[18] S Marcelja. "Mathematical description of the responses of simple cortical cells". In: JOSA 70.11 (1980), pp. 1297-1300.
[19] John G Daugman. "Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters". In: JOSA A 2.7 (1985), pp. 1160-1169.
[20] Bruno A Olshausen and David J Field. "Emergence of simple-cell receptive field properties by learning a sparse code for natural images". In: Nature 381.6583 (1996), pp. 607-609.
[21] AF Dean. "The variability of discharge of simple cells in the cat striate cortex". In: Experimental Brain Research 44.4 (1981), pp. 437-440.
[22] David J Tolhurst, J Anthony Movshon, and Andrew F Dean. "The statistical reliability of signals in single neurons in cat and monkey visual cortex". In: Vision research 23.8 (1983), pp. 775-785.
[23] Daniel LK Yamins et al. "Performance-optimized hierarchical models predict neural responses in higher visual cortex". In: Proceedings of the national academy of sciences 111.23 (2014), pp. 8619–8624.
[24] Alexander S Ecker et al. "A rotation-equivariant convolutional neural network model of primary visual cortex". In: arXiv preprint arXiv:1809.10504 (2018).
[25] Santiago A Cadena et al. "Deep convolutional models improve predictions of macaque V1 responses to natural images". In: PLoS computational biology 15.4 (2019), e1006897.
[26] Benjamin Cowley and Jonathan W Pillow. "High-contrast "gaudy" images improve the training of deep neural network models of visual cortex". In: Advances in Neural Information Processing Systems 33 (2020), pp. 21591-21603.
[27] Konstantin-Klemens Lurz et al. "Generalization in data-driven models of primary visual cortex". In: BioRxiv (2020), pp. 2020-10.
[28] Mohammad Bashiri et al. “A flow-based latent state generative model of neural population responses to natural images”. In: Advances in Neural Information Processing Systems 34 (2021), pp. 15801–15815.
[29] Santiago A Cadena et al. "Diverse task-driven modeling of macaque V4 reveals functional specialization towards semantic tasks". In: bioRxiv (2022), pp. 2022-05.
[30] Konstantin F Willeke et al. "Deep learning-driven characterization of single cell tuning in primate visual area V4 unveils topological organization". In: bioRxiv (2023), pp. 2023-05.
[31] Pawel A Pierzchlewicz et al. "Energy Guided Diffusion for Generating Neurally Exciting Images". In: bioRxiv (2023), pp. 2023-05.
[32] Charles F Cadieu et al. "Deep neural networks rival the representation of primate IT cortex for core visual object recognition". In: PLoS computational biology 10.12 (2014), e1003963.
[33] Eleanor Batty et al. "Multilayer recurrent network models of primate retinal ganglion cell responses". In: International Conference on Learning Representations. 2016.
[34] Ján Antolik et al. "Model constrained by visual hierarchy improves prediction of neural responses to natural scenes". In: PLoS computational biology 12.6 (2016), e1004927.
[35] Lane McIntosh et al. "Deep learning models of the retinal response to natural scenes". In: Advances in neural information processing systems 29 (2016).
[36] William F Kindel, Elijah D Christensen, and Joel Zylberberg. "Using deep learning to reveal the neural code for images in primary visual cortex". In: arXiv preprint arXiv:1706.06208 (2017).
[37] David Klindt et al. "Neural system identification for large populations separating "what" and "where". In: Advances in Neural Information Processing Systems 30 (2017).
[38] Martin Schrimpf et al. "Brain-score: Which artificial neural network for object recognition is most brain-like?" In: BioRxiv (2018), p. 407007.
[39] Fabian Sinz et al. "Stimulus domain transfer in recurrent models for large scale cortical population prediction on video". In: Advances in neural information processing systems 31 (2018).