Neural Regression, Representational Similarity, Model Zoology Neural Taskonomy at Scale in Rodent Visual Cortex
How 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 c...
| Main Authors: | , , , , , |
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| Format: | Article |
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Center for Brains, Minds and Machines (CBMM), The Thirty-fifth Annual Conference on Neural Information Processing Systems (NeurIPS)
2022
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| Online Access: | https://hdl.handle.net/1721.1/141361 |
| _version_ | 1811083144958312448 |
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| author | Conwell, Colin Mayo, David Buice, Michael A. Katz, Boris Alvarez, George A. Barbu, Andrei |
| author_facet | Conwell, Colin Mayo, David Buice, Michael A. Katz, Boris Alvarez, George A. Barbu, Andrei |
| author_sort | Conwell, Colin |
| collection | MIT |
| description | How 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. |
| first_indexed | 2024-09-23T12:24:44Z |
| format | Article |
| id | mit-1721.1/141361 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T12:24:44Z |
| publishDate | 2022 |
| publisher | Center for Brains, Minds and Machines (CBMM), The Thirty-fifth Annual Conference on Neural Information Processing Systems (NeurIPS) |
| record_format | dspace |
| spelling | mit-1721.1/1413612022-03-25T03:21:21Z Neural Regression, Representational Similarity, Model Zoology Neural Taskonomy at Scale in Rodent Visual Cortex Conwell, Colin Mayo, David Buice, Michael A. Katz, Boris Alvarez, George A. Barbu, Andrei How 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. This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF – 1231216. 2022-03-24T17:21:18Z 2022-03-24T17:21:18Z 2021-12-06 Article Technical Report Working Paper https://hdl.handle.net/1721.1/141361 CBMM Memo;131 application/pdf Center for Brains, Minds and Machines (CBMM), The Thirty-fifth Annual Conference on Neural Information Processing Systems (NeurIPS) |
| spellingShingle | Conwell, Colin Mayo, David Buice, Michael A. Katz, Boris Alvarez, George A. Barbu, Andrei Neural Regression, Representational Similarity, Model Zoology Neural Taskonomy at Scale in Rodent Visual Cortex |
| title | Neural Regression, Representational Similarity, Model Zoology Neural Taskonomy at Scale in Rodent Visual Cortex |
| title_full | Neural Regression, Representational Similarity, Model Zoology Neural Taskonomy at Scale in Rodent Visual Cortex |
| title_fullStr | Neural Regression, Representational Similarity, Model Zoology Neural Taskonomy at Scale in Rodent Visual Cortex |
| title_full_unstemmed | Neural Regression, Representational Similarity, Model Zoology Neural Taskonomy at Scale in Rodent Visual Cortex |
| title_short | Neural Regression, Representational Similarity, Model Zoology Neural Taskonomy at Scale in Rodent Visual Cortex |
| title_sort | neural regression representational similarity model zoology neural taskonomy at scale in rodent visual cortex |
| url | https://hdl.handle.net/1721.1/141361 |
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