[40] Pouya Bashivan, Kohitij Kar, and James J DiCarlo. "Neural population control via deep image synthesis". In: Science 364.6439 (2019), eaav9436.
[41] Carlos R Ponce et al. "Evolving images for visual neurons using a deep generative network reveals coding principles and neuronal preferences". In: Cell 177.4 (2019), pp. 999-1009.
[42] Edgar Y Walker et al. "Inception loops discover what excites neurons most using deep predictive models". In: Nature neuroscience 22.12 (2019), pp. 2060-2065.
[43] Benigno Uria, Iain Murray, and Hugo Larochelle. "RNADE: The real-valued neural autoregressive density-estimator". In: Advances in Neural Information Processing Systems 26 (2013).
[44] Aaron Van den Oord et al. "Conditional image generation with pixelCNN decoders". In: Advances in neural information processing systems 29 (2016).
[45] Diederik P Kingma and Max Welling. "Auto-encoding variational bayes". In: arXiv preprint arXiv:1312.6114 (2013).
[46] Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. "Stochastic backpropagation and approximate inference in deep generative models". In: International conference on machine learning. PMLR. 2014, pp. 1278-1286.
[47] Rewon Child. "Very deep vaes generalize autoregressive models and can outperform them on images". In: arXiv preprint arXiv:2011.10650 (2020).
[48] Laurent Dinh, David Krueger, and Yoshua Bengio. "Nice: Non-linear independent components estimation". In: arXiv preprint arXiv:1410.8516 (2014).
[49] Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. "Density estimation using real nvp". In: arXiv preprint arXiv:1605.08803 (2016).
[50] Mathieu Germain et al. "Made: Masked autoencoder for distribution estimation". In: International conference on machine learning. PMLR. 2015, pp. 881-889.
[51] Durk P Kingma and Prafulla Dhariwal. "Glow: Generative flow with invertible 1x1 convolutions". In: Advances in neural information processing systems 31 (2018).
[52] Jonathan Ho, Ajay Jain, and Pieter Abbeel. “Denoising diffusion probabilistic models”. In: Advances in neural information processing systems 33 (2020), pp. 6840–6851.
[53] Jiaming Song, Chenlin Meng, and Stefano Ermon. "Denoising diffusion implicit models". In: arXiv preprint arXiv:2010.02502 (2020).
[54] Robin Rombach et al. "High-resolution image synthesis with latent diffusion models". In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022, pp. 10684-10695.
[55] Ian H Stevenson and Konrad P Kording. "How advances in neural recording affect data analysis". In: Nature neuroscience 14.2 (2011), pp. 139-142.
[56] Peiran Gao and Surya Ganguli. "On simplicity and complexity in the brave new world of large-scale neuroscience". In: Current opinion in neurobiology 32 (2015), pp. 148-155.
[57] Cole Hurwitz et al. "Building population models for large-scale neural recordings: Opportunities and pitfalls". In: Current opinion in neurobiology 70 (2021), pp. 64-73.
[58] Wei Ji Ma et al. "Bayesian inference with probabilistic population codes". In: Nature neuroscience 9.11 (2006), pp. 1432-1438.
[59] Jeffrey M Beck et al. "Probabilistic population codes for Bayesian decision making". In: Neuron 60.6 (2008), pp. 1142-1152.
[60] Danilo Rezende and Shakir Mohamed. "Variational inference with normalizing flows". In: International conference on machine learning. PMLR. 2015, pp. 1530-1538.
[61] L Theis, A van den Oord, and M Bethge. “A note on the evaluation of generative models”. In: International Conference on Learning Representations (ICLR 2016). 2016, pp. 1–10.
[62] Aaron Van den Oord and Benjamin Schrauwen. "Factoring variations in natural images with deep gaussian mixture models". In: Advances in neural information processing systems 27 (2014).