DEVELOPING A COMPUTATIONAL MODEL OF NEURAL NETWORKS INTO A LEARNING MACHINE
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Date
2017-01-01
Type of Work
Department
Mathematics and Statistics
Program
Mathematics, Applied
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This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
Distribution Rights granted to UMBC by the author.
Distribution Rights granted to UMBC by the author.
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Abstract
The purpose of this dissertations work is to contribute to the development of a biologically plausible model of neural networks into a learning machine. Temporal hierarchical probabilistic associative memory (THPAM) is a functional model of biological neural networks which performs a variant of supervised and unsupervised Hebbian learning to store information in the synapses, uses dendritic trees to encode information, and communicates information via spike trains. THPAM can be viewed as a recurrent hierarchical network of processing units, neuronal compartments that serve as pattern recognizers. This work proposes supplemental developments, a parallel programming implementation, and several benchmark results pertaining to the processing unit architecture. Supplemental theories and mechanisms pertaining to the processing unit architecture are contributed in this dissertations. These contributions serve to confirm propositions contained in original publications, enable alternative constructions of the processing unit generalization component, and allow for an alternative generalization mechanism. The new generalization mechanism has a unique application in efficiently learning data clusters centered at a target input vector. Orthogonal expansion of a vector in the processing unit is an exponential function of the dimension of the vector. Although there are ways to avoid vectors with a large dimension, a parallel programming implementation proposed in this work is utilized to somewhat alleviate the severe limitations imposed by this complexity on serial machines. The scalability of the parallel program is examined on the maya cluster of the UMBC High Performance Computing Facility. The parallelized processing unit implementation is beneficial in reducing the run time of sufficiently large fixed problem sizes from several hours to a few seconds. The performance of the processing unit as a pattern recognizer is demonstrated on sample data sets obtained from the UCI Machine Learning Repository. These data sets independently contained categorical data, missing data, and real-valued data. Several data encoding techniques are performed and examined in order to best suit the predictive performance of the processing unit on the data sets considered. Differences in performance between particular encoding methods are thoroughly examined and discussed in relation to the processing unit mechanisms, and the effects hyperparameter adjustments are precisely considered.