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Lisa Yang Integrative Computational Neuroscience (ICoN) Center at MIT"},{"name":"Stanford University Ric Weiland Graduate Fellowship"},{"name":"Wu Tsai Neurosciences Institute and Institute for Human-Centered AI"},{"name":"Stanford Institute for Human Centered Artificial Intelligence."},{"DOI":"10.13039\/100000913","name":"James S. McDonnell Foundation","doi-asserted-by":"publisher","award":["220020469"],"award-info":[{"award-number":["220020469"]}],"id":[{"id":"10.13039\/100000913","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000893","name":"Simons Foundation","doi-asserted-by":"publisher","award":["543061"],"award-info":[{"award-number":["543061"]}],"id":[{"id":"10.13039\/100000893","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000879","name":"Alfred P. 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However, an overall understanding of the mouse\u2019s visual cortex, and how it supports a range of behaviors, remains unknown. Here, we take a computational approach to help address these questions, providing a high-fidelity quantitative model of mouse visual cortex and identifying key structural and functional principles underlying that model\u2019s success. Structurally, we find that a comparatively shallow network structure with a low-resolution input is optimal for modeling mouse visual cortex. Our main finding is functional\u2014that models trained with task-agnostic, self-supervised objective functions based on the concept of contrastive embeddings are much better matches to mouse cortex, than models trained on supervised objectives or alternative self-supervised methods. This result is very much unlike in primates where prior work showed that the two were roughly equivalent, naturally leading us to ask the question of why these self-supervised objectives are better matches than supervised ones in mouse. To this end, we show that the self-supervised, contrastive objective builds a general-purpose visual representation that enables the system to achieve better transfer on out-of-distribution visual scene understanding and reward-based navigation tasks. Our results suggest that mouse visual cortex is a low-resolution, shallow network that makes best use of the mouse\u2019s limited resources to create a light-weight, general-purpose visual system\u2014in contrast to the deep, high-resolution, and more categorization-dominated visual system of primates.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1011506","type":"journal-article","created":{"date-parts":[[2023,10,2]],"date-time":"2023-10-02T17:26:26Z","timestamp":1696267586000},"page":"e1011506","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":23,"title":["Mouse visual cortex as a limited resource system that self-learns an ecologically-general representation"],"prefix":"10.1371","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7509-9629","authenticated-orcid":true,"given":"Aran","family":"Nayebi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nathan C. 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