Time Series Analysis of Spiking Neural Systems via Transfer Entropy and Directed Persistent Homology

• URL: http://arxiv.org/abs/2508.19048v1
• Date: Tue, 26 Aug 2025 14:01:53 GMT
• Title: Time Series Analysis of Spiking Neural Systems via Transfer Entropy and Directed Persistent Homology
• Authors: Dylan Peek, Siddharth Pritam, Matthew P. Skerritt, Stephan Chalup,
• Abstract summary: We present a framework for analysing neural time series that integrates Transfer Entropy (TE) with Persistent Homology (PH)<n>TE quantifies directional influence between neurons, producing weighted, directed graphs that reflect dynamic interactions.<n>We apply this framework to synthetic spiking networks trained on logic gate tasks, image-classification networks exposed to structured and perturbed inputs, and mouse cortical recordings annotated with behavioral events.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present a topological framework for analysing neural time series that integrates Transfer Entropy (TE) with directed Persistent Homology (PH) to characterize information flow in spiking neural systems. TE quantifies directional influence between neurons, producing weighted, directed graphs that reflect dynamic interactions. These graphs are then analyzed using PH, enabling assessment of topological complexity across multiple structural scales and dimensions. We apply this TE+PH pipeline to synthetic spiking networks trained on logic gate tasks, image-classification networks exposed to structured and perturbed inputs, and mouse cortical recordings annotated with behavioral events. Across all settings, the resulting topological signatures reveal distinctions in task complexity, stimulus structure, and behavioral regime. Higher-dimensional features become more prominent in complex or noisy conditions, reflecting interaction patterns that extend beyond pairwise connectivity. Our findings offer a principled approach to mapping directed information flow onto global organizational patterns in both artificial and biological neural systems. The framework is generalizable and interpretable, making it well suited for neural systems with time-resolved and binary spiking data.

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