39 citations · 94 across the 11 of their papers we have counts for
11 papers
MIRAGE: Robust multi-modal architectures translate fMRI-to-image models from vision to mental imagery
Reese Kneeland, Cesar Kadir Torrico Villanueva, Jordyn Ojeda +4
To be useful for downstream applications, vision decoding models that are trained to reconstruct seen images from human brain activity must be able to generalize to internally gene…
NSD-Imagery: A benchmark dataset for extending fMRI vision decoding methods to mental imagery
Reese Kneeland, Paul S. Scotti, Ghislain St-Yves +3
We release NSD-Imagery, a benchmark dataset of human fMRI activity paired with mental images, to complement the existing Natural Scenes Dataset (NSD), a large-scale dataset of fMRI…
A 7T fMRI dataset of synthetic images for out-of-distribution modeling of vision
Alessandro T. Gifford, Radoslaw M. Cichy, Thomas Naselaris +1
Now published in Nature Communications DOI: https://doi.org/10.1038/s41467-026-69345-9 Large-scale visual neural datasets such as the Natural Scenes Dataset (NSD) are boosting comp…
MindEye2: Shared-Subject Models Enable fMRI-To-Image With 1 Hour of Data
Paul S. Scotti, Mihir Tripathy, Cesar Kadir Torrico Villanueva +8
Reconstructions of visual perception from brain activity have improved tremendously, but the practical utility of such methods has been limited. This is because such models are tra…
How does the primate brain combine generative and discriminative computations in vision?
Benjamin Peters, James J. DiCarlo, Todd Gureckis +10
Vision is widely understood as an inference problem. However, two contrasting conceptions of the inference process have each been influential in research on biological vision as we…
Brain-optimized inference improves reconstructions of fMRI brain activity
Reese Kneeland, Jordyn Ojeda, Ghislain St-Yves +1
The release of large datasets and developments in AI have led to dramatic improvements in decoding methods that reconstruct seen images from human brain activity. We evaluate the p…
Second Sight: Using brain-optimized encoding models to align image distributions with human brain activity
Reese Kneeland, Jordyn Ojeda, Ghislain St-Yves +1
Two recent developments have accelerated progress in image reconstruction from human brain activity: large datasets that offer samples of brain activity in response to many thousan…
Reconstructing seen images from human brain activity via guided stochastic search
Reese Kneeland, Jordyn Ojeda, Ghislain St-Yves +1
Visual reconstruction algorithms are an interpretive tool that map brain activity to pixels. Past reconstruction algorithms employed brute-force search through a massive library to…
Visual representations in the human brain are aligned with large language models
Adrien Doerig, Tim C Kietzmann, Emily Allen +4
The human brain extracts complex information from visual inputs, including objects, their spatial and semantic interrelations, and their interactions with the environment. However,…
NeuroGen: activation optimized image synthesis for discovery neuroscience
Zijin Gu, Keith W. Jamison, Meenakshi Khosla +6
Functional MRI (fMRI) is a powerful technique that has allowed us to characterize visual cortex responses to stimuli, yet such experiments are by nature constructed based on a prio…
Encoding and decoding V1 fMRI responses to natural images with sparse nonparametric models
Vincent Q. Vu, Pradeep Ravikumar, Thomas Naselaris +3
Functional MRI (fMRI) has become the most common method for investigating the human brain. However, fMRI data present some complications for statistical analysis and modeling. One…