dc.contributor.advisor | Nair, Satish S., 1960- | eng |
dc.contributor.advisor | Quirk, Gregory J. | eng |
dc.contributor.author | Li, Guoshi | eng |
dc.date.issued | 2009 | eng |
dc.date.submitted | 2009 Fall | eng |
dc.description | The 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.description | Title from PDF of title page (University f Missouri--Columbia, viewed on January 28, 2011) | eng |
dc.description | Thesis advisors: Dr. Satish S Nair and Dr Gregory J. Quirk. | eng |
dc.description | Vita. | eng |
dc.description | Ph. D. University of Missouri--Columbia 2009. | eng |
dc.description.abstract | Computational 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.bibref | Includes bibliographical references. | eng |
dc.format.extent | xiii, 224 pages | eng |
dc.identifier.oclc | 698746686 | eng |
dc.identifier.uri | https://hdl.handle.net/10355/9875 | |
dc.identifier.uri | https://doi.org/10.32469/10355/9875 | eng |
dc.language | English | eng |
dc.publisher | University of Missouri--Columbia | eng |
dc.relation.ispartofcommunity | University of Missouri--Columbia. Graduate School. Theses and Dissertations | eng |
dc.rights | OpenAccess. | eng |
dc.rights.license | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License. | |
dc.subject.lcsh | Neural networks (Neurobiology) | eng |
dc.subject.lcsh | Fear -- Computer simulation | eng |
dc.subject.lcsh | Post-traumatic stress disorder -- Computer simulation | eng |
dc.subject.lcsh | Anxiety disorders -- Computer simulation | eng |
dc.subject.lcsh | Amygdaloid body -- Computer simulation | eng |
dc.title | Computational modeling of the fear circuit : a system approach to understand anxiety and stress disorder | eng |
dc.type | Thesis | eng |
thesis.degree.discipline | Electrical and computer engineering (MU) | eng |
thesis.degree.grantor | University of Missouri--Columbia | eng |
thesis.degree.level | Doctoral | eng |
thesis.degree.name | Ph. D. | eng |