ArticleNature communications2023
Brain-optimized deep neural network models of human visual areas learn non-hierarchical representations.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Making models disagree to learn how brains compute.Nature reviews. Neuroscience · 2026Review
- Contrastive learning to fine-tune feature extraction models for the visual cortex.PLoS computational biology · 2026Article
- Higher visual areas act like domain-general filters with strong selectivity and functional specialization.Nature communications · 2026Article
- MIRAGE: Robust multi-modal architectures translate fMRI-to-image models from vision to mental imagery.PLoS computational biology · 2026Article
- A 7T fMRI dataset of synthetic images for out-of-distribution modeling of vision.Nature communications · 2026Article
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- Variation in the geometry of concept manifolds across human visual cortex.PLoS computational biology · 2025Article
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- Alignment of auditory artificial networks with massive individual fMRI brain data leads to generalisable improvements in brain encoding and downstream tasks.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Encoding models for developmental cognitive computational neuroscience: Promise, challenges, and potential.Developmental cognitive neuroscience · 2024Review
- Stacked regressions and structured variance partitioning for interpretable brain maps.NeuroImage · 2024Article
- Using unsupervised capsule neural network reveal spatial representations in the human brain.Human brain mapping · 2024Article
- The Geometry of Low- and High-Level Perceptual Spaces.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- How does the primate brain combine generative and discriminative computations in vision?ArXiv · 2024Article
- High-performing neural network models of visual cortex benefit from high latent dimensionality.PLoS computational biology · 2024Article
- Article
- Second Sight: Using brain-optimized encoding models to align image distributions with human brain activity.ArXiv · 2023Article
- Article
- Stacked regressions and structured variance partitioning for interpretable brain maps.bioRxiv : the preprint server for biology · 2023Article
- Article
Corrections and comments
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Authors and funding
5 authors.
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Abstract
Deep neural networks (DNNs) optimized for visual tasks learn representations that align layer depth with the hierarchy of visual areas in the primate brain. One interpretation of this finding is that hierarchical representations are necessary to accurately predict brain activity in the primate visual system. To test this interpretation, we optimized DNNs to directly predict brain activity measured with fMRI in human visual areas V1-V4. We trained a single-branch DNN to predict activity in all four visual areas jointly, and a multi-branch DNN to predict each visual area independently. Although it was possible for the multi-branch DNN to learn hierarchical representations, only the single-branch DNN did so. This result shows that hierarchical representations are not necessary to accurately predict human brain activity in V1-V4, and that DNNs that encode brain-like visual representations may differ widely in their architecture, ranging from strict serial hierarchies to multiple independent branches.
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