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.