ArticleScience advances2023
Cortical topographic motifs emerge in a self-organized map of object space.
Article in Science advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- 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
- Article
- Divergent modes of episodic organization underlie whether emotional learning enhances memory across event boundaries.Psychonomic bulletin & review · 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
- Dual computational systems in the development and evolution of mammalian brains.Science advances · 2026Article
- Local lateral connectivity is sufficient for replicating cortex-like topographical organization in deep neural networks.Nature communications · 2026Article
- Computational constraints underlying shape and texture functional domain organization in macaque V4.Cerebral cortex (New York, N.Y. : 1991) · 2026Article
- Investigating action topography in visual cortex and deep artificial neural networks.Nature communications · 2025Article
- Mapping macaque to human cortex with natural scene responses.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Article
- Pattern Mining of Older Drivers' Driving Behavior Through Telematics-data-driven Unsupervised Learning.IEEE sensors journal · 2025Article
- End-to-end topographic networks as models of cortical map formation and human visual behaviour.Nature human behaviour · 2025Article
- The Representational Organization of Static and Dynamic Visual Features in the Human Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- TopoNets: High performing vision and language models with brain-like topography.... International Conference on Learning Representations · 2025Article
- Individual variation in the functional lateralization of human ventral temporal cortex: Local competition and long-range coupling.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Rarely categorical, always high-dimensional: how the neural code changes along the cortical hierarchy.bioRxiv : the preprint server for biology · 2025Article
- Individual variation in the functional lateralization of human ventral temporal cortex: Local competition and long-range coupling.bioRxiv : the preprint server for biology · 2025Article
- Object-based attention during scene perception elicits boundary contraction in memory.Memory & cognition · 2025Article
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2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The human ventral visual stream has a highly systematic organization of object information, but the causal pressures driving these topographic motifs are highly debated. Here, we use self-organizing principles to learn a topographic representation of the data manifold of a deep neural network representational space. We find that a smooth mapping of this representational space showed many brain-like motifs, with a large-scale organization by animacy and real-world object size, supported by mid-level feature tuning, with naturally emerging face- and scene-selective regions. While some theories of the object-selective cortex posit that these differently tuned regions of the brain reflect a collection of distinctly specified functional modules, the present work provides computational support for an alternate hypothesis that the tuning and topography of the object-selective cortex reflect a smooth mapping of a unified representational space.
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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.