A Persistent Homology Pipeline for the Analysis of Neural Spike Train Data

TL;DR

This paper presents a topological data analysis pipeline using persistent homology to analyze neural spike train ensembles, revealing stimulus-discriminative structures at the population level that are not detectable through single-neuron analysis.

Contribution

The authors develop a novel TDA framework applying persistent homology to neural spike train data, demonstrating its effectiveness in identifying stimulus-related ensemble structures.

Findings

Population-level topological signatures differentiate thermal stimuli.

Ensemble organization encodes perceptually relevant information.

Method is robust to perturbations in the spike train data.

Abstract

In this article, we introduce a Topological Data Analysis (TDA) pipeline for neural spike train data. Understanding how the brain transforms sensory information into perception and behavior requires analyzing coordinated neural population activity. Modern electrophysiology enables simultaneous recording of spike train ensembles, but extracting meaningful information from these datasets remains a central challenge in neuroscience. A fundamental question is how ensembles of neurons discriminate between different stimuli or behavioral states, particularly when individual neurons exhibit weak or no stimulus selectivity, yet their coordinated activity may still contribute to network-level encoding. We describe a TDA framework that identifies stimulus-discriminative structure in spike train ensembles recorded from the mouse insular cortex during presentation of deionized water stimuli at distinct non-nociceptive temperatures. We show that population-level topological signatures effectively differentiate oral thermal stimuli even when individual neurons provide little or no discrimination. These findings demonstrate that ensemble organization can carry perceptually relevant information that standard single-unit analysis may miss. The framework builds on a mathematical representation of spike train ensembles that enables persistent homology to be applied to collections of point processes. At its core is the widely-used Victor-Purpura (VP) distance. Using this metric, we construct persistence-based descriptors that capture multiscale topological features of ensemble geometry. Two key theoretical results support the method: a stability theorem establishing robustness of persistent homology to perturbations in the VP metric parameter, and a probabilistic stability theorem ensuring robustness of topological signatures.