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dc.contributor.authorTaylor, Shawn
dc.date.accessioned2012-08-28T18:17:00Z
dc.date.available2012-08-28T18:17:00Z
dc.date.issued2012-08-28
dc.date.submittedJuly 2012
dc.identifier.urihttp://hdl.handle.net/1928/21083
dc.description.abstractA computational cognitive neuroscience model is proposed, which models episodic memory based on the mammalian brain. A computational neural architecture instantiates the proposed model and is tested on a particular task of distal reward learning. Categorical Neural Semantic Theory informs the architecture design. To experiment upon the computational brain model, embodiment and an environment in which the embodiment exists are simulated. This simulated environment realizes the Morris Water Maze task, a well established biological experimental test of distal reward learning. The embodied neural architecture is treated as a virtual rat and the environment it acts in as a virtual water tank. Performance levels of the neural architectures are evaluated through analysis of embodied behavior in the distal reward learning task. Comparison is made to biological rat experimental data, as well as comparison to other published models. In addition, differences in performance are compared between the normal and categorically informed versions of the architecture.en_US
dc.description.sponsorshipNational Science Foundation, Defense Threat Reduction Agency, Sandia National Laboratoriesen_US
dc.language.isoen_USen_US
dc.subjectneural architecture episodic memory distal rewarden_US
dc.subject.lcshCognitive neuroscience--Mathematical models.
dc.titleA new class of neural architectures to model episodic memory : computational studies of distal reward learningen_US
dc.typeDissertationen_US
dc.description.degreeEngineeringen_US
dc.description.levelDoctoralen_US
dc.description.departmentUniversity of New Mexico. Dept. of Electrical and Computer Engineeringen_US
dc.description.advisorCaudell, Thomas
dc.description.committee-memberHeileman, Gregory
dc.description.committee-memberCalhoun, Vincent
dc.description.committee-memberHamilton, Derek


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