ReviewProceedings of the National Academy of Sciences of the United States of America2024
Representations and generalization in artificial and brain neural networks.
Review in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
12 citing papers in PubMed.
- Orbitofrontal-sensory cortical interactions in learning and adaptive decision-making.Trends in cognitive sciences · 2026Review
- Exploring neural manifolds across a wide range of intrinsic dimensions.PLoS computational biology · 2026Article
- Toward a Generalizable Prediction Model of Molten Salt Mixture Density with Chemistry-Informed Transfer Learning.Chemphyschem : a European journal of chemical physics and physical chemistry · 2025Article
- Variation in the geometry of concept manifolds across human visual cortex.PLoS computational biology · 2025Article
- Practice reshapes the geometry and dynamics of task-tailored representations.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Transitional probabilities outweigh frequency of occurrence in statistical learning of simultaneously presented visual shapes.Memory & cognition · 2025Article
- Probabilistic and deep learning approaches for conductivity-driven nanocomposite classification.Scientific reports · 2025Article
- Enhanced visibility graph for EEG classification.Frontiers in neuroscience · 2025Article
- From microelectrode arrays to all-optical and multimodal neural interfaces: emerging platforms for spatiotemporal interrogation ofFrontiers in synaptic neuroscience · 2025Review
- Measuring and Controlling Solution Degeneracy across Task-Trained Recurrent Neural Networks.Advances in neural information processing systems · 2025Article
- Practice Reshapes the Geometry and Dynamics of Task-tailored Representations.bioRxiv : the preprint server for biology · 2024Article
- Machine learning meets physics: A two-way street.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Humans and animals excel at generalizing from limited data, a capability yet to be fully replicated in artificial intelligence. This perspective investigates generalization in biological and artificial deep neural networks (DNNs), in both in-distribution and out-of-distribution contexts. We introduce two hypotheses: First, the geometric properties of the neural manifolds associated with discrete cognitive entities, such as objects, words, and concepts, are powerful order parameters. They link the neural substrate to the generalization capabilities and provide a unified methodology bridging gaps between neuroscience, machine learning, and cognitive science. We overview recent progress in studying the geometry of neural manifolds, particularly in visual object recognition, and discuss theories connecting manifold dimension and radius to generalization capacity. Second, we suggest that the theory of learning in wide DNNs, especially in the thermodynamic limit, provides mechanistic insights into the learning processes generating desired neural representational geometries and generalization. This includes the role of weight norm regularization, network architecture, and hyper-parameters. We will explore recent advances in this theory and ongoing challenges. We also discuss the dynamics of learning and its relevance to the issue of representational drift in the brain.
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Registered trials
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.