Events

Date: 10 October 2025   Time: 14:00 - 15:00

Location: Hybrid: Grad Centre GC-204 or via the Teams link

Abstract: Topological data analysis (TDA), and in particular persistent homology, has provided robust results for numerous applications, such as protein structure, cancer detection, and material science. In neuroscience, TDA methods have proven especially valuable, enabling analyses of individual cells to large-scale neuronal networks. A key example is the Topological Morphology Descriptor (TMD), which encodes the spatial structure of branching trees as persistence barcodes through a radial filtration. The TMD has been successfully applied to classify and cluster neurons and microglia, bridging the space of trees with the space of barcodes.
In this talk, I will present recent results in the topological representation of brain cells, focusing on neurons. I will introduce our solution to the inverse TDA problem for neurons: how to reconstruct neuronal trees from persistence barcodes and what this process reveals about the geometry of the space of neuronal trees. I will then demonstrate how algebraic topology provides insights into the relationships between single neurons and networks, as well as differences across species. A central question in neuroscience concerns the organizational principles that distinguish the human brain from other species.
Our findings show that human pyramidal cells form exceptionally complex networks, with greater numbers and higher simplex dimensions than those observed in mice. By comparing dendritic topology, we reveal that human pyramidal cells exhibit much denser perisomatic (basal and oblique) branching, leading to the emergence of highly complex network structures. This greater dendritic complexity, a unique characteristic of human neurons, may underlie the enhanced computational power and cognitive flexibility of the human cortex.