{"id":{"repo_id":"unt","oai_identifier":"info:ark/67531/metadc3702"},"canonical_url":"https://search.dev.ndltd.org/etd/unt/info:ark/67531/metadc3702","repository":{"repo_id":"unt","name":"University of North Texas","base_url":"https://digital.library.unt.edu/oai/"},"display":{"title":"Determining Properties of Synaptic Structure in a Neural Network through Spike Train Analysis","abstract":"A \"complex\" system typically has a relatively large number of dynamically interacting components and tends to exhibit emergent behavior that cannot be explained by analyzing each component separately. A biological neural network is one example of such a system. A multi-agent model of such a network is developed to study the relationships between a network's structure and its spike train output. Using this model, inferences are made about the synaptic structure of networks through cluster analysis of spike train summary statistics A complexity measure for the network structure is also presented which has a one-to-one correspondence with the standard time series complexity measure sample entropy.","abstract_html":"A &quot;complex&quot; system typically has a relatively large number of dynamically interacting components and tends to exhibit emergent behavior that cannot be explained by analyzing each component separately. A biological neural network is one example of such a system. A multi-agent model of such a network is developed to study the relationships between a network&#x27;s structure and its spike train output. Using this model, inferences are made about the synaptic structure of networks through cluster analysis of spike train summary statistics A complexity measure for the network structure is also presented which has a one-to-one correspondence with the standard time series complexity measure sample entropy.","abstract_has_math":false,"creators":["Brooks, Evan"],"institution":"University of North Texas","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Monticino, Michael G.","Quintanilla, John","Brand, Neal"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2007,"date_issued":"2007-05","date_published":"2007-05","updated_at":"2026-07-24T05:34:52Z","subjects":["complexity","neural network","multi-agent model","sample entropy","Neural networks (Neurobiology) -- Mathematical models.","Neural transmission -- Mathematical models."],"languages":["English"],"rights":["Public","Copyright","Brooks, Evan","Copyright is held by the author, unless otherwise noted. All rights reserved."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["oclc: 174144166","https://digital.library.unt.edu/ark:/67531/metadc3702/","ark: ark:/67531/metadc3702"],"render_values":[{"text":"oclc: 174144166","href":null,"code":true},{"text":"https://digital.library.unt.edu/ark:/67531/metadc3702/","href":"https://digital.library.unt.edu/ark:/67531/metadc3702/","code":true},{"text":"ark: ark:/67531/metadc3702","href":null,"code":true}]}]},"links":{"outbound_url":"https://doi.org/10.12794/metadc3702","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Monticino, Michael G.","Quintanilla, John","Brand, Neal"]},{"key":"dc:creator","label":"Author","values":["Brooks, Evan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2007-05"]},{"key":"dc:publisher","label":"Institution","values":["University of North Texas"]},{"key":"dc:type","label":"Dc Type","values":["Thesis or Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["complexity","neural network","multi-agent model","sample entropy","Neural networks (Neurobiology) -- Mathematical models.","Neural transmission -- Mathematical models."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]},{"key":"dc:rights","label":"Dc Rights","values":["Public","Copyright","Brooks, Evan","Copyright is held by the author, unless otherwise noted. All rights reserved."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["oclc: 174144166","doi: 10.12794/metadc3702","https://digital.library.unt.edu/ark:/67531/metadc3702/","ark: ark:/67531/metadc3702"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["A \"complex\" system typically has a relatively large number of dynamically interacting components and tends to exhibit emergent behavior that cannot be explained by analyzing each component separately. A biological neural network is one example of such a system. A multi-agent model of such a network is developed to study the relationships between a network's structure and its spike train output. Using this model, inferences are made about the synaptic structure of networks through cluster analysis of spike train summary statistics A complexity measure for the network structure is also presented which has a one-to-one correspondence with the standard time series complexity measure sample entropy."]},{"key":"dc:format","label":"Dc Format","values":["Text"]},{"key":"dc:title","label":"Title","values":["Determining Properties of Synaptic Structure in a Neural Network through Spike Train Analysis"]}]}],"canonical_facts":{"dc:contributor":["Monticino, Michael G.","Quintanilla, John","Brand, Neal"],"dc:creator":["Brooks, Evan"],"dc:date":["2007-05"],"dc:description":["A \"complex\" system typically has a relatively large number of dynamically interacting components and tends to exhibit emergent behavior that cannot be explained by analyzing each component separately. A biological neural network is one example of such a system. A multi-agent model of such a network is developed to study the relationships between a network's structure and its spike train output. Using this model, inferences are made about the synaptic structure of networks through cluster analysis of spike train summary statistics A complexity measure for the network structure is also presented which has a one-to-one correspondence with the standard time series complexity measure sample entropy."],"dc:format":["Text"],"dc:identifier":["oclc: 174144166","doi: 10.12794/metadc3702","https://digital.library.unt.edu/ark:/67531/metadc3702/","ark: ark:/67531/metadc3702"],"dc:language":["English"],"dc:publisher":["University of North Texas"],"dc:rights":["Public","Copyright","Brooks, Evan","Copyright is held by the author, unless otherwise noted. All rights reserved."],"dc:subject":["complexity","neural network","multi-agent model","sample entropy","Neural networks (Neurobiology) -- Mathematical models.","Neural transmission -- Mathematical models."],"dc:title":["Determining Properties of Synaptic Structure in a Neural Network through Spike Train Analysis"],"dc:type":["Thesis or Dissertation"]},"updated_at":"2026-07-24T05:34:52Z"}