{"id":{"repo_id":"washington","oai_identifier":"oai:digital.lib.washington.edu:1773/53259"},"canonical_url":"https://search.dev.ndltd.org/etd/washington/oai:digital.lib.washington.edu:1773/53259","repository":{"repo_id":"washington","name":"University of Washington","base_url":"https://digital.lib.washington.edu/server/oai/request"},"display":{"title":"Network Behavior Analysis of Spike Timing Dependent Plasticity (STDP) in Simulated Neural Networks","abstract":"The machine learning landscape is rapidly evolving with researchers often turning toward nature for inspiration. Understanding the development of neural networks \\textit{in vivo} contributes significant transferable insight for advancing both neuroscience and computational research. This project applies a multiplicative Spike Timing Dependent Plasticity (STDP) model to the weighted graph output from neural growth simulations and analyzes the resulting spike and weight changes over time. This preliminary investigation establishes a baseline process for understanding the effects of STDP on a neural network and provides a framework for defining the resulting network behavior. Through rigorous data analysis, we examine bursting behavior during the refinement phase, analyze the progressive effects of STDP on synapse weights, and compare how the network behavior changes between the growth and refinement phases of neural development.","abstract_html":"The machine learning landscape is rapidly evolving with researchers often turning toward nature for inspiration. Understanding the development of neural networks \\textit{in vivo} contributes significant transferable insight for advancing both neuroscience and computational research. This project applies a multiplicative Spike Timing Dependent Plasticity (STDP) model to the weighted graph output from neural growth simulations and analyzes the resulting spike and weight changes over time. This preliminary investigation establishes a baseline process for understanding the effects of STDP on a neural network and provides a framework for defining the resulting network behavior. Through rigorous data analysis, we examine bursting behavior during the refinement phase, analyze the progressive effects of STDP on synapse weights, and compare how the network behavior changes between the growth and refinement phases of neural development.","abstract_has_math":false,"creators":["Arndorfer, Vanessa"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Stiber, Michael"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-08-01","date_published":"2025-08-01","updated_at":"2026-07-24T05:58:25Z","subjects":["computational neuroscience","neural networks","spike timing dependent plasticity","Computer science"],"languages":["en_US"],"rights":["CC BY"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1773/53259","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Stiber, Michael"]},{"key":"dc:creator","label":"Author","values":["Arndorfer, Vanessa"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-08-01T22:11:59Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-08-01T22:11:59Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-08-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["computational neuroscience","neural networks","spike timing dependent plasticity","Computer science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_US"]},{"key":"dc:rights","label":"Dc Rights","values":["CC BY"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["Arndorfer_washington_0250O_28513.pdf"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1773/53259"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis (Master's)--University of Washington, 2025"]},{"key":"dc:description.abstract","label":"Abstract","values":["The machine learning landscape is rapidly evolving with researchers often turning toward nature for inspiration. Understanding the development of neural networks \\textit{in vivo} contributes significant transferable insight for advancing both neuroscience and computational research. This project applies a multiplicative Spike Timing Dependent Plasticity (STDP) model to the weighted graph output from neural growth simulations and analyzes the resulting spike and weight changes over time. This preliminary investigation establishes a baseline process for understanding the effects of STDP on a neural network and provides a framework for defining the resulting network behavior. Through rigorous data analysis, we examine bursting behavior during the refinement phase, analyze the progressive effects of STDP on synapse weights, and compare how the network behavior changes between the growth and refinement phases of neural development."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Network Behavior Analysis of Spike Timing Dependent Plasticity (STDP) in Simulated Neural Networks"]}]}],"canonical_facts":{"dc:contributor.advisor":["Stiber, Michael"],"dc:creator":["Arndorfer, Vanessa"],"dc:date.accessioned":["2025-08-01T22:11:59Z"],"dc:date.available":["2025-08-01T22:11:59Z"],"dc:date.issued":["2025-08-01"],"dc:description":["Thesis (Master's)--University of Washington, 2025"],"dc:description.abstract":["The machine learning landscape is rapidly evolving with researchers often turning toward nature for inspiration. Understanding the development of neural networks \\textit{in vivo} contributes significant transferable insight for advancing both neuroscience and computational research. This project applies a multiplicative Spike Timing Dependent Plasticity (STDP) model to the weighted graph output from neural growth simulations and analyzes the resulting spike and weight changes over time. This preliminary investigation establishes a baseline process for understanding the effects of STDP on a neural network and provides a framework for defining the resulting network behavior. Through rigorous data analysis, we examine bursting behavior during the refinement phase, analyze the progressive effects of STDP on synapse weights, and compare how the network behavior changes between the growth and refinement phases of neural development."],"dc:format.mimetype":["application/pdf"],"dc:identifier.other":["Arndorfer_washington_0250O_28513.pdf"],"dc:identifier.uri":["https://hdl.handle.net/1773/53259"],"dc:language.iso":["en_US"],"dc:rights":["CC BY"],"dc:subject":["computational neuroscience","neural networks","spike timing dependent plasticity","Computer science"],"dc:title":["Network Behavior Analysis of Spike Timing Dependent Plasticity (STDP) in Simulated Neural Networks"],"dc:type":["Thesis"]},"updated_at":"2026-07-24T05:58:25Z"}