Transitions in event-timing responses in recurrent neural network models mirror those in the human brain

Transitions in event-timing responses in recurrent neural network models mirror those in the human brain | 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 Transitions in event-timing responses in recurrent neural network models mirror those in the human brain Evi Hendrikx, Daniel Manns, Nathan van der Stoep, Alberto Testolin, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6190315/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 The brain exhibits transitions in responses to visual event duration and frequency through the visual processing hierarchy. Timing-tuned responses gradually emerge from monotonically increasing responses, and the properties of both response types are progressively transformed over their respective hierarchies. Here, we reveal the requirements for these neural response transitions using artificial neural networks. We find that multi-layer recurrent networks exhibit monotonic and tuned responses, even without training. In these networks, progressions of monotonic response properties between network layers resemble those between brain areas. Transitions from monotonic to tuned responses emerge after training to predict upcoming inputs. Furthermore, specifically after training on predictable sequences, progressions of tuned response properties resemble those observed in the brain. These results demonstrate that the recurrent neural computations and predictive processing inherent in sensory systems are sufficient to explain the emergence and subsequent hierarchical transformations of tuned responses to sensory event timing, without needing timing-specific processes. Biological sciences/Neuroscience/Computational neuroscience/Network models Biological sciences/Neuroscience/Cognitive neuroscience/Perception Full Text Additional Declarations There is NO Competing Interest. Supplementary Files nrreportingsummary.pdf Reporting summary nreditorialpolicychecklist.pdf Editorial checklist supplementaryMaterialsHendrikxetal2025.pdf Supplementary materials 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-6190315","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":427486191,"identity":"f09d9227-8cdc-4203-b944-c1b7089f4595","order_by":0,"name":"Evi 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