DOI

10.17077/etd.z8053ugw

Document Type

Dissertation

Date of Degree

Fall 2013

Degree Name

PhD (Doctor of Philosophy)

Degree In

Applied Mathematical and Computational Sciences

First Advisor

Curtu, Rodica

Second Advisor

Oliveira, Suely

First Committee Member

Curtu, Rodica

Second Committee Member

Oliveira, Suely

Third Committee Member

Ayati, Bruce

Fourth Committee Member

Mitchell, Colleen

Fifth Committee Member

Stewart, David

Abstract

In spite of the many discoveries made in neuroscience, the mechanism by which memories are formed is still unclear. To better understand how some disorders of the brain arise, it is necessary to improve our knowledge of memory formation in the brain. With the aid of a biological experiment, an artificial neural network is developed to provide insight into how information is stored and recalled. In particular, the bi-conditional association of distinct spatial and non-spatial information is examined using computational techniques. The thesis defines three versions of a computational model based on a combination of feedforward and recurrent neural networks and a biologically-inspired spike time dependent plasticity learning rule. The ability of the computational model to store and recall the bi-conditional object-space association task through reward-modulated plastic synapses is numerically investigated.

Further, the network's response to variation of certain parameter values is numerically addressed. A parallel algorithm is introduced to reduce the running time necessary to test the robustness of this artificial neural network. The numerical results produced with this algorithm are then analyzed by a statistical approach, and the network's ability for learning is assessed.

Keywords

hippocampus, neural networks, object-space association, parallel computing, spike time dependent plasticity

Pages

xiii, 163 pages

Bibliography

Includes bibliographical references (pages 161-163).

Copyright

Copyright 2013 Jeannine Therese Abiva

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