Date of Degree
PhD (Doctor of Philosophy)
Applied Mathematical and Computational Sciences
First Committee Member
Second Committee Member
Third Committee Member
Fourth Committee Member
Fifth Committee Member
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.
hippocampus, neural networks, object-space association, parallel computing, spike time dependent plasticity
xiii, 163 pages
Includes bibliographical references (pages 161-163).
Copyright 2013 Jeannine Therese Abiva
Abiva, Jeannine Therese. "Learning the association of multiple inputs in recurrent networks." PhD (Doctor of Philosophy) thesis, University of Iowa, 2013.