[19] Demis Hassabis, Dharshan Kumaran, Christopher Summerfield, and Matthew Botvinick. Neuroscience-inspired artificial intelligence. Neuron, 95(2):245-258, 2017.
[20] Anthony Zador, Sean Escola, Blake Richards, Bence Ölveczky, Yoshua Bengio, Kwabena Boahren, Matthew Botvinick, Dmitri Chklovskii, Anne Churchland, Claudia Clopath, James DiCarlo, Surya Ganguli, Jeff Hawkins, Konrad Kording, Alexei Koulakov, Yann LeCun, Timothy Lillicrap, Adam Marblestone, Bruno Olshausen, Alexandre Pouget, Cristina Savin, Terrence Sejnowski, Eero Simoncelli, Sara Solla, David Sussillo, Andreas S. Tolias, and Doris Tsao. Catalyzing next-generation artificial intelligence through neuroai. Nature Communications, 14(1):1597, Mar 2023.
[21] David H Hubel and Torsten N Wiesel. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. The Journal of physiology, 160(1):106, 1962. Publisher: Wiley-Blackwell.
[22] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.
[23] Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105-6114. PMLR, 2019.
[24] Zhe Li, Wieland Brendel, Edgar Walker, Erick Cobos, Taliah Muhammad, Jacob Reimer, Matthias Bethge, Fabian Sinz, Zachary Pitkow, and Andreas Tolias. Learning from brains how to regularize machines. In Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019.
[25] Joel Dapello, Tiago Marques, Martin Schrimpf, Franziska Geiger, David Cox, and James J DiCarlo. Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations. In Advances in Neural Information Processing Systems, volume 33, pages 13073-13087. Curran Associates, Inc., 2020.
[26] Manish V. Reddy, Andrzej Banburski, Nishka Pant, and Tomaso Poggio. Biologically Inspired Mechanisms for Adversarial Robustness, June 2020. arXiv:2006.16427 [cs, stat].
[27] J. Allman, F. Miezin, and E. McGuinness. Stimulus specific responses from beyond the classical receptive field: neurophysiological mechanisms for local-global comparisons in visual neurons. Annual Review of Neuroscience, 8:407-430, 1985.
[28] J. J. Knierim and D. C. van Essen. Neuronal responses to static texture patterns in area V1 of the alert macaque monkey. Journal of Neurophysiology, 67(4):961-980, April 1992. Publisher: American Physiological Society.
[29] Gary A. Walker, Izumi Ohzawa, and Ralph D. Freeman. Asymmetric Suppression Outside the Classical Receptive Field of the Visual Cortex. Journal of Neuroscience, 19(23):10536-10553, December 1999. Publisher: Society for Neuroscience.
[30] Zhi-Ming Shen, Wei-Feng Xu, and Chao-Yi Li. Cue-invariant detection of centre-surround discontinuity by V1 neurons in awake macaque monkey. The Journal of Physiology, 583(Pt 2):581-592, September 2007.
[31] Gregory C DeAngelis, RALPH D Freeman, and IZUMI Ohzawa. Length and width tuning of neurons in the cat's primary visual cortex. Journal of neurophysiology, 71(1):347-374, 1994.
[32] Michael P Sceniak, Dario L Ringach, Michael J Hawken, and Robert Shapley. Contrast's effect on spatial summation by macaque V1 neurons. Nature neuroscience, 2(8):733-739, 1999. Publisher: Nature Publishing Group.
[33] Michael P Sceniak, Michael J Hawken, and Robert Shapley. Visual spatial characterization of macaque V1 neurons. Journal of neurophysiology, 85(5):1873-1887, 2001. Publisher: American Physiological Society Bethesda, MD.
[34] Hosein Hasani, Mahdieh Soleymani, and Hamid Aghajan. Surround Modulation: A Bio-inspired Connectivity Structure for Convolutional Neural Networks. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d' Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019.
[35] Zahra Babaiee, Ramin Hasani, Mathias Lechner, Daniela Rus, and Radu Grosu. On-off center-surround receptive fields for accurate and robust image classification. In International Conference on Machine Learning, pages 478-489. PMLR, 2021.
[36] Alexei A Koulakov and Dmitri B Chklovskii. Orientation preference patterns in mammalian visual cortex: a wire length minimization approach. Neuron, 29(2):519-527, 2001. Publisher: Elsevier.