Understanding Neural Burst Patterns Through Graph Neural Network Explainability in Simulated Neuronal Networks

Abstract

Spontaneous bursting activity in neural networks represents a fundamental mode of informationprocessing in the brain, yet the mechanisms triggering these synchronized events remain poorly understood. While graph-based representations of neural networks are established, discovering the specific connectivity and activity patterns that predict burst initiation remains a significant challenge. This work uses GNNs to classify and explain burst initiation in Graphitti-simulated cortical networks by representing neurons as nodes with temporal firing statistics and synapses as edges, thereby integrating activity patterns with network architecture. To move beyond black-box classification, we applied GNNExplainer to identify the minimal neural connectivity patterns driving model predictions. This explainability analysis revealed which specific neurons and synaptic connections the model deemed most critical for each prediction. This work demonstrates how explainable AI can transform our understanding of complex neural dynamics, providing insights that pure predictive modeling cannot offer. By combining the representation power of graph neural networks with explainability techniques, we bridge the gap between prediction and understanding. Our findings challenge prevailing views of burst initiation as a localized phenomenon, instead revealing the critical role of distributed precursor patterns in driving network-wide synchronization. This methodology opens new avenues for investigating emergent behaviors in complex networks.