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A new class of neural architectures to model episodic memory : computational studies of distal reward learning

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1928/21083

A new class of neural architectures to model episodic memory : computational studies of distal reward learning

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dc.contributor.author Taylor, Shawn
dc.date.accessioned 2012-08-28T18:17:00Z
dc.date.available 2012-08-28T18:17:00Z
dc.date.issued 2012-08-28
dc.date.submitted July 2012
dc.identifier.uri http://hdl.handle.net/1928/21083
dc.description.abstract A 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.sponsorship National Science Foundation, Defense Threat Reduction Agency, Sandia National Laboratories en_US
dc.language.iso en_US en_US
dc.subject neural architecture episodic memory distal reward en_US
dc.subject.lcsh Cognitive neuroscience--Mathematical models.
dc.title A new class of neural architectures to model episodic memory : computational studies of distal reward learning en_US
dc.type Dissertation en_US
dc.description.degree Engineering en_US
dc.description.level Doctoral en_US
dc.description.department University of New Mexico. Dept. of Electrical and Computer Engineering en_US
dc.description.advisor Caudell, Thomas
dc.description.committee-member Heileman, Gregory
dc.description.committee-member Calhoun, Vincent
dc.description.committee-member Hamilton, Derek


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