On a Geometry of Interbrain Networks

[2509.10650] On a Geometry of Interbrain Networks Skip to main content Search Submit Donate Log in Quantitative Biology > Neurons and Cognition arXiv:2509.10650 (q-bio) [Submitted on 12 Sep 2025 ( v1 ), last revised 6 Jul 2026 (this version, v4)] Title: On a Geometry of Interbrain Networks Authors: Nicolás Hinrichs , Noah Guzmán , Melanie Weber View PDF HTML (experimental) Abstract: Effective analysis in neuroscience benefits significantly from robust conceptual frameworks. Traditional metrics of interbrain synchrony in social neuroscience typically depend on fixed, correlation-based approaches, restricting their explanatory capacity to descriptive observations. Inspired by the successful integration of geometric insights in network science, we propose leveraging discrete geometry to examine the dynamic reconfigurations in neural interactions during social exchanges. Unlike conventional synchrony approaches, our method interprets inter-brain connectivity changes through the evolving geometric structures of neural networks. This geometric framework is realized through a pipeline that identifies critical transitions in network connectivity using entropy metrics derived from curvature distributions. By doing so, we significantly enhance the capacity of hyperscanning methodologies to uncover underlying neural mechanisms in interactive social behavior. Comments: 4 pages, 1 figure, 2 appendixes, accepted NeurIPS 2025 Workshop on Symmetry and Geometry in Neural Representations (NeurReps) and the Proceedings of the Geometry, Topology, and Machine Learning Workshop, PMLR 325:145-152 Subjects: Neurons and Cognition (q-bio.NC) ; Computational Geometry (cs.CG); Machine Learning (cs.LG) Cite as: arXiv:2509.10650 [q-bio.NC] (or arXiv:2509.10650v4 [q-bio.NC] for this version) https://doi.org/10.48550/arXiv.2509.10650 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Proceedings of the Geometry, Topology, and Machine Learning Workshop, PMLR 325:145-152, 2025 Submission history From: Nicolás Hinrichs [ view email ] [v1] Fri, 12 Sep 2025 19:26:27 UTC (373 KB) [v2] Sun, 12 Oct 2025 18:20:20 UTC (369 KB) [v3] Wed, 26 Nov 2025 22:21:50 UTC (369 KB) [v4] Mon, 6 Jul 2026 15:24:55 UTC (356 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: q-bio.NC < prev | next > new | recent | 2025-09 Change to browse by: cs cs.CG cs.LG q-bio References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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