPhD Thesisde_DE
local.submission.typefull-textde_DE
hsu.dnb.deeplinkhttps://d-nb.info/997266899/-
hsu.restrictedAccessKein Vertrag für das ZIP-Archiv vorhanden.-
item.grantfulltextopen-
item.languageiso639-1en-
item.fulltext_sWith Fulltext-
item.openairetypeThesis-
item.fulltextWith Fulltext-
crisitem.author.deptAllgemeine und Biologische Psychologie-
crisitem.author.parentorgFakultät für Geistes- und Sozialwissenschaften-
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