2026-09-19T09:10:08Zhttps://digital.lib.washington.edu/server/oai/request

oai:digital.lib.washington.edu:1773/532592026-03-03T03:10:19Zcom_1773_4888col_1773_19652

Stiber, Michael Arndorfer, Vanessa 2025-08-01T22:11:59Z 2025-08-01T22:11:59Z 2025-08-01 2025 Arndorfer_washington_0250O_28513.pdf https://hdl.handle.net/1773/53259 Thesis (Master's)--University of Washington, 2025 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. application/pdf en_US CC BY computational neuroscience neural networks spike timing dependent plasticity Computer science Computing and software systems Network Behavior Analysis of Spike Timing Dependent Plasticity (STDP) in Simulated Neural Networks Thesis Open Access open.access