ArticleFrontiers in computational neuroscience2021
Evaluating State Space Discovery by Persistent Cohomology in the Spatial Representation System.
Article in Frontiers in computational neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Spaces and sequences in the hippocampus: a homological perspective.Journal of computational neuroscience · 2026Article
- Topological Neuroscience: Linking Circuits to Function.Annual review of neuroscience · 2025Review
- Hexagons all the way down: grid cells as a conformal isometric map of space.PLoS computational biology · 2025Article
- The topology of representational geometry.Frontiers in neuroscience · 2025Article
- The role of oscillations in grid cells' toroidal topology.PLoS computational biology · 2025Article
- Uncovering 2-D toroidal representations in grid cell ensemble activity during 1-D behavior.Nature communications · 2024Article
- Topological data analysis of the firings of a network of stochastic spiking neurons.Frontiers in neural circuits · 2023Article
- Article
- Spatial representability of neuronal activity.Scientific reports · 2021Article
- Dynamical self-organization and efficient representation of space by grid cells.Current opinion in neurobiology · 2021Review
- Evaluating State Space Discovery by Persistent Cohomology in the Spatial Representation System.Frontiers in computational neuroscience · 2021Article
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Authors and funding
3 authors.
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Abstract
Persistent cohomology is a powerful technique for discovering topological structure in data. Strategies for its use in neuroscience are still undergoing development. We comprehensively and rigorously assess its performance in simulated neural recordings of the brain's spatial representation system. Grid, head direction, and conjunctive cell populations each span low-dimensional topological structures embedded in high-dimensional neural activity space. We evaluate the ability for persistent cohomology to discover these structures for different dataset dimensions, variations in spatial tuning, and forms of noise. We quantify its ability to decode simulated animal trajectories contained within these topological structures. We also identify regimes under which mixtures of populations form product topologies that can be detected. Our results reveal how dataset parameters affect the success of topological discovery and suggest principles for applying persistent cohomology, as well as persistent homology, to experimental neural recordings.
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