Doughnut or Mickey Mouse? Detecting Toroidal Structure in Data through Persistent Cup-Length

TL;DR

This paper introduces a practical implementation of persistent cup-length, a topological invariant, to detect complex toroidal structures in high-dimensional data, especially in neural datasets, overcoming limitations of traditional persistent homology.

Contribution

First implementation of persistent cup-length method demonstrating its effectiveness in identifying toroidal structures in high-dimensional data.

Findings

Successfully detects toroidal structures in neural data

Overcomes computational challenges with optimized algorithms

Enhances topological data analysis beyond persistent homology

Abstract

Understanding the structure of high-dimensional data is fundamental to neuroscience and other data-intensive scientific fields. While persistent homology effectively identifies basic topological features such as "holes," it lacks the ability to reliably detect more complex topologies, particularly toroidal structures, despite previous heuristic attempts. To address this limitation, recent work introduced persistent cup-length, a novel topological invariant derived from persistent cohomology. In this paper, we present the first implementation of this method and demonstrate its practical effectiveness in detecting toroidal structures, uncovering topological features beyond the reach of persistent homology alone. Our implementation overcomes computational bottlenecks through strategic optimization, efficient integration with the Ripser library, and the application of landmark subsampling techniques. By applying our method to grid cell population activity data, we demonstrate that persistent cup-length effectively identifies toroidal structures in neural manifolds. Our approach offers a powerful new tool for analyzing high-dimensional data, advancing existing topological methods.