ArticlePLoS computational biology2021
Unveiling functions of the visual cortex using task-specific deep neural networks.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- Self-supervised learning yields representational signatures of category-selective cortex.Communications biology · 2026Article
- Contrastive learning to fine-tune feature extraction models for the visual cortex.PLoS computational biology · 2026Article
- Distinct perceptual and conceptual representations of natural actions along the lateral and dorsal visual streams.Communications biology · 2026Article
- A 2D Gabor-wavelet baseline model out-performs a 3D surface model in scene-responsive cortex.PLoS computational biology · 2026Article
- Neuro-cortical signatures of attention in uni- and multi-sensory audio-visual processing in children.Neuroscience · 2026Article
- Universal dimensions of visual representation.Science advances · 2025Article
- Representation of locomotive action affordances in human behavior, brains, and deep neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- NeuroFusionNet: cross-modal modeling from brain activity to visual understanding.Frontiers in computational neuroscience · 2025Article
- Exploring neural architectures for simultaneously recognizing multiple visual attributes.Scientific reports · 2024Article
- A large-scale examination of inductive biases shaping high-level visual representation in brains and machines.Nature communications · 2024Article
- The topology and geometry of neural representations.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Neural computations in prosopagnosia.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Diverse task-driven modeling of macaque V4 reveals functional specialization towards semantic tasks.PLoS computational biology · 2024Article
- Visual features are processed before navigational affordances in the human brain.Scientific reports · 2024Article
- Decoding face recognition abilities in the human brain.PNAS nexus · 2024Article
- High-performing neural network models of visual cortex benefit from high latent dimensionality.PLoS computational biology · 2024Article
- Representational maps in the brain: concepts, approaches, and applications.Frontiers in cellular neuroscience · 2024Review
- Neurobiological mechanisms for language, symbols and concepts: Clues from brain-constrained deep neural networks.Progress in neurobiology · 2023Review
- The neuroconnectionist research programme.Nature reviews. Neuroscience · 2023Review
- Skeletal representations of shape in the human visual cortex.Neuropsychologia · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The human visual cortex enables visual perception through a cascade of hierarchical computations in cortical regions with distinct functionalities. Here, we introduce an AI-driven approach to discover the functional mapping of the visual cortex. We related human brain responses to scene images measured with functional MRI (fMRI) systematically to a diverse set of deep neural networks (DNNs) optimized to perform different scene perception tasks. We found a structured mapping between DNN tasks and brain regions along the ventral and dorsal visual streams. Low-level visual tasks mapped onto early brain regions, 3-dimensional scene perception tasks mapped onto the dorsal stream, and semantic tasks mapped onto the ventral stream. This mapping was of high fidelity, with more than 60% of the explainable variance in nine key regions being explained. Together, our results provide a novel functional mapping of the human visual cortex and demonstrate the power of the computational approach.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.