Efficient coding of natural images in the mouse visual cortex
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DOI: 10.1038/s41467-024-45919-3
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- Javier G. Orlandi & Mohammad Abdolrahmani & Ryo Aoki & Dmitry R. Lyamzin & Andrea Benucci, 2023. "Distributed context-dependent choice information in mouse posterior cortex," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
- Kevin K. Sit & Michael J. Goard, 2020. "Distributed and retinotopically asymmetric processing of coherent motion in mouse visual cortex," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
- Gidon Felsen & Jon Touryan & Feng Han & Yang Dan, 2005. "Cortical Sensitivity to Visual Features in Natural Scenes," PLOS Biology, Public Library of Science, vol. 3(10), pages 1-1, September.
- Mari Ganesh Kumar & Ming Hu & Aadhirai Ramanujan & Mriganka Sur & Hema A Murthy, 2021. "Functional parcellation of mouse visual cortex using statistical techniques reveals response-dependent clustering of cortical processing areas," PLOS Computational Biology, Public Library of Science, vol. 17(2), pages 1-25, February.
- Anthony M. Zador, 2019. "A critique of pure learning and what artificial neural networks can learn from animal brains," Nature Communications, Nature, vol. 10(1), pages 1-7, December.
- Ryo Aoki & Tadashi Tsubota & Yuki Goya & Andrea Benucci, 2017. "An automated platform for high-throughput mouse behavior and physiology with voluntary head-fixation," Nature Communications, Nature, vol. 8(1), pages 1-9, December.
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