[37] Hyodong Lee, Eshed Margalit, Kamila M. Jozwik, Michael A. Cohen, Nancy Kanwisher, Daniel L. K. Yamins, and James J. DiCarlo. Topographic deep artificial neural networks reproduce the hallmarks of the primate inferior temporal cortex face processing network. preprint, Neuroscience, July 2020.
[38] Roman Pogodin, Yash Mehta, Timothy Lillicrap, and Peter E Latham. Towards Biologically Plausible Convolutional Networks. In Advances in Neural Information Processing Systems, volume 34, pages 13924-13936. Curran Associates, Inc., 2021.
[39] Matteo Carandini and David J. Heeger. Normalization as a canonical neural computation. Nature Reviews Neuroscience, 13(1):51-62, January 2012. Number: 1 Publisher: Nature Publishing Group.
[40] David J. Heeger and Klavdia O. Zemlianova. A recurrent circuit implements normalization, simulating the dynamics of V1 activity. Proceedings of the National Academy of Sciences, 117(36):22494-22505, September 2020. Publisher: Proceedings of the National Academy of Sciences.
[41] Max F Burg, Santiago A Cadena, George H Denfield, Edgar Y Walker, Andreas S Tolias, Matthias Bethge, and Alexander S Ecker. Learning divisive normalization in primary visual cortex. PLOS Computational Biology, 17(6):e1009028, 2021. Publisher: Public Library of Science San Francisco, CA USA.
[42] Andrew Cirincione, Reginald Verrier, Artiom Bic, Stephanie Olaiya, James J DiCarlo, Lawrence Udeigwe, and Tiago Marques. Implementing Divisive Normalization in CNNs Improves Robustness to Common Image Corruptions.
[43] Nicole C. Rust, Valerio Mante, Eero P. Simoncelli, and J. Anthony Movshon. How MT cells analyze the motion of visual patterns. Nature Neuroscience, 9(11):1421-1431, November 2006. Number: 11 Publisher: Nature Publishing Group.
[44] M Concetta Morrone, DC Burr, and Lamberto Maffei. Functional implications of cross-orientation inhibition of cortical visual cells. I. Neurophysiological evidence. Proceedings of the Royal Society of London. Series B. Biological Sciences, 216(1204):335–354, 1982. Publisher: The Royal Society London.
[45] GC DeAngelis, JG Robson, I Ohzawa, and RD Freeman. Organization of suppression in receptive fields of neurons in cat visual cortex. Journal of Neurophysiology, 68(1):144-163, 1992.
[46] PM Daniel and D Whitteridge. The representation of the visual field on the cerebral cortex in monkeys. The Journal of physiology, 159(2):203, 1961. Publisher: Wiley-Blackwell.
[47] Eric L Schwartz. Spatial mapping in the primate sensory projection: analytic structure and relevance to perception. Biological cybernetics, 25(4):181-194, 1977. Publisher: Springer.
[48] Eric L Schwartz. Computational anatomy and functional architecture of striate cortex: a spatial mapping approach to perceptual coding. Vision research, 20(8):645-669, 1980. Publisher: Elsevier.
[49] Eric L Schwartz. Computational studies of the spatial architecture of primate visual cortex: columns, maps, and protomaps. Primary visual cortex in primates, pages 359-411, 1994. Publisher: Springer.
[50] Jonathan R Polimeni, Mukund Balasubramanian, and Eric L Schwartz. Multi-area visuotopic map complexes in macaque striate and extra-striate cortex. Vision research, 46(20):3336-3359, 2006. Publisher: Elsevier.
[51] Carlos Esteves, Christine Allen-Blanchette, Xiaowei Zhou, and Kostas Daniilidis. Polar Transformer Networks. International Conference on Learning Representations, 2018.
[52] João F. Henriques and Andrea Vedaldi. Warped Convolutions: Efficient Invariance to Spatial Transformations, 2021. _eprint: 1609.04382.
[53] Taro Kiritani and Koji Ono. Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks, 2020. _eprint: 2002.05388.
[54] M.R. Blackburn. A Simple Computational Model of Center-Surround Receptive Fields in the Retina. Technical report. Section: Technical Reports.
[55] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pages 248–255, June 2009. ISSN: 1063-6919.
[56] Dan Hendrycks and Thomas Dietterich. Benchmarking Neural Network Robustness to Common Corruptions and Perturbations, March 2019. arXiv:1903.12261 [cs, stat].