ArticleScientific reports2018
Deep Residual Network Predicts Cortical Representation and Organization of Visual Features for Rapid Categorization.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
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- What have we learned about infant visual attention in the first 25 years of the 21st century?Infant behavior & development · 2025Review
- Racial stereotypes bias the neural representation of objects towards perceived weapons.Nature communications · 2025Article
- An invariant schema emerges within a neural network during hierarchical learning of visual boundaries.bioRxiv : the preprint server for biology · 2025Article
- Compression-enabled interpretability of voxelwise encoding models.PLoS computational biology · 2025Article
- A large-scale examination of inductive biases shaping high-level visual representation in brains and machines.Nature communications · 2024Article
- Unsupervised learning on spontaneous retinal activity leads to efficient neural representation geometry.ArXiv · 2023Article
- Brain Functional Representation of Highly Occluded Object Recognition.Brain sciences · 2023Article
- Applications of generative adversarial networks in neuroimaging and clinical neuroscience.NeuroImage · 2023Review
- Decoding Visual fMRI Stimuli from Human Brain Based on Graph Convolutional Neural Network.Brain sciences · 2022Article
- Self-supervised Natural Image Reconstruction and Large-scale Semantic Classification from Brain Activity.NeuroImage · 2022Article
- A self-supervised domain-general learning framework for human ventral stream representation.Nature communications · 2022Article
- Linking patterns of infant eye movements to a neural network model of the ventral stream using representational similarity analysis.Developmental science · 2022Article
- Article
- Computational models of category-selective brain regions enable high-throughput tests of selectivity.Nature communications · 2021Article
- Naturalistic Stimuli: A Paradigm for Multi-Scale Functional Characterization of the Human Brain.Current opinion in biomedical engineering · 2021Article
- A Visual Encoding Model Based on Contrastive Self-Supervised Learning for Human Brain Activity along the Ventral Visual Stream.Brain sciences · 2021Article
- Modeling NeurodegenerationFrontiers in neuroinformatics · 2021Article
- GaborNet Visual Encoding: A Lightweight Region-Based Visual Encoding Model With Good Expressiveness and Biological Interpretability.Frontiers in neuroscience · 2021Article
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Authors and funding
4 authors.
Funding
Abstract
The brain represents visual objects with topographic cortical patterns. To address how distributed visual representations enable object categorization, we established predictive encoding models based on a deep residual network, and trained them to predict cortical responses to natural movies. Using this predictive model, we mapped human cortical representations to 64,000 visual objects from 80 categories with high throughput and accuracy. Such representations covered both the ventral and dorsal pathways, reflected multiple levels of object features, and preserved semantic relationships between categories. In the entire visual cortex, object representations were organized into three clusters of categories: biological objects, non-biological objects, and background scenes. In a finer scale specific to each cluster, object representations revealed sub-clusters for further categorization. Such hierarchical clustering of category representations was mostly contributed by cortical representations of object features from middle to high levels. In summary, this study demonstrates a useful computational strategy to characterize the cortical organization and representations of visual features for rapid categorization.
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