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dc.contributor.advisorNair, Satish S., 1960-eng
dc.contributor.advisorQuirk, Gregory J.eng
dc.contributor.authorLi, Guoshieng
dc.date.issued2009eng
dc.date.submitted2009 Falleng
dc.descriptionThe entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technica public abstract appears in the public.pdf file.eng
dc.descriptionTitle from PDF of title page (University f Missouri--Columbia, viewed on January 28, 2011)eng
dc.descriptionThesis advisors: Dr. Satish S Nair and Dr Gregory J. Quirk.eng
dc.descriptionVita.eng
dc.descriptionPh. D. University of Missouri--Columbia 2009.eng
dc.description.abstractComputational models are becoming increasingly important to systems neuroscience. In fear learning, although there have been a few attempts at modeling emotional learning and memory in the past, most were limited to simplified connectionist or artificial neural network models which did not incorporate current knowledge about the biophysical properties of accurate neurons. This research focused on extending our understanding of the neural mechanisms underlying fear learning and extinction using biophysically realistic network models. Since disruptionof the fear circuit is thought to underlie the pathology of post traumatic stress (PTSD)and other anxiety disorders, such models could potentially provide ideas and approaches for the development of new medications.We initiated modeling of the overall fear circuit starting with the most critical component, the lateral amygdala (LA), and attempted todescribe how a single structure (i.e., LA) can encode both acquisition and extinctionmemories learned during auditory fear ditioning. Next, we developed a biophysical model of another critical element of the fear circuit, the ITC (intercalated cells) to understand the role of ITC neurons in suppressing fear. After successful development ofcomponent model for the LA and ITC network, an overall amygdala network model was developed to investigate how conditioning-induced potentiation of LA response leadst o activation of the central amgyala (CE) output, by inclusion of another important unit ofthe circuit - basal amydala (BA).eng
dc.description.bibrefIncludes bibliographical referenceseng
dc.format.extentxiii, 224 pageseng
dc.identifier.oclc698746686eng
dc.identifier.urihttps://hdl.handle.net/10355/9875
dc.identifier.urihttps://doi.org/10.32469/10355/9875eng
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartofcommunityUniversity of Missouri--Columbia. Graduate School. Theses and Dissertationseng
dc.rightsOpenAccess.eng
dc.rights.licenseThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.
dc.subject.lcshNeural networks (Neurobiology)eng
dc.subject.lcshFear -- Computer simulationeng
dc.subject.lcshPost-traumatic stress disorder -- Computer simulationeng
dc.subject.lcshAnxiety disorders -- Computer simulationeng
dc.subject.lcshAmygdaloid body -- Computer simulationeng
dc.titleComputational modeling of the fear circuit : a system approach to understand anxiety and stress disordereng
dc.typeThesiseng
thesis.degree.disciplineElectrical and computer engineering (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelDoctoraleng
thesis.degree.namePh. D.eng


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