Abdelhack M, Kamitani Y (2018) Sharpening of Hierarchical Visual Feature Representations of Blurred Images. eneuro 5, ENEURO.0443–17.2018

Allen EJ et al (2021) A massive 7T fMRI dataset to bridge cognitive and computational neuroscience. bioRxiv

Bontempi G (2021) "Statistical foundations of machine learning" (2nd edition) handbook

Buchsbaum G, Gottschalk A (1983) Trichromacy, opponent colours coding and optimum colour information transmission in the retina. In: Proceedings of the Royal Society of London. Series B. Biological Sciences 220, 113–89

Cadieu CF et al (2014) Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition. PLoS Comput Biol 10:1–18

Article  Google Scholar 

Chang N et al (2019) BOLD5000, a public fMRI dataset while viewing 5000 visual images. Sci Data 6:49

Article  PubMed  PubMed Central  Google Scholar 

Chang C, Cunningham J, Glover G (2008) Influence of heart rate on the BOLD signal: The cardiac response function. Neuroimage 44:857–69

Article  PubMed  Google Scholar 

Chen L-Z, Lin Z, Wang Z, Yang Y-L, Cheng M-M (2021) Spatial Information Guided Convolution for Real-Time RGBD Semantic Segmentation. IEEE Trans Image Process 30:2313–2324

Article  PubMed  Google Scholar 

Conway BR, Malik-Moraleda S, Gibson E (2023) Color appearance and the end of Hering’s Opponent-Colors Theory. Trends in Cognitive Sciences

Fujiwara Y, Miyawaki Y, Kamitani Y (2009) Estimating image bases for visual image reconstruction from human brain activity in Advances in Neural Information Processing Systems (eds Bengio, Y., Schuurmans, D., Lafferty, J., Williams, C. & Culotta, A.) 22 (Curran Associates, Inc.)

Goodfellow I, et al (2014) Generative Adversarial Nets in Advances in Neural Information Processing Systems (eds Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. & Weinberger, K. Q.) 27 (Curran Associates, Inc.)

Guo S, Yan Z, Zhang K, Zuo W, Zhang L (2019) Toward Convolutional Blind Denoising of Real Photographs in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 1712–1722

Hartmann C, Lazar A, Nessler B, Triesch J (2015) Where’s the Noise? Key Features of Spontaneous Activity and Neural Variability Arise through Learning in a Deterministic Network. PLoS Comput Biol 11:e1004640

Article  PubMed  PubMed Central  Google Scholar 

Hering, E. Zur Lehre vom Lichtsinne: sechs Mittheilungen an die Kaiser. Akad. der Wissenschaften in Wien (C. Gerold’s Sohn, 1878)

Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780

Article  CAS  PubMed  Google Scholar 

Huang W et al (2024) From Sight to Insight: A Multi-task Approach with the Visual Language Decoding Model. Inf Fusion 112:102573

Article  Google Scholar 

Hurvich LM, Jameson D (1957) An opponent-process theory of color vision. Psych Rev 64:384–404

Article  Google Scholar 

Kamitani Y, Tong F (2005) Decoding the visual and subjective contents of the human brain. Nat Neurosci 8:679–85

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kay K, Naselaris T, Prenger R, Gallant J (2008) Identifying natural images from human brain activity. Nature 452:352–5

Article  CAS  PubMed  PubMed Central  Google Scholar 

Krizhevsky A, Sutskever I, Hinton GE. ImageNet Classification with Deep Convolutional Neural Networks in Advances in Neural Information Processing Systems (eds Pereira, F., Burges, C., Bottou, L. & Weinberger, K.) 25 (Curran Associates, Inc., 2012)

Li Q (2022) Functional connectivity inference from fMRI data using multivariate information measures. Neural Netw 146:85–97

Article  PubMed  Google Scholar 

Li Q (2023) Saliency prediction based on multi-channel models of visual processing. Machine Vis Appl 34:47

Article  CAS  Google Scholar 

Li Q, Calhoun V, Iraji A (2024) Revealing complex functional topology brain network correspondences between humans and marmosets. Neurosci Lett 822:137624

Article  CAS  PubMed  Google Scholar 

Li FF, Fergus R, Perona P, Zekrifa D (2013) Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories. Comput Vis Image Underst 106:59–70

Google Scholar 

Li Q, Gomez-Villa A, Bertalmío M, Malo J (2022) Contrast sensitivity functions in autoencoders. J Vis 22:8–8

Article  PubMed  PubMed Central  Google Scholar 

Li Q, Steeg GV, Yu S, Malo J (2022) Functional Connectome of the Human Brain with Total Correlation. Entropy 24:1725

Article  PubMed  PubMed Central  Google Scholar 

Li Q, Ver Steeg G, Malo J (2023) Functional connectivity via total correlation: Analytical results in visual areas. Neurocomputing 571:127143

Article  Google Scholar 

Li Q, Calhoun VD, Pham TD, Iraji A (2024) Exploring nonlinear dynamics in brain functionality through phase portraits and fuzzy recurrence plots. Chaos: An Interdisciplinary Journal of Nonlinear Science. 34. https://doi.org/10.1101/2023.07.06.547922

Li Q (2021) Bidirected Information Flow in the High-Level Visual Cortex in Brain Informatics (eds Mahmud, M., Kaiser, M. S., Vassanelli, S., Dai, Q. & Zhong, N.) (Springer International Publishing, Cham), 57–66

Li Q (2022) Investigate Bidirectional Functional Brain Networks Using Directed Information in 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) , 109–116

Liu T (2016) Noise contributions to the fMRI signal: An overview. NeuroImage 143:141–151

Article  PubMed  Google Scholar 

Maass W (1997) Networks of spiking neurons: The third generation of neural network models. Neural Networks 10, 1659–1671. ISSN: 0893-6080

Miyawaki Y et al (2009) Visual Image Reconstruction from Human Brain Activity using a Combination of Multiscale Local Image Decoders. Neuron 60:915–29

Article  Google Scholar 

Mordvintsev A, Olah C, Tyka M (2015) Inceptionism: Going Deeper into Neural Networks. https://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html

Naselaris T, Kay K, Nishimoto S, Gallant J (2011) Encoding and decoding in fMRI. Neuroimage 56:400–10

Article  PubMed  Google Scholar 

Naselaris T, Prenger R, Kay K, Oliver M, Gallant J (2009) Bayesian Reconstruction of Natural Images from Human Brain Activity. Neuron 63:902–15

Article  CAS  PubMed  PubMed Central  Google Scholar 

Nishimoto S et al (2011) Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies. Current Biol 21:1641–6

Article  CAS  Google Scholar 

Ozcelik F, VanRullen R (2023) Natural scene reconstruction from fMRI signals using generative latent diffusion. Sci Rep 13:15666

Article  CAS  PubMed  PubMed Central  Google Scholar 

Park H-J, Friston K (2013) Structural and functional brain networks: from connections to cognition. Science 342:1238411

Article  PubMed  Google Scholar 

Power JD et al (2011) Functional Network Organization of the Human Brain. Neuron 72:665–678

Article  CAS  PubMed  PubMed Central  Google Scholar 

Quan Y, Chen M, Pang T, Ji H (2020) Self2Self With Dropout: Learning Self-Supervised Denoising From Single Image in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 1887–1895

Raghavan G, Thomson M (2019) Neural networks grown and self-organized by noise in NeurIPS

Ren Z et al (2021) Reconstructing seen image from brain activity by visually-guided cognitive representation and adversarial learning. Neuroimage 228:117602

Article  PubMed  Google Scholar 

Schmidhuber J (2014) Deep learning in neural networks: An overview. Neural Netw Offic J Int Neural Netw Soc 61:85–117

Article  Google Scholar 

Schurgin MW (2018) Visual memory, the long and the short of it: a review of visual working memory and long-term memory. Attention Percept Psychophys 80:1035–1056

Article  Google Scholar 

Shen G, Dwivedi K, Majima K, Horikawa T, Kamitani Y (2019) End-to-End Deep Image Reconstruction From Human Brain Activity. Front Comput Neurosci 13:21

Article  PubMed  PubMed Central  Google Scholar 

Shen G, Horikawa T, Majima K, Kamitani Y (2019) Deep image reconstruction from human brain activity. PLoS Comput Biol 15:e1006633

Article  PubMed  PubMed Central  Google Scholar 

Soh J, Cho N (2021) Deep Universal Blind Image Denoising in (25th International Conference on Pattern Recognition, Underline Science I)

Stanley G, Li F, Dan Y (1999) Reconstruction of Natural Scenes from Ensemble Responses in the Lateral Geniculate Nucleus. J Neurosci Offic J Soc Neurosci 19:8036–42

Article  CAS  Google Scholar 

Szegedy C, et al (2015) Going deeper with convolutions in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 1–9

Thirion B et al (2007) Inverse retinotopy: Inferring the visual content of images from brain activation patterns. Neuroimage 33:1104–16

Article  Google Scholar 

Ulyanov D, Vedaldi A, Lempitsky V (2020) Deep Image Prior. Int J Comput Vis 128:1867

Article  Google Scholar 

VanRullen R, Reddy L (2019) Reconstructing faces from fMRI patterns using deep generative neural networks. Commun Biol 2(1):193

Article  PubMed