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dc.contributor.authorConwell, Colin
dc.contributor.authorMayo, David
dc.contributor.authorBuice, Michael A.
dc.contributor.authorKatz, Boris
dc.contributor.authorAlvarez, George A.
dc.contributor.authorBarbu, Andrei
dc.date.accessioned2022-03-24T17:21:18Z
dc.date.available2022-03-24T17:21:18Z
dc.date.issued2021-12-06
dc.identifier.urihttps://hdl.handle.net/1721.1/141361
dc.description.abstractHow well do deep neural networks fare as models of mouse visual cortex? A majority of research to date suggests results far more mixed than those produced in the modeling of primate visual cortex. Here, we perform a large-scale bench- marking of dozens of deep neural network models in mouse visual cortex with both representational similarity analysis and neural regression. Using the Allen Brain Observatory’s 2-photon calcium-imaging dataset of activity in over 6,000 reliable rodent visual cortical neurons recorded in response to natural scenes, we replicate previous findings and resolve previous discrepancies, ultimately demonstrating that modern neural networks can in fact be used to explain activity in the mouse visual cortex to a more reasonable degree than previously suggested. Using our benchmark as an atlas, we offer preliminary answers to overarching questions about levels of analysis, questions about the properties of models that best predict the visual system overall and questions about the mapping between biological and artificial representations. Our results provide a reference point for future ventures in the deep neural network modeling of mouse visual cortex, hinting at novel combinations of mapping method, architecture, and task to more fully characterize the computational motifs of visual representation in a species so central to neuroscience, but with a perceptual physiology and ecology markedly different from the ones we study in primates.en_US
dc.description.sponsorshipThis work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF – 1231216.en_US
dc.publisherCenter for Brains, Minds and Machines (CBMM), The Thirty-fifth Annual Conference on Neural Information Processing Systems (NeurIPS)en_US
dc.relation.ispartofseriesCBMM Memo;131
dc.titleNeural Regression, Representational Similarity, Model Zoology Neural Taskonomy at Scale in Rodent Visual Cortexen_US
dc.typeArticleen_US
dc.typeTechnical Reporten_US
dc.typeWorking Paperen_US


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