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dc.contributor.authorAzhar, Feraz
dc.date.accessioned2014-08-28
dc.date.available2014-08-28
dc.date.issued2014-06-19
dc.identifier.urihttp://hdl.handle.net/2123/11707
dc.description.abstractLocalizing content in neural networks provides a bridge to understanding the way in which the brain stores and processes information. A relatively powerful and yet accessible architectural setting in which to approach the question of whether and how such networks house content is provided by feedforward neural networks. Within this context, we establish the existence of polytopes in the state space of the hidden layer of neural networks as vehicles of content. We analyze these geometrical structures from an information-theoretic point of view, invoking mutual information to help define the content stored within them. Our proposal is tested in a suite of experimental investigations, looking into synthetic data, experiments on sonar signals, as well as at data related to the diagnosis of myocardial perfusion studies. We establish how our proposal addresses the problem of misclassification, and provide a new solution to the disjunction problem, which hinges on the precise nature of the causal-informational framework for content advocated herein.en_AU
dc.publisherUniversity of Sydneyen_AU
dc.publisherFaculty of Scienceen_AU
dc.rightsThe author retains copyright of this thesis. It may only be used for the purposes of research and study. It must not be used for any other purposes and may not be transmitted or shared with others without prior permission.en_AU
dc.subjectInformational contenten_AU
dc.subjectMutual informationen_AU
dc.subjectFeedforward neural networksen_AU
dc.titleVehicles of informational content in neural networksen_AU
dc.typeMasters Thesisen_AU
dc.date.valid2014-01-01en_AU
dc.type.pubtypeMaster of Science M.Sc.en_AU
dc.description.disclaimerAccess is restricted to staff and students of the University of Sydney . UniKey credentials are required. Non university access may be obtained by visiting the University of Sydney Library.en_AU


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