A Network Reconstruction Method Based on a Local-Deviation-based Iterative Sparse Regression

A Network Reconstruction Method Based on a Local-Deviation-based Iterative Sparse Regression | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Network Reconstruction Method Based on a Local-Deviation-based Iterative Sparse Regression Zherui Liu, Kun Zhu, Jian Gao, Xiaojuan Sun, Lili Gui, Kun Xu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7716861/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Mar, 2026 Read the published version in Nonlinear Dynamics → Version 1 posted 9 You are reading this latest preprint version Abstract Reconstructing the connectivity of neuronal networks remains a fundamental challenge in neuroscience. However, directly measuring the structure of large-scale neural networks remains technically challenging with current methodologies, underscoring the importance of inference methods based on time series data. Accurately recovering connectivity is particularly difficult in the presence of strong network synchronization, observational noise, and large-scale systems. To overcome these challenges, we introduce a novel reconstruction framework grounded in iterative local linearization, based on the principle that nonlinear systems can be locally linearized in phase space. We systematically validate our method using both Hodgkin-Huxley and Izhikevich neuron models, demonstrating superior accuracy over existing techniques, especially under conditions of pronounced synchronization or noise. Notably, the approach maintains robust performance as network size increases. Further application to a network of coupled Lorenz oscillators confirms its versatility and precision. Our framework thus provides a practical and effective approach for structural reconstruction of neuronal networks or networks in other fields. Biological neural networks Network reconstruction Complex networks Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Mar, 2026 Read the published version in Nonlinear Dynamics → Version 1 posted Editorial decision: Revision requested 11 Nov, 2025 Reviews received at journal 11 Nov, 2025 Reviews received at journal 08 Oct, 2025 Reviewers agreed at journal 01 Oct, 2025 Reviewers agreed at journal 30 Sep, 2025 Reviewers invited by journal 30 Sep, 2025 Editor assigned by journal 29 Sep, 2025 Submission checks completed at journal 26 Sep, 2025 First submitted to journal 25 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7716861","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":527598893,"identity":"4b742440-78cc-4a88-9e62-d7a20d1c1ae6","order_by":0,"name":"Zherui Liu","email":"","orcid":"","institution":"Beijing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Zherui","middleName":"","lastName":"Liu","suffix":""},{"id":527598894,"identity":"520990c9-c64d-4f38-af95-962b50869f34","order_by":1,"name":"Kun Zhu","email":"","orcid":"","institution":"Beijing University of Posts and 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