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

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.

Name thesis:degree_name

Computer Engineering

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Year

2012

Author dc:creator

• Taylor, Shawn

Contributors dc:contributor

• Caudell, Thomas
• Heileman, Gregory
• Calhoun, Vincent
• Hamilton, Derek

Language dc:language

English

Repository record dc:identifier

https://digitalrepository.unm.edu/ece_etds/247

OAI identifier oai:identifier

oai:digitalrepository.unm.edu:ece_etds-1246