Task-structured Modularity Emerges in Artificial Networks and Aligns with Brain Architecture

Task-structured Modularity Emerges in Artificial Networks and Aligns with Brain Architecture | 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 Article Task-structured Modularity Emerges in Artificial Networks and Aligns with Brain Architecture Shi Gu, Yuhang Wu, Shikuang Deng, Kangrui Du, Marcelo Mattar, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6987147/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Understanding how neural systems develop modular organization is fundamental to both neuroscience and artificial intelligence. Although modular architectures can improve adaptability and cognitive performance, the processes leading to their emergence are poorly understood. Here we demonstrate that multitask and incremental learning enhance modularity in recurrent neural networks (RNNs) compared to single-task learning, revealing how functional demands influence the structural organization of neural networks. We trained RNNs on cognitive tasks under three distinct learning paradigms: single-task, simultaneous multitask, and incremental multitask learning. Our results suggest that networks adopting multitask learning show an enhanced degree of modularity compared to single-task training, especially when the task load exceeds the network's representation capacity. Those trained with incremental multitask learning, in particular, develop the highest degree of modularity, maintaining superior performance even when connections are selectively pruned. Furthermore, these task-induced networks exhibit structural properties more closely resembling those of biological brain networks than those based solely on spatial constraints, particularly in clustering coefficients and edge length distributions. Collectively, these findings suggest that modular brain architecture emerges not only from physical constraints but as an adaptive response to the sequential introduction of complex cognitive tasks, providing a causal explanation for how functional demands shape network topology. Our findings also illustrate how the developmental and evolutionary context of the brain, wherein multiple tasks are learned, can inform the design of artificial systems well-suited for similar environments. Biological sciences/Neuroscience/Computational neuroscience/Network models Biological sciences/Neuroscience/Cognitive neuroscience Full Text Additional Declarations There is NO Competing Interest. Supplementary Files supplement.pdf Supplementary Information Cite Share Download PDF Status: Under Review Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6987147","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":493367201,"identity":"b7bd5e04-a495-4a52-831c-8362164a7ef7","order_by":0,"name":"Shi 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