ReviewJournal of computational neuroscience2016
Two's company, three (or more) is a simplex : Algebraic-topological tools for understanding higher-order structure in neural data.
Review in Journal of computational neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 114 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
114 citing papers in PubMed, 36 citations in OpenAlex.
- Disrupted metabolic connectivity in dopaminergic and cholinergic networks at different stages of dementia fromScientific reports · 2021Trial
- Structure Guides Function: Beyond Pairwise Neural Interactions.The Journal of comparative neurology · 2026Review
- Charting higher-order models of brain function beyond pairwise interactions.Nature communications · 2026Article
- Phase locking and multistability in the topological Kuramoto model on cell complexes.Nature communications · 2026Article
- Structural signatures of synergy and redundancy in human brain function.bioRxiv : the preprint server for biology · 2026Article
- Node persistence from topological data analysis reveals changes in brain functional connectivity.Patterns (New York, N.Y.) · 2026Article
- Disrupted Higher-Order Topology in OCD Brain Networks Revealed by Hodge Laplacian - an ENIGMA Study.bioRxiv : the preprint server for biology · 2026Article
- Enhancing structural to functional brain network prediction using topological graph learning.Frontiers in neuroscience · 2026Article
- Topological analysis of neuronal assemblies reveals low-rank structure modulated by cholinergic activity.bioRxiv : the preprint server for biology · 2025Article
- Synthesized Kuramoto potential via optomechanical Floquet engineering.Science advances · 2025Article
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- Topological Neuroscience: Linking Circuits to Function.Annual review of neuroscience · 2025Review
- Hypergraph reconstruction from dynamics.Nature communications · 2025Article
- Multilevel irreversibility reveals higher-order organization of nonequilibrium interactions in human brain dynamics.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Study on the robust control of higher-order networks.Scientific reports · 2025Article
- Synchronization of Kuromoto Oscillators on Simplicial Complexes: Hysteresis, Cluster Formation and Partial Synchronization.Entropy (Basel, Switzerland) · 2025Review
- Biological detail and graph structure in network neuroscience.Frontiers in network physiology · 2025Review
- A Q-analysis package for higher-order interactions analysis in Python and its application in network physiology.Frontiers in network physiology · 2025Article
- Functional Hypergraphs of Stock Markets.Entropy (Basel, Switzerland) · 2024Article
54 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The language of graph theory, or network science, has proven to be an exceptional tool for addressing myriad problems in neuroscience. Yet, the use of networks is predicated on a critical simplifying assumption: that the quintessential unit of interest in a brain is a dyad - two nodes (neurons or brain regions) connected by an edge. While rarely mentioned, this fundamental assumption inherently limits the types of neural structure and function that graphs can be used to model. Here, we describe a generalization of graphs that overcomes these limitations, thereby offering a broad range of new possibilities in terms of modeling and measuring neural phenomena. Specifically, we explore the use of simplicial complexes: a structure developed in the field of mathematics known as algebraic topology, of increasing applicability to real data due to a rapidly growing computational toolset. We review the underlying mathematical formalism as well as the budding literature applying simplicial complexes to neural data, from electrophysiological recordings in animal models to hemodynamic fluctuations in humans. Based on the exceptional flexibility of the tools and recent ground-breaking insights into neural function, we posit that this framework has the potential to eclipse graph theory in unraveling the fundamental mysteries of cognition.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.