[19] Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi. Describing textures in the wild. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3606-3613, 2014.
[20] Uri Cohen, SueYeon Chung, Daniel D Lee, and Haim Sompolinsky. Separability and geometry of object manifolds in deep neural networks. Nature communications, 11(1):1-13, 2020.
[21] Colin Conwell, Jacob S. Prince, Kendrick N. Kay, George A. Alvarez, and Talia Konkle. What can 1.8 billion regressions tell us about the pressures shaping high-level visual representation in brains and machines? bioRxiv, 2023. doi: 10.1101/2022.03.28.485868. URL https://www.biorxiv.org/content/early/2023/07/01/2022.03.28.485868.
[22] Thomas M Cover. Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE transactions on electronic computers, (3):326-334, 1965.
[23] Victor Guilherme Turrisi da Costa, Enrico Fini, Moin Nabi, Nicu Sebe, and Elisa Ricci. solo-learn: A library of self-supervised methods for visual representation learning. J. Mach. Learn. Res., 23:56-1, 2022.
[24] Joel Dapello, Jenelle Feather, Hang Le, Tiago Marques, David Cox, Josh McDermott, James J DiCarlo, and SueYeon Chung. Neural population geometry reveals the role of stochasticity in robust perception. Advances in Neural Information Processing Systems, 34:15595-15607, 2021.
[25] James J DiCarlo and David D Cox. Untangling invariant object recognition. Trends in cognitive sciences, 11(8):333-341, 2007.
[26] Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, and Andrew Zisserman. With a little help from my friends: Nearest-neighbor contrastive learning of visual representations. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9588–9597, 2021.
[27] Eric Elmoznino and Michael F Bonner. High-performing neural network models of visual cortex benefit from high latent dimensionality. bioRxiv, 2022.
[28] Aleksandr Ermolov, Aliaksandr Siarohin, Enver Sangineto, and Nicu Sebe. Whitening for self-supervised representation learning. In International Conference on Machine Learning, pages 3015-3024. PMLR, 2021.
[29] Adrienne L Fairhall, Geoffrey D Lewen, William Bialek, and Robert R de Ruyter van Steveninck. Efficiency and ambiguity in an adaptive neural code. Nature, 412(6849):787-792, 2001.
[30] Jeremy Freeman, Corey M Ziemba, David J Heeger, Eero P Simoncelli, and J Anthony Movshon. A functional and perceptual signature of the second visual area in primates. Nature neuroscience, 16(7):974-981, 2013.
[31] Emmanouil Froudarakis, Uri Cohen, Maria Diamantaki, Edgar Y Walker, Jacob Reimer, Philipp Berens, Haim Sompolinsky, and Andreas S Tolias. Object manifold geometry across the mouse cortical visual hierarchy. BioRxiv, pages 2020-08, 2020.
[32] Stefano Fusi, Earl K Miller, and Mattia Rigotti. Why neurons mix: high dimensionality for higher cognition. Current opinion in neurobiology, 37:66-74, 2016.
[33] Juan A Gallego, Matthew G Perich, Lee E Miller, and Sara A Solla. Neural manifolds for the control of movement. Neuron, 94(5):978-984, 2017.
[34] Elizabeth Gardner. The space of interactions in neural network models. Journal of physics A: Mathematical and general, 21(1):257, 1988.
[35] Elizabeth Gardner and Bernard Derrida. Optimal storage properties of neural network models. Journal of Physics A: Mathematical and general, 21(1):271, 1988.
[36] F Gerl and U Krey. Storage capacity and optimal learning of potts-model perceptrons by a cavity method. Journal of Physics A: Mathematical and General, 27(22):7353, 1994.