ArticleNature communications2024
Large-scale calcium imaging reveals a systematic V4 map for encoding natural scenes.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- The colors of images preferred by individual voxels can be used to delineate functionally distinct visually responsive brain areas.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Article
- Multidimensional visual feature encoding and functional organization in the pigeon entopallium.Zoological research · 2026Article
- Computational constraints underlying shape and texture functional domain organization in macaque V4.Cerebral cortex (New York, N.Y. : 1991) · 2026Article
- A TALE OF TWO TAILS: PREFERRED AND ANTI-PREFERRED NATURAL STIMULI IN VISUAL CORTEX.... International Conference on Learning Representations · 2026Article
- Deep learning-driven characterization of single cell tuning in primate visual area V4 supports topological organization.bioRxiv : the preprint server for biology · 2025Article
- In silico discovery of representational relationships across visual cortex.Nature human behaviour · 2025Article
- Characterize neuronal responses to natural movies in the mouse superior colliculus.Frontiers in cellular neuroscience · 2025Article
- Identification of movie encoding neurons enables movie recognition AI.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- An implanted lennula approach to measure neural CaImaging neuroscience (Cambridge, Mass.)Article
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Authors and funding
8 authors.
Funding
Abstract
Biological visual systems have evolved to process natural scenes. A full understanding of visual cortical functions requires a comprehensive characterization of how neuronal populations in each visual area encode natural scenes. Here, we utilized widefield calcium imaging to record V4 cortical response to tens of thousands of natural images in male macaques. Using this large dataset, we developed a deep-learning digital twin of V4 that allowed us to map the natural image preferences of the neural population at 100-µm scale. This detailed map revealed a diverse set of functional domains in V4, each encoding distinct natural image features. We validated these model predictions using additional widefield imaging and single-cell resolution two-photon imaging. Feature attribution analysis revealed that these domains lie along a continuum from preferring spatially localized shape features to preferring spatially dispersed surface features. These results provide insights into the organizing principles that govern natural scene encoding in V4.
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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.