[47] Furkan Ozcelik and Rufin VanRullen. Brain-diffuser: Natural scene reconstruction from fmri signals using generative latent diffusion. arXiv preprint arXiv:2303.05334, 2023.
[48] Yizhuo Lu, Changde Du, Dianpeng Wang, and Huiguang He. Minddiffuser: Controlled image reconstruction from human brain activity with semantic and structural diffusion. arXiv preprint arXiv:2303.14139, 2023.
[49] Reese Kneeland, Jordyn Ojeda, Ghislain St-Yves, and Thomas Naselaris. Second sight: Using brain-optimized encoding models to align image distributions with human brain activity. ArXiv, 2023.
[50] Edgar Y Walker, Fabian H Sinz, Erick Cobos, Taliah Muhammad, Emmanouil Froudarakis, Paul G Fahey, Alexander S Ecker, Jacob Reimer, Xaq Pitkow, and Andreas S Tolias. Inception loops discover what excites neurons most using deep predictive models. Nature neuroscience, 22(12):2060-2065, 2019.
[51] Pouya Bashivan, Kohitij Kar, and James J DiCarlo. Neural population control via deep image synthesis. Science, 364(6439):eaav9436, 2019.
[52] Meenakshi Khosla and Leila Wehbe. High-level visual areas act like domain-general filters with strong selectivity and functional specialization. bioRxiv, pages 2022-03, 2022.
[53] Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al. Laion-5b: An open large-scale dataset for training next generation image-text models. arXiv preprint arXiv:2210.08402, 2022.
[54] Paweł A Pierzchlewicz, Konstantin F Willeke, Arne F Nix, Pavithra Elumalai, Kelli Restivo, Tori Shinn, Cate Nealley, Gabrielle Rodriguez, Saumil Patel, Katrin Franke, et al. Energy guided diffusion for generating neurally exciting images. bioRxiv, 2023.
[55] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020.
[56] Aria Y. Wang, Ruogu Lin, Michael J. Tarr, and Leila Wehbe. Joint interpretation of representations in neural network and the brain. In 'How Can Findings About The Brain Improve AI Systems?' Workshop @ ICLR 2021, 2021.
[57] Quan Sun, Yuxin Fang, Ledell Wu, Xinlong Wang, and Yue Cao. Eva-clip: Improved training techniques for clip at scale. arXiv preprint arXiv:2303.15389, 2023.
[58] Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. Advances in Neural Information Processing Systems, 34:8780-8794, 2021.
[59] Wei Li, Xue Xu, Xinyan Xiao, Jiachen Liu, Hu Yang, Guohao Li, Zhanpeng Wang, Zhifan Feng, Qiaoqiao She, Yajuan Lyu, et al. Upainting: Unified text-to-image diffusion generation with cross-modal guidance. arXiv preprint arXiv:2210.16031, 2022.
[60] Jacob S Prince, Ian Charest, Jan W Kurzawski, John A Pyles, Michael J Tarr, and Kendrick N Kay. Improving the accuracy of single-trial fmri response estimates using glmsingle. eLife, 11: e77599, nov 2022. ISSN 2050-084X. doi: 10.7554/eLife.77599.
[61] Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. Dpm solver++: Fast solver for guided sampling of diffusion probabilistic models. arXiv preprint arXiv:2211.01095, 2022.
[62] Susung Hong, Gyuseong Lee, Wooseok Jang, and Seungryong Kim. Improving sample quality of diffusion models using self-attention guidance. arXiv preprint arXiv:2210.00939, 2022.
[63] Alec Radford, Jong Wook Kim, Chris Hallacy, A. Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision. In ICML, 2021.