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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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Title: A new class of neural architectures to model episodic memory : computational studies of distal reward learning
Author: Taylor, Shawn
Advisor(s): Caudell, Thomas
Committee Member(s): Heileman, Gregory
Calhoun, Vincent
Hamilton, Derek
Department: University of New Mexico. Dept. of Electrical and Computer Engineering
Subject(s): neural architecture episodic memory distal reward
LC Subject(s): Cognitive neuroscience--Mathematical models.
Degree Level: Doctoral
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.
Graduation Date: July 2012
URI: http://hdl.handle.net/1928/21083

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