General Information

News

  • 2024/04/02 - Take the  NSD / large-scale neuroimaging dataset anonymous survey ! Deadline May 15, 2024.
  • 2023/11/22 - A minor error was discovered in the implemented transformation from anatomical to functional space for each given NSD subject (see  Technical notes  for details). The error is very minor, and so we have not changed any data files.
  • 2023/08/20 - All data and files associated with the last 3 NSD core scan sessions have now been publicly released.
  • 2021/12/16 - The NSD data paper is now published in Nature Neuroscience.
  • 2021/09/03 - The NSD dataset is now released, and version 1.1 of the NSD Data Manual is now complete. A video walkthrough of NSD data files is also now available (details below).
  • 2021/02/15 - Version 1.0 of the NSD Data Manual is now complete.

Basic information

Welcome to the  Natural Scenes Dataset (NSD) Data Manual . This web site provides a detailed, technical description of all NSD data files that are available. It will be updated as questions and issues arise. The information on this site is also available as a single downloadable PDF  (last snapshot 2024/12/02 - version 1.4)  (this may be convenient for performing "Find" queries).

If you want to browse or download the data, please see  How to get the data .

The official paper that formally describes the NSD dataset is available as:
We refer to this as the "NSD data paper". The data paper has some associated online resources at the  NSD OSF site . The contents of this data manual assume familiarity with the data paper.


Announcements and updates to the NSD dataset will be documented and logged on this page, so check back regularly.

If you have questions about the NSD dataset, please either (1) post your question to the  nsd-users mailing list , (2) open an issue or discussion on the relevant github repository (e.g.  http://github.com/cvnlab/nsdcode/  or  http://github.com/cvnlab/nsddatapaper/ ), (3) send queries directly to  kay@umn.edu , or (4) submit anonymous feedback/suggestions via this  Google form.  Please let us know if there is missing documentation or if something is not clear.

Change history

Substantive changes to NSD data files are documented and logged here:
  • 2023/08/20 - The files related to the last 3 NSD core scan sessions from each subject are now publicly released.
  • 2023/05/27 - The files related to the final memory test (nsdmemory) are now publicly released. See  Experiments  and  Behavioral data .
  • 2022/08/15 - In nsddata/inspections/rois/prf-visualandecc/, a few visualizations were incorrect. Specifically, the files "subj02_prf-eccrois_on_eccentricity.png" and "subj02_prf-visualrois_on_angle.png" have now been corrected.
  • 2022/01/26 - For user convenience, we now provide some additional versions of the nsddata/stimuli/prf stimulus files (description has been updated in  Experiments ).
  • 2021/10/20 - Diffusion derivatives are now available (nsddata_diffusion/) and documented in the data manual (see  Diffusion data ). Summary b=0 diffusion files (called nsddata/ppdata/subj*/anat/DWI_*.nii.gz) and associated nsddata/inspections/coregistration/*DWI* files have been created to help visualize the quality of the pre-processed diffusion data and their registration to the T1+T2 anatomy. In addition, the "knowndataproblems.txt" file has been slightly updated/modified.
  • 2021/09/03 - Initial public release of the NSD dataset.
  • 2021/09/02 - actually add split-half ncsnr (noise ceiling) files (this was for some reason not completed on the previous iteration on 2021/08/07)
  • 2021/08/07 - add additional files pertaining to BOLDscreen calibration; add information on race to nsddemographics.xlsx; include Phase component of the SWI scans to the raw BIDS data; add split-half ncsnr (noise ceiling) files; add pre-processed eyetracking data and inspection figures
  • 2021/07/23 - design .tsv files for the nsdsynthetic experiment were incorrect; these have been fixed.
  • 2021/05/16 - Added probmap .mgz files (see  ROIs ) and associated .png surfacevisualizations (see  Data inspections )
  • 2020/12/20 - Official version 1.0 release of nsd_mapdata (in the  nsdcode  repository).

Community-driven content

If you have NSD-related information, tools, resources, tutorials, or links that you would like to share with the community, please contact  kay@umn.edu  and the information can be listed here.
  • nsdexamples ( http://github.com/kendrickkay/nsdexamples ). These example scripts, written by Kendrick Kay, were created to demonstrate some basic loading, analysis, and visualization of the NSD dataset.
  • nsd_access ( https://github.com/tknapen/nsd_access ). This toolbox, written by Tomas Knapen, provides a convenient Python-based interface to the NSD dataset. There are also some examples of how to load data and perform basic visualization. The toolbox also enables easy access to COCO image annotation information, including category labels and bounding boxes.   

Papers and pre-prints

Here are links to papers that use NSD data.
  •  Fractional Ridge Regression: a Fast, Interpretable Reparameterization of Ridge Regression.  Rokem, A. & Kay, K.   GigaScience (2020). 
  •  Extensive sampling for complete models of individual brains.  Naselaris, T., Allen, E., & Kay, K.   Current Opinion in Behavioral Sciences (2021). 
  •  A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence.  Allen, St-Yves, Wu, Breedlove, Prince, Dowdle, Nau, Caron, Pestilli, Charest, Hutchinson, Naselaris*, & Kay*.  Nature Neuroscience (2022). 
  •  NeuroGen: activation optimized image synthesis for discovery neuroscience.  Gu, Z., Jamison, K.W., Khosla, M., Allen, E.J., Wu, Y., Naselaris, T., Kay, K., Sabuncu, M.R., Kuceyeski, A.  NeuroImage (2022). 
  •  Non-Neural Factors Influencing BOLD Response Magnitudes within Individual Subjects.  Kurzawski, J.W., Gulban, O.F., Jamison, K., Winawer, J.*, Kay, K.*   Journal of Neuroscience (2022). 
  •  Improving the accuracy of single-trial fMRI response estimates using GLMsingle.  Prince, J.S., Charest, I., Kurzawski, J.W., Pyles, J.A., Tarr, M.J., Kay, K.N.   eLife (2022). 
  •  Personalized visual encoding model construction with small data.  Zijin Gu, Keith Jamison, Mert Sabuncu, and Amy Kuceyeski  Communications Biology (2022). 
  •  Selectivity for food in human ventral visual cortex.  Nidhi Jain, Aria Wang, Margaret M. Henderson, Ruogu Lin, Jacob S. Prince, Michael J. Tarr, and Leila Wehbe  Communications Biology (2023). 
  •  Short-term plasticity in the human visual thalamus.  Jan W Kurzawski, Claudia Lunghi, Laura Biagi, Michela Tosetti, Maria Concetta Morrone, Paola Binda  eLife (2022). 
  •  Color-biased regions in the ventral visual pathway are food selective.  Pennock, I.M.L., Racey, C., Allen, E.J., Wu, Y., Naselaris, T., Kay, K.N., Franklin, A., Bosten, J.M.  Current Biology (2022). 
  •  Multiple Traces and Altered Signal-to-Noise in Systems Consolidation: Complementary Evidence from the 7T fMRI Natural Scenes Dataset.  Vanasse, T.J., Boly, M., Allen, E.J., Wu, Y., Naselaris, T., Kay, K., Cirelli, C., Tononi, G.  PNAS (2022). 
  •  The risk of bias in data denoising methods: examples from neuroimaging.  Kay, K.  PLoS One (2022). 
  •  A Highly Selective Response to Food in Human Visual Cortex Revealed by Hypothesis-Free Voxel Decomposition.  Meenakshi Khosla, N. Apurva Ratan Murty, Nancy G Kanwisher  Current Biology (2022). 
  • See commentary: Visual cortex: Big data analysis uncovers food specificity.  Michael M. Bannert and Andreas Bartels  Current Biology (2022). 
  •  Low-level tuning biases in higher visual cortex reflect the semantic informativeness of visual features.  Margaret Henderson, Michael J. Tarr, Leila Wehbe  Journal of Vision (2023). 
  •  Re-expression of CA1 and entorhinal activity patterns preserves temporal context memory at long timescales.  Futing Zou, Wanjia Guo, Emily J. Allen, Yihan Wu, Ian Charest, Thomas Naselaris, Kendrick Kay, Brice A. Kuhl, J. Benjamin Hutchinson, Sarah DuBrow  Nature Communications (2023). 
  •  A texture statistics encoding model reveals hierarchical feature selectivity across human visual cortex.  Margaret M. Henderson, Michael J. Tarr, Leila Wehbe  Journal of Neuroscience (2023). 
  •  Natural scene sampling reveals reliable coarse-scale orientation tuning in human V1.  Roth, Z.N., Kay, K.*, Merriam, E.P.*  Nature Communications (2022). 
  •  Representations in human primary visual cortex drift over time.  Roth, Z.N., Merriam, E.P.  Nature Communications (2023). 
  •  Human brain responses are modulated when exposed to optimized natural images or synthetically generated images  Zijin Gu, Keith Jamison, Mert R. Sabuncu, and Amy Kuceyeski  Communications Biology (2023). 
  •  Brain-optimized deep neural networks of human visual areas learn non-hierarchical representations.  St-Yves, G., Allen, E.J., Wu, Y., Kay, K.*, Naselaris, T.*   Nature Communications (2023). 
  •  Natural scene reconstruction from fMRI signals using generative latent diffusion  Furkan Ozcelik and Rufin VanRullen.  Scientific Reports (2023). 
  •  Better models of human high-level visual cortex emerge from natural language supervision with a large and diverse dataset.  Wang, A.Y., Kay, K., Naselaris, T., Tarr, M.J., Wehbe, L.  Nature Machine Intelligence (2023). 
  •  Encoding of Visual Objects in the Human Medial Temporal Lobe  Yue Wang, Runnan Cao and Shuo Wang  Journal of Neuroscience (2024). 
  •  Mind-bridge: reconstructing visual images based on diffusion model from human brain activity  Qing Liu, Hongqing Zhu, Ning Chen, Bingcang Huang, Weiping Lu & Ying Wang  Signal, Image and Video Processing (2024) 
  •  A unifying framework for functional organization in early and higher ventral visual cortex  Eshed Margalit, Hyodong Lee, Dawn Finzi, James J. DiCarlo, Kalanit Grill-Spector, Daniel L.K. Yamins  Neuron (2024). 
  •  Natural scenes reveal diverse representations of 2D and 3D body pose in the human brain  Hongru Zhu, Yijun Ge, Alexander Bratch, Alan Yuille, Kendrick Kay, Daniel Kersten  PNAS (2024). 
  •  Retrieving and reconstructing conceptually similar images from fMRI with latent diffusion models and a neuro-inspired brain decoding model  Matteo Ferrante, Tommaso Boccato, Luca Passamonti, and Nicola Toschi  Journal of Neural Engineering (2024). 
  •  Large-scale parameters framework with large convolutional kernel for encoding visual fMRI activity information  Shuxiao Ma, Linyuan Wang, Senbao Hou, Chi Zhang, Bin Yan  Cerebral Cortex (2024). 
  •  Primate brain: A unique connection between dorsal and ventral visual cortex  Jason D. Yeatman  Current Biology (2024). 
  •  Frontostriatal salience network expansion in individuals in depression  Charles J. Lynch, Immanuel G. Elbau, Tommy Ng, Aliza Ayaz, Shasha Zhu, Danielle Wolk, Nicola Manfredi, Megan Johnson, Megan Chang, Jolin Chou, Indira Summerville, Claire Ho, Maximilian Lueckel, Hussain Bukhari, Derrick Buchanan, Lindsay W. Victoria, Nili Solomonov, Eric Goldwaser, Stefano Moia, Cesar Caballero-Gaudes, Jonathan Downar, Fidel Vila-Rodriguez, Zafiris J. Daskalakis, Daniel M. Blumberger, Kendrick Kay, Amy Aloysi, Evan M. Gordon, Mahendra T. Bhati, Nolan Williams, Jonathan D. Power, Benjamin Zebley, Logan Grosenick, Faith M. Gunning & Conor Liston  Nature (2024). 
  •  Contrastive learning explains the emergence and function of visual category-selective regions   Jacob S. Prince ,  George A. Alvarez , and  Talia Konkle   Science Advances (2024). 
  •  A large-scale examination of inductive biases shaping high-level visual representation in brains and machines  Colin Conwell, Jacob S. Prince, Kendrick N. Kay, George A. Alvarez & Talia Konkle  Nature Communications (2024). 

Here are links to conference papers and pre-prints that use NSD data.
  •  What can 5.17 billion regression fits tell us about artificial models of the human visual system?  Colin Conwell, Jacob S. Prince, George A. Alvarez, Talia Konkle  NeurIPS SVRHM workshop (2021). 
  •  Large-Scale Benchmarking of Diverse Artificial Vision Models in Prediction of 7T Human Neuroimaging Data.  Colin Conwell, Jacob S. Prince, George A. Alvarez, Talia Konkle  bioRxiv (2022). 
  •  High-level visual areas act like domain-general filters with strong selectivity and functional specialization.  Meenakshi Khosla, Leila Wehbe  bioRxiv (2022). 
  •  Semantic scene descriptions as an objective of human vision  Doerig, A., Kietzmann, T.C., Allen, E., Wu, Y., Naselaris, T., Kay, K., Charest, I.  arXiv (2022). 
  •  Mind Reader: Reconstructing complex images from brain activities.  Sikun Lin, Thomas Sprague, Ambuj K Singh.  arXiv (2022). 
  •  High-resolution image reconstruction with latent diffusion models from human brain activity.  Takagi, Y., Nishimoto, S.  bioRxiv (2022). 
  •  Decoding natural image stimuli from fMRI data with a surface-based convolutional network.  Zijin Gu, Keith Jamison, Amy Kuceyeski, Mert Sabuncu  arXiv (2022). 
  •  Sample Reweighting for Label Denoising of Neural Activity Data  Dongfang Xu, Rong Chen  IEEE/EMBS Conference on Neural Engineering (2023) 
  •  The Algonauts Project 2023 Challenge: How the Human Brain Makes Sense of Natural Scenes.  A.T. Gifford, B. Lahner, S. Saba-Sadiya, M.G. Vilas, A. Lascelles, A. Oliva, K. Kay, G. Roig, R.M. Cichy.  arXiv (2023). 
  •  Neural Selectivity for Real-World Object Size In Natural Images  Andrew F. Luo, Leila Wehbe, Michael J. Tarr, Margaret M. Henderson  bioRxiv (2023) 
  •  MindDiffuser: Controlled Image Reconstruction from Human Brain Activity with Semantic and Structural Diffusion  Yizhuo Lu, Changde Du, Dianpeng Wang, Huiguang He  arXiv (2023). 
  •  The transition from vision to language: distinct patterns of functional connectivity for sub-regions of the visual word form area  Maya Yablonski, Iliana I Karipidis, Emily Kubota, Jason D Yeatman  bioRxiv (2023). 
  •  Reconstructing seen images from human brain activity via guided stochastic search  Reese Kneeland, Jordyn Ojeda, Ghislain St-Yves, Thomas Naselaris  arXiv (2023). 
  •  BrainCLIP: Bridging Brain and Visual-Linguistic Representation Via CLIP for Generic Natural Visual Stimulus Decoding  Yulong Liu, Yongqiang Ma, Wei Zhou, Guibo Zhu, Nanning Zheng  arXiv (2023). 
  •  Brain Captioning: Decoding human brain activity into images and text  Matteo Ferrante, Furkan Ozcelik, Tommaso Boccato, Rufin VanRullen, Nicola Toschi  arXiv (2023). 
  •  A Unifying Principle for the Functional Organization of Visual Cortex  Eshed Margalit, Hyodong Lee, Dawn Finzi, James J. DiCarlo, Kalanit Grill-Spector, Daniel L. K. Yamins  arXiv (2023). 
  •  Reconstructing the Mind’s Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priors  Paul S. Scotti*, Atmadeep Banerjee*, Jimmie Goode, Stepan Shabalin, Alex Nguyen, Ethan Cohen, Aidan J. Dempster, Nathalie Verlinde, Elad Yundler, David Weisberg, Kenneth A. Norman*, and Tanishq Mathew Abraham*  arXiv (2023). 
  •  Brain Dissection: fMRI-trained Networks Reveal Spatial Selectivity in the Processing of Natural Images  Gabriel H. Sarch, Michael J. Tarr, Katerina Fragkiadaki, Leila Wehbe  arXiv (2023). 
  •  Second Sight: Using brain-optimized encoding models to align image distributions with human brain activity  Reese Kneeland, Jordyn Ojeda, Ghislain St-Yves, Thomas Naselaris  arXiv (2023). 
  •  Brain Diffusion for Visual Exploration: Cortical Discovery using Large Scale Generative Models.  Andrew F. Luo, Margaret M. Henderson, Leila Wehbe, Michael J. Tarr  arXiv (2023).   [NeurIPS 2023 (Oral)]. 
  •  Improving visual image reconstruction from human brain activity using latent diffusion models via multiple decoded inputs.  Yu Takagi, Shinji Nishimoto  arXiv (2023).  
  •  DreamCatcher: Revealing the Language of the Brain with fMRI using GPT Embedding  Subhrasankar Chatterjee, Debasis Samanta  arXiv (2023). 
  •  What can 1.8 billion regressions tell us about the pressures shaping high-level visual representation in brains and machines?  Colin Conwell, Jacob S. Prince, Kendrick N. Kay, George A. Alvarez, Talia Konkle  bioRxiv (2023) 
  •  THE ALGONAUTS PROJECT 2023 CHALLENGE: UARK-UALBANY TEAM SOLUTION  Xuan Bac Nguyen, Xudong Liu, Xin Li, Khoa Luu  arXiv (2023) 
  •  Memory Encoding Model  Huzheng Yang, James Gee, Jianbo Shi  arXiv (2023) 
  •  Applicability of scaling laws to vision encoding models  Takuya Matsuyama, Kota S Sasaki, Shinji Nishimoto  arXiv (2023). 
  •  A contrastive coding account of category selectivity in the ventral visual stream  Jacob S. Prince, George A. Alvarez, Talia Konkle  bioRxiv (2023). 
  •  Predicting brain activity using Transformers  Hossein Adeli, Sun Minni, Nikolaus Kriegeskorte  bioRxiv (2023). 
  •  A Parameter-efficient Multi-subject Model for Predicting fMRI Activity  Connor Lane, Gregory Kiar  arXiv (2023).  
  •  Expansion of a frontostriatal salience network in individuals with depression  Charles J. Lynch, I. Elbau, Tommy Ng, Aliza Ayaz, Shasha Zhu, Nicola Manfredi, Megan A. Johnson, Daniel L Wolk, Jonathan D. Power, E. Gordon, Kendrick Norris Kay, A. Aloysi, Stefano Moia, C. Caballero-Gaudes, L. Victoria, N. Solomonov, E. Goldwaser, Benjamin Zebley, L. Grosenick, J. Downar, F. Vila-Rodriguez, Z. Daskalakis, D. Blumberger, N. Williams, F. Gunning, C. Liston  bioRxiv (2023). 
  •  UniBrain: Unify Image Reconstruction and Captioning All in One Diffusion Model from Human Brain Activity  Weijian Mai, Zhijun Zhang  arXiv (2023). 
  •  A Multimodal Visual Encoding Model Aided by Introducing Verbal Semantic Information  Ma Shuxiao, Wang Linyuan, Yan Bin  arXiv (2023). 
  •  Through their eyes: multi-subject Brain Decoding with simple alignment techniques  Matteo Ferrante, Tommaso Boccato, and Nicola Toschi  arXiv (2023). 
  •  Direct perception of affective valence from vision  Saeedeh Sadeghi, Zijin Gu, Eve DeRosa,   Amy Kuceyeski ,  Adam K. Anderson  psyArXiv (2023). 
  •  Dissociable contributions of the medial parietal cortex to recognition memory  Seth R. Koslov, Joseph W. Kable, & Brett L. Foster  bioRxiv (2023).  
  •  UNIDIRECTIONAL BRAIN-COMPUTER INTERFACE: ARTIFICIAL NEURAL NETWORK ENCODING NATURAL IMAGES TO fMRI RESPONSE IN THE VISUAL CORTEX  Ruixing Liang, Xiangyu Zhang, Qiong Li, Lai Wei, Hexin Liu, Avisha Kumar, Kelley M. Kempski Leadingham, Joshua Punnoose, Leibny Paola Garcia, Amir Manbachi  arXiv (2023). 
  •  Cortical and subcortical brain networks predict prevailing heart rate  Amy Isabella Sentis, Javier Rasero, Peter J. Gianaros, Timothy D. Verstynen  bioRxiv (2023). 
  •  DREAM: Visual Decoding from REversing HumAn Visual SysteM  Weihao Xia, Raoul de Charette, Cengiz Oztireli, Jing-Hao Xue  arXiv (2023). 
  •  BrainSCUBA: Fine-Grained Natural Language Captions of Visual Cortex Selectivity  Andrew F. Luo, Margaret M. Henderson, Michael J. Tarr, Leila Wehbe  arXiv (2023). 
  •  IDENTIFYING INTERPRETABLE VISUAL FEATURES IN  ARTIFICIAL AND BIOLOGICAL NEURAL SYSTEMS  David Klindt, Sophia Sanborn, Francisco Acosta, Fr ́ed ́eric Poitevin, Nina Miolane  arXiv (2023).  
  •  fMRI-PTE: A Large-scale fMRI Pretrained Transformer Encoder for Multi-Subject Brain Activity Decoding  Xuelin Qian, Yun Wang, Jingyang Huo, Jianfeng Feng, Yanwei Fu  arXiv (2023). 
  •  BRAIN DECODING: TOWARD REAL-TIME RECONSTRUCTION OF VISUAL PERCEPTION  Yohann Benchetrit, Hubert Banville, Jean-Remi King  arXiv (2023). 
  •  Soft Matching Distance: A metric on neural representations that captures single-neuron tuning  Meenakshi Khosla, Alex H. Williams  arXiv (2023).  
  •  Brainformer: Modeling MRI Brain Functions to Machine Vision  Xuan-Bac Nguyen , Xin Li, Samee U. Khan, Khoa Luu  arXiv (2023) 
  •  Brain Decodes Deep Nets  Huzheng Yang, James Gee*, Jianbo Shi*  arXiv (2023) 
  •  OneLLM: One Framework to Align All Modalities with Language  Jiaming Han, Kaixiong Gong, Yiyuan Zhang, Jiaqi Wang, Kaipeng Zhang,  Dahua Lin, Yu Qiao, Peng Gao, Xiangyu Yue  arXiv (2023). 
  •  Lite-Mind: Towards Efficient and Versatile Brain Representation Network  Zixuan Gong, Qi Zhang, Duoqian Miao, Guangyin Bao, Liang Hu  arXiv (2023). 
  •  Multimodal decoding of human brain activity into images and text  Matteo Ferrante, Tommaso Boccato, Furkan Ozcelik, Rufin VanRullen, Nicola Toschi  NeurIPS (2023). 
  •  ALIGNING BRAIN FUNCTIONS BOOSTS THE DECODING OF VISUAL SEMANTICS IN NOVEL SUBJECTS  Alexis Thual, Yohann Benchetrit, Felix Geilert, Jeremy Rapin, Iurii Makarov, Hubert Banville, Jean-Remi King  arXiv (2023). 
  •  Brain-optimized inference improves reconstructions of fMRI brain activity  Reese Kneeland, Jordyn Ojeda, Ghislain St-Yves, Thomas Naselaris  arXiv (2023). 
  •  Body Cosmos: An Immersive Experience Driven by Real-Time Bio-Data  Rem RunGu Lin; Yongen Ke; Kang Zhang  IEEE VIS Arts Program (VISAP) (2023). 
  •  MinD-3D: Reconstruct High-quality 3D objects in Human Brain  Jianxiong Gao, Yuqian Fu, Yun Wang, Xuelin Qian, Jianfeng Feng, Yanwei Fu  arXiv (2023). 
  •  A single computational objective drives specialization of streams in visual cortex  Dawn Finzi, Eshed Margalit, Kendrick Kay, Daniel L. K. Yamins, Kalanit Grill-Spector  bioRxiv (2023). 
  •  Evaluation of Representational Similarity Scores Across Human Visual Cortex  Francisco Acosta, Colin Conwell, Sophia Sanborn, David A. Klindt, Nina Miolane  NeurIPS (2023). 
  •  A randomized algorithm to solve reduced rank operator regression  Giacomo Turri*, Vladimir Kostic*, Pietro Novelli*, and Massimiliano Pontil  arXiv (2023). 
  •  Aligned with LLM: a new multi-modal training paradigm for encoding fMRI activity in visual cortex  Shuxiao Ma, Linyuan Wang, Senbao Hou, Bin Yan  arXiv (2024). 
  •  Recent statistics shift object representations in parahippocampal cortex  Solomon, S.H., Kay, K., & Schapiro, A.C.  bioRxiv (2024). 
  •  Parsing Brain Network Specialization: A Replication and Expansion of Wang et al. (2014)  Madeline Peterson, Dorothea L. Floris, and Jared A. Nielsen  bioRxiv (2024). 
  •  CLIP-MUSED: CLIP-GUIDED MULTI-SUBJECT VISUAL NEURAL INFORMATION SEMANTIC DECODING  Qiongyi Zhou, Changde Du, Shengpei Wang and Huiguang He  ICLR (2024). 
  •  NeuralDiffuser: Controllable fMRI Reconstruction with Primary Visual Feature Guided Diffusion  Haoyu Li, Hao Wu, Badong Chen  arXiv (2024). 
  •  Visual Image Reconstruction from Human Brain Activity using Linear Image Decoders plus Nonlinear Noise Suppression  Qiang Li  bioRxiv (2024).  
  •  See Through Their Minds: Learning Transferable Neural Representation from Cross-Subject fMRI   Yulong Liu, Yongqiang Ma, Guibo Zhu, Haodong Jing, and Nanning Zheng  arXiv (2024). 
  •  Inter-individual and inter-site neural code conversion and image reconstruction without shared stimuli  Haibao Wang, Jun Kai Ho, Fan L. Cheng, Shuntaro C. Aoki, Yusuke Muraki, Misato Tanaka, Yukiyasu Kamitani  arXiv (2024).  
  •  NeuroPictor: Refining fMRI-to-Image Reconstruction via Multi-individual Pretraining and Multi-level Modulation  Jingyang Huo, Yikai Wang, Xuelin Qian, Yun Wang, Chong Li, Jianfeng Feng, Yanwei Fu  arXiv (2024). 
  •  Psychometry: An Omnifit Model for Image Reconstruction from Human Brain Activity  Ruijie Quan, Wenguan Wang, Zhibo Tian, Fan Ma, Yi Yang  arXiv (2024). 
  •  Reconstructing Retinal Visual Images from 3T fMRI Data Enhanced by Unsupervised Learning  Yujian Xiong, Wenhui Zhu, Zhong-Lin Lu, Yalin Wang  arXiv (2024). 
  •  Unified Multimodal Decoding of Brain Signals  Weihao Xia, Raoul de Charette, Cengiz Oztireli, Jing-Hao Xue  arXiv (2024). 
  •  MindBridge: A Cross-Subject Brain Decoding Framework  Shizun Wang, Songhua Liu, Zhenxiong Tan, Xinchao Wang  arXiv (2024).  
  •  Functional Brain-to-Brain Transformation with No Shared Data  Navve Wasserman, Roman Beliy, Roy Urbach, and Michal Irani  arXiv (2024).  
  •  MindTuner: Cross-Subject Visual Decoding with Visual Fingerprint and Semantic Correction  Zixuan Gong, Qi Zhang, Guangyin Bao, Lei Zhu, Ke Liu, Liang Hu, Duoqian Miao  arXiv (2024). 
  •  Wills Aligner: A Robust Multi-Subject Brain Representation Learner  Guangyin Bao, Zixuan Gong, Qi Zhang, Jialei Zhou, Wei Fan, Kun Yi, Usman Naseem, Liang Hu, Duoqian Miao  arXiv (2024). 
  •  Integrated Gradient Correlation: a Dataset-wise Attribution Method  Pierre Lelièvre, Chien-Chung Chen  arXiv (2024). 
  • D isentangling signal and noise in neural responses through generative modeling  Kay, K., Prince, J.S., Gebhart, T., Tuckute, G., Zhou, J., Naselaris, T., Schutt, H.  bioRxiv (2024). 
  •  Neuro-Vision to Language: Image Reconstruction and Language enabled Interaction via Brain Recordings  Guobin Shen, Dongcheng Zhao, Xiang He, Linghao Feng, Yiting Dong, Jihang Wang, Qian Zhang and Yi Zeng  arXiv (2024). 
  •  Automating the Diagnosis of Human Vision Disorders by Cross-modal 3D Generation  Li Zhang, Yuankun Yang, Ziyang Xie, Zhiyuan Yuan, Jianfeng Feng, Xiatian Zhu, and Yu-Gang Jiang  arXiv (2024) 
  •  MindShot: Brain Decoding Framework Using Only One Image  Shuai Jiang, Zhu Meng, Delong Liu, Haiwen Li, Fei Su and Zhicheng Zhao  arXiv (2024). 
  •  MindFormer: A Transformer Architecture for Multi-Subject Brain Decoding via fMRI  Inhwa Han, Jaayeon Lee, Jong Chul Ye  arXiv (2024). 
  •  MindSemantix: Deciphering Brain Visual Experiences with a Brain-Language Model  Ziqi Ren, Jie Li, Xuetong Xue, Xin Li, Fan Yang, Zhicheng Jiao, Xinbo Gao  arXiv (2024). 
  •  The Wisdom of a Crowd of Brains: A Universal Brain Encoder  Roman Beliy, Navve Wasserman, Amit Zalcher, Michal Irani  arXiv (2024) 
  •  Neuro-Vis: Guided Complex Image Reconstruction from Brain Signals Using Multiple Semantic and Perceptual Controls  Gabriela M. Balisacan, Anne Therese A. Paulo  CMLDS '24: Proceedings of the International Conference on Computing, Machine Learning and Data Science (2024) 
  •  Privileged representational axes in biological and artificial neural networks  Meenakshi Khosla, Alex H Williams, Josh McDermott, Nancy Kanwisher  bioRxiv (2024). 
  •  BrainMAE: A Region-aware Self-supervised Learning Framework for Brain Signals  Yifan Yang, Yutong Mao, Xufu Liu, Xiao Liu  arXiv (2024). 
  •  AlignedCut: Visual Concepts Discovery on Brain-Guided Universal Feature Space  Huzheng Yang, James Gee*, Jianbo Shi*  arXiv (2024) 
  •  MindLDM: Reconstruct Visual Stimuli from fMRI Using Latent Diffusion Model  Junhao Guo; Chanlin Yi; Fali Li; Peng Xu; Yin Tian  2024 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) (2024) 
  •  NeuroBind: Towards Unified Multimodal Representations for Neural Signals  Fengyu Yang, Chao Feng, Daniel Wang, Tianye Wang, Ziyao Zeng, Zhiyang Xu, Hyoungseob Park, Pengliang Ji, Hanbin Zhao, Yuanning Li, Alex Wong  arXiv (2024). 
  •  Neural 3D decoding for human vision diagnosis  Li Zhang, Yuankun Yang, Ziyang Xie, Zhiyuan Yuan, Jianfeng Feng, Xiatian Zhu,   Yu-Gang Jiang  arXiv (2024). 
  •  Temporal asymmetry of neural representations predicts memory decisions  Zhifang Ye, Yufei Zhao, Emily J. Allen, Thomas Naselaris, Kendrick Kay, J. Benjamin Hutchinson, Brice A. Kuhl  bioRxiv (2024). 
  •  Hierarchical Quantum Control Gates for Functional MRI Understanding  Xuan-Bac Nguyen, Hoang-Quan Nguyen, Hugh Churchill, Samee U. Khan, Khoa Luu  arXiv (2024). 
  •  UNIVERSAL DIMENSIONS OF VISUAL REPRESENTATION  Zirui Chen & Michael F. Bonner  arXiv (2024). 
  •  FedMinds: Privacy-Preserving Personalized Brain Visual Decoding  Guangyin Bao, Duoqian Miao  arXiv (2024). 
  •  Efficient fMRI and Textual Alignment for ImageReconstruction from Human Brain Activity  Bich-Nga Pham, Trong-Tai Dam Vu, Anh-Khoa Nguyen Vu, Vinh-Tiep Nguyen  Research Square (2024). 
  •  UNIVERSAL SCALE-FREE REPRESENTATIONS IN HUMAN VISUAL CORTEX  Raj Magesh Gauthaman, Brice Ménard, Michael F. Bonner  arXiv (2024). 
  •  fMRI-3D: A Comprehensive Dataset for Enhancing fMRI-based 3D Reconstruction  Jianxiong Gao, Yuqian Fu, Yun Wang, Xuelin Qian, Jianfeng Feng, Yanwei Fu  arXiv (2024). 
  •  Unsupervised alignment reveals structural commonalities and differences in neural representations of natural scenes across individuals and brain areas  Ken Takeda, Kota Abe, Jun Kitazono, Masafumi Oizumi  bioRxiv (2024). 
  •  Positive and Negative Retinotopic Codes in the Human Hippocampus  Peter A Angeli, Adam Steel, Edward H Silson, Caroline E Robertson  bioRxiv (2024). 
  •  Decoding the Echoes of Vision from fMRI: Memory Disentangling for Past Semantic Information  Runze Xia, Congchi Yin, Piji Li  arXiv (2024). 
  •  On the Stability of Reduced-Rank Ridge Regression  David Degras, Thomas Chapalain, Bertrand Thirion  EUSIPCO 2024 - 32nd European signal processing conference (2024). 
  •  Generalizability analysis of deep learning predictions of human brain responses to augmented and semantically novel visual stimuli  Valentyn Piskovskyi, Riccardo Chimisso, Sabrina Patania, Tom Foulsham, Giuseppe Vizzari, and Dimitri Ognibene  arXiv (2024). 
  •  Contrastive Learning to Fine-Tune Feature Extraction Models for the Visual Cortex   Alex Mulrooney ,  Austin J. Brockmeier   arXiv (2024). 
  •  BRAIN MAPPING WITH DENSE FEATURES: GROUNDING CORTICAL SEMANTIC SELECTIVITY IN NATURAL IMAGES WITH VISION TRANSFORMERS  Andrew F. Luo, Jacob Yeung, Rushikesh Zawar, Shaurya Dewan,   Margaret M. Henderson, Leila Wehbe*, Michael J. Tarr*  arXiv (2024). 
  •  Retinotopic coding organizes the opponent dynamic between internally and externally oriented brain networks  Adam Steel, Peter A. Angeli, Edward H. Silson, Caroline E. Robertson  bioRxiv (2024). 
  •  Vicarious Somatotopic Maps Tile Visual Cortex  Nicholas Hedger, Thomas Naselaris, Kendrick Kay, Tomas Knapen   bioRxiv (2024). 
  •  LINBRIDGE: A LEARNABLE FRAMEWORK FOR INTERPRETING NONLINEAR NEURAL ENCODING MODELS  Xiaohui Gao*, Yue Cheng*, Peiyang Li, Yijie Niu, Yifan Ren, Yiheng Liu, Haiyang Sun, Zhuoyi Li, Weiwei Xing, and Xintao Hu  arXiv (2024). 
  •  Finding Shared Decodable Concepts and their Negations in the Brain  Cory Efird, Alex Murphy, Joel Zylberberg, Alona Fyshe  arXiv (2024).  
  •  Towards Neural Foundation Models for Vision: Aligning EEG, MEG, and fMRI Representations for Decoding, Encoding, and Modality Conversion  Matteo Ferrante, Tommaso Boccato, Grigorii Rashkov, Nicola Toschi  arXiv (2024). 
  •  In silico discovery of representational relationships across visual cortex  Alessandro T. Gifford, Maya A. Jastrzębowska, Johannes J.D. Singer, Radoslaw M. Cichy  arXiv (2024). 
  •  Quantum-Brain: Quantum-Inspired Neural Network Approach to Vision-Brain Understanding  Hoang-Quan Nguyen, Xuan-Bac Nguyen, Hugh Churchill, Arabinda Kumar Choudhary, Pawan Sinha, Samee U. Khan, Khoa Luu  arXiv (2024). 
  •  OPTIMIZED TWO-STAGE AI-BASED NEURAL DECODING FOR ENHANCED VISUAL STIMULUS RECONSTRUCTION FROM FMRI DATA  Lorenzo Veronese, Andrea Moglia, Luca Mainardi, Pietro Cerveri  arXiv (2024).