\"Artificial neural networks (ANNs) have been developed as adaptable, robust function approximators for at least the last quarter-century. They have progressed through two generations, and the third is now under development. Spiking neural networks (SNNs) seek to improve on previous generations in two ways: by using a more biologically-inspired neuron, they are shown to be capable of more complex calculations; incorporating polychronous properties of highly-recurrent networks with delays of different lengths on each synapse to achieve large numbers of possible patterns with relatively few neurons and synapses.
Abstracted spiking neurons have been used as a third-generation activation function in a traditional feedforward network architecture, and their potency in application to a real-world problem -- identification of power system generator dynamics -- is demonstrated in this dissertation in comparison to a standard sigmoidal multi-layer perceptron network. However, the goal of SNNs is to be able to utilize biological-like neural network modeling to capture the computational prowess of living brains. In order to achieve such a feat, first a bio-inspired SNN must be able to handle continuous-valued function approximation; until this is done, such networks cannot even be compared to their second-generation predecessors.
This dissertation demonstrates a technique for using a faithfully modeled SNN on continuous-valued problems. The encoding and decoding frameworks developed in this dissertation for the biologically-inspired SNN enables it, like any other ANN, to be applied to any time-dependent problem, including neuroidentification of power system generator dynamics\"--Abstract, page iii.
","abstract_html":"<p>"Artificial neural networks (ANNs) have been developed as adaptable, robust function approximators for at least the last quarter-century. They have progressed through two generations, and the third is now under development. Spiking neural networks (SNNs) seek to improve on previous generations in two ways: by using a more biologically-inspired neuron, they are shown to be capable of more complex calculations; incorporating polychronous properties of highly-recurrent networks with delays of different lengths on each synapse to achieve large numbers of possible patterns with relatively few neurons and synapses.</p> <p>Abstracted spiking neurons have been used as a third-generation activation function in a traditional feedforward network architecture, and their potency in application to a real-world problem -- identification of power system generator dynamics -- is demonstrated in this dissertation in comparison to a standard sigmoidal multi-layer perceptron network. However, the goal of SNNs is to be able to utilize biological-like neural network modeling to capture the computational prowess of living brains. In order to achieve such a feat, first a bio-inspired SNN must be able to handle continuous-valued function approximation; until this is done, such networks cannot even be compared to their second-generation predecessors.</p> <p>This dissertation demonstrates a technique for using a faithfully modeled SNN on continuous-valued problems. The encoding and decoding frameworks developed in this dissertation for the biologically-inspired SNN enables it, like any other ANN, to be applied to any time-dependent problem, including neuroidentification of power system generator dynamics"--Abstract, page iii.</p>","abstract_has_math":false,"creators":["Johnson, Cameron Eric"],"institution":"Missouri University of Science and Technology","degree_name":"Ph. D. in Computer Engineering","degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-02-10T08:00:00Z","date_published":"2016-02-10T08:00:00Z","updated_at":"2026-07-24T03:18:50Z","subjects":["Computer Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarsmine.mst.edu/doctoral_dissertations/78","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Johnson, Cameron Eric"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2016-02-10T08:00:00Z"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph. D. in Computer Engineering"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Missouri University of Science and Technology"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Computer Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarsmine.mst.edu/doctoral_dissertations/78"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["\"Artificial neural networks (ANNs) have been developed as adaptable, robust function approximators for at least the last quarter-century. They have progressed through two generations, and the third is now under development. Spiking neural networks (SNNs) seek to improve on previous generations in two ways: by using a more biologically-inspired neuron, they are shown to be capable of more complex calculations; incorporating polychronous properties of highly-recurrent networks with delays of different lengths on each synapse to achieve large numbers of possible patterns with relatively few neurons and synapses.
Abstracted spiking neurons have been used as a third-generation activation function in a traditional feedforward network architecture, and their potency in application to a real-world problem -- identification of power system generator dynamics -- is demonstrated in this dissertation in comparison to a standard sigmoidal multi-layer perceptron network. However, the goal of SNNs is to be able to utilize biological-like neural network modeling to capture the computational prowess of living brains. In order to achieve such a feat, first a bio-inspired SNN must be able to handle continuous-valued function approximation; until this is done, such networks cannot even be compared to their second-generation predecessors.
This dissertation demonstrates a technique for using a faithfully modeled SNN on continuous-valued problems. The encoding and decoding frameworks developed in this dissertation for the biologically-inspired SNN enables it, like any other ANN, to be applied to any time-dependent problem, including neuroidentification of power system generator dynamics\"--Abstract, page iii.
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Abstracted spiking neurons have been used as a third-generation activation function in a traditional feedforward network architecture, and their potency in application to a real-world problem -- identification of power system generator dynamics -- is demonstrated in this dissertation in comparison to a standard sigmoidal multi-layer perceptron network. However, the goal of SNNs is to be able to utilize biological-like neural network modeling to capture the computational prowess of living brains. In order to achieve such a feat, first a bio-inspired SNN must be able to handle continuous-valued function approximation; until this is done, such networks cannot even be compared to their second-generation predecessors.
This dissertation demonstrates a technique for using a faithfully modeled SNN on continuous-valued problems. The encoding and decoding frameworks developed in this dissertation for the biologically-inspired SNN enables it, like any other ANN, to be applied to any time-dependent problem, including neuroidentification of power system generator dynamics\"--Abstract, page iii.
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