Information-geometric method for multiple neuronal spike data analysis

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dc.contributor.authorNie, Yimin
dc.contributor.supervisorTatsuno, Masami
dc.date.accessioned2014-10-10T16:54:24Z
dc.date.available2014-10-10T16:54:24Z
dc.date.issued2014
dc.degree.levelPh.Den_US
dc.degree.levelPhD
dc.description.abstractThis dissertation explores a novel statistical technique—information geometric method for theory and its application in analysis of multiple neuronal spike data. The previous studies have indicated that information-geometric method provides a powerful tool of estimating neuronal interactions from observed spiking data. However, these studies were conducted based on simplified neural network structure, which has limitations in the real brain. We systematically extended the previous studies by using intensive mathematical analysis and numerical simulations of realistic and complex neural network. The studies show that information geometric approach provide robust estimation for the sum of the connection weights between neuronal pairs in a complex recurrent network, providing a way of investigating the underlying network structures from neuronal spike data.en_US
dc.description.sponsorshipAlberta Innovates Technology Futures (SCH001),National Science Foundation(CRCNS-1010172),Alberta Innovates Health Solutionsen_US
dc.identifier.urihttps://hdl.handle.net/10133/3528
dc.language.isoenen_US
dc.proquest.subject0317en_US
dc.proquest.subject0463en_US
dc.proquest.subject0790en_US
dc.proquestyesYesen_US
dc.publisherLethbridge, Alta. : University of Lethbridge, Dept. of Neuroscienceen_US
dc.publisher.departmentDepartment of Neuroscienceen_US
dc.publisher.facultyArts and Scienceen_US
dc.relation.ispartofseriesThesis (University of Lethbridge. Faculty of Arts and Science)en_US
dc.subjectInformation geometryen_US
dc.subjectneural networken_US
dc.subjectneuroscienceen_US
dc.subjectspike dataen_US
dc.titleInformation-geometric method for multiple neuronal spike data analysisen_US
dc.typeThesisen_US
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