ArticleNeuron2024
A unifying framework for functional organization in early and higher ventral visual cortex.
Article in Neuron, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Metabolic Imaging as Future Technology and Innovation in Brain-Tumour Surgery: A Systematic Review.Current oncology (Toronto, Ont.) · 2025Pooled it
- Review
- Self-supervised learning yields representational signatures of category-selective cortex.Communications biology · 2026Article
- A single computational objective can produce specialization of streams in visual cortex.Nature communications · 2026Article
- Multidimensional Feature Tuning in Category Selective Areas of Human Visual Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Triple-N dataset: large-scale fMRI-guided dense recordings of nonhuman primate neural responses to natural scenes.Nature neuroscience · 2026Article
- Integrating neuroscience across species and scales.Nature neuroscience · 2026Article
- Re-emergence of orientation coding in primate IT cortex and deep networks reveals functional hubs for visual processing.bioRxiv : the preprint server for biology · 2026Article
- Category selectivity observed in the human brain is distinct from category selectivity observed in artificial neural networks.bioRxiv : the preprint server for biology · 2026Article
- A Deep Learning Framework for Spatiotemporal Modeling of Visual Task fMRI.bioRxiv : the preprint server for biology · 2026Article
- Exposure to naturalistic occlusion promotes generalized, human-like robustness in deep neural networks.bioRxiv : the preprint server for biology · 2026Article
- Dual computational systems in the development and evolution of mammalian brains.Science advances · 2026Article
- Representations converge as brain maps diverge along the cortical hierarchy.bioRxiv : the preprint server for biology · 2026Article
- Millimeter-scale selective amplification in the developing visual cortex.bioRxiv : the preprint server for biology · 2026Article
- Rethinking category-selectivity in human visual cortex.Cognitive neuroscience · 2026Review
- Local lateral connectivity is sufficient for replicating cortex-like topographical organization in deep neural networks.Nature communications · 2026Article
- The Emergence of Topography and Hemispheric Lateralization in High-Level Vision.Quarterly journal of experimental psychology (2006) · 2026Review
- Achieving more human brain-like vision via human EEG representational alignment.Communications biology · 2026Article
- Computational constraints underlying shape and texture functional domain organization in macaque V4.Cerebral cortex (New York, N.Y. : 1991) · 2026Article
- How 'Neural' is a Neural Foundation Model?ArXiv · 2026Article
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6 authors.
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Abstract
A key feature of cortical systems is functional organization: the arrangement of functionally distinct neurons in characteristic spatial patterns. However, the principles underlying the emergence of functional organization in the cortex are poorly understood. Here, we develop the topographic deep artificial neural network (TDANN), the first model to predict several aspects of the functional organization of multiple cortical areas in the primate visual system. We analyze the factors driving the TDANN's success and find that it balances two objectives: learning a task-general sensory representation and maximizing the spatial smoothness of responses according to a metric that scales with cortical surface area. In turn, the representations learned by the TDANN are more brain-like than in spatially unconstrained models. Finally, we provide evidence that the TDANN's functional organization balances performance with between-area connection length. Our results offer a unified principle for understanding the functional organization of the primate ventral visual system.
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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.