ArticleNature neuroscience2026
Neural population geometry and optimal coding of tasks with shared latent structure.
Article in Nature neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Article
- Granule cells reorient cortical manifolds to separate contexts but preserve their geometry.bioRxiv : the preprint server for biology · 2026Article
- Variations in neuronal selectivity create efficient representational geometries for perception.bioRxiv : the preprint server for biology · 2025Article
- Contrastive-Equivariant Self-Supervised Learning Improves Alignment with Primate Visual Area IT.Advances in neural information processing systems · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
Animals can recognize latent structures in their environment and apply this information to efficiently navigate the world. Several works argue that the brain supports these abilities by forming neural representations from which behaviorally relevant variables can be read out across contexts and tasks. However, it is unclear which features of neural activity facilitate downstream readout. Here we analytically determine the geometric properties of neural activity that govern linear readout generalization on a set of tasks sharing a common latent structure. We show that four statistics summarizing the dimensionality, factorization and correlation structures of neural activity determine generalization. Early in learning, optimal neural representations are lower dimensional and exhibit higher correlations between single units and task variables than late in learning. We support these predictions through biological and artificial neural data analysis. Our results tie the linearly decodable information in neural population activity to its geometry.
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