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

Author

Shawn Taylor

Publication Date

8-28-2012

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.

Sponsors

National Science Foundation, Defense Threat Reduction Agency, Sandia National Laboratories

Document Type

Dissertation

Language

English

Degree Name

Computer Engineering

Level of Degree

Doctoral

Department Name

Electrical and Computer Engineering

First Committee Member (Chair)

Heileman, Gregory

Second Committee Member

Calhoun, Vincent

Third Committee Member

Hamilton, Derek

Recommended Citation

Taylor, Shawn. "A new class of neural architectures to model episodic memory : computational studies of distal reward learning." (2012). https://digitalrepository.unm.edu/ece_etds/247