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dc.contributor.advisorZeigler, Bernard P.en_US
dc.contributor.authorVahie, Sankait, 1968-
dc.creatorVahie, Sankait, 1968-en_US
dc.date.accessioned2013-05-09T11:37:02Z
dc.date.available2013-05-09T11:37:02Z
dc.date.issued1996en_US
dc.identifier.urihttp://hdl.handle.net/10150/290699
dc.description.abstractThis document presents a new paradigm for learning, based on an abstraction of the mechanisms found in biological neural networks. Biologically motivated neurons, referred to as Dynamic Neurons are connected together in a knowledge-bearing topology to create Dynamic Neuronal Ensembles. The neurons are developed by first identifying key mechanisms and analyzing their computational significance. These mechanisms are then incorporated into the implementation of the dynamic neurons that make up the dynamic neuronal ensemble. While almost all these mechanisms have been studied and incorporated into the development of models of biological neurons in isolation or as subgroups, a single model incorporating these mechanisms in their computationally abstract form has not been implemented and analyzed. The motivation for this research is two-fold. Firstly, to provide biologists with a modular, flexible tool, incorporating current state-of-the-art modeling and simulation capabilities for use in hypothesis testing, development and analysis. Conversely, to provide engineers with a new paradigm for the development of adaptable, evolutionary systems capable of learning in a dynamic environment. Preliminary results of an implementation of the DNE models in DEVS are presented. A biological model of the Snail Aplysia and an application of its behavioral functionality for engineering are also demonstrated.
dc.language.isoen_USen_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.subjectBiology, Neuroscience.en_US
dc.subjectPsychology, Cognitive.en_US
dc.subjectComputer Science.en_US
dc.titleDynamic neuronal ensembles: A new paradigm for learningen_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.leveldoctoralen_US
dc.identifier.proquest9720678en_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineElectrical and Computer Engineeringen_US
thesis.degree.namePh.D.en_US
dc.identifier.bibrecord.b34582629en_US
refterms.dateFOA2018-07-03T15:45:46Z
html.description.abstractThis document presents a new paradigm for learning, based on an abstraction of the mechanisms found in biological neural networks. Biologically motivated neurons, referred to as Dynamic Neurons are connected together in a knowledge-bearing topology to create Dynamic Neuronal Ensembles. The neurons are developed by first identifying key mechanisms and analyzing their computational significance. These mechanisms are then incorporated into the implementation of the dynamic neurons that make up the dynamic neuronal ensemble. While almost all these mechanisms have been studied and incorporated into the development of models of biological neurons in isolation or as subgroups, a single model incorporating these mechanisms in their computationally abstract form has not been implemented and analyzed. The motivation for this research is two-fold. Firstly, to provide biologists with a modular, flexible tool, incorporating current state-of-the-art modeling and simulation capabilities for use in hypothesis testing, development and analysis. Conversely, to provide engineers with a new paradigm for the development of adaptable, evolutionary systems capable of learning in a dynamic environment. Preliminary results of an implementation of the DNE models in DEVS are presented. A biological model of the Snail Aplysia and an application of its behavioral functionality for engineering are also demonstrated.


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