ArticleNature neuroscience2023
Schema formation in a neural population subspace underlies learning-to-learn in flexible sensorimotor problem-solving.
Article in Nature neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Hierarchical learning creates invariant schema within plastic neural networks.Journal of computational neuroscience · 2026Article
- Meta-learning is expressed through altered prefrontal cortical dynamics.bioRxiv : the preprint server for biology · 2026Article
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- Brain-inspired energy efficient technologies for next-generation artificial intelligence.Biological cybernetics · 2026Review
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- Domain-specific schema reuse supports flexible learning to learn in the primate brain.Nature communications · 2026Article
- Orthogonal-Rotational Dynamics Supports Efficient Encoding and Updating for Streaming Information in Working Memory.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Cognitive training shapes brain plasticity and schematic representations.Behavioral and brain functions : BBF · 2025Review
- The effects of task similarity during representation learning in brains and neural networks.Nature communications · 2025Article
- A neural circuit framework for economic choice: from building blocks of valuation to compositionality in multitasking.bioRxiv : the preprint server for biology · 2025Article
- Motor cortex flexibly deploys a high-dimensional repertoire of subskills.bioRxiv : the preprint server for biology · 2025Article
- Practice reshapes the geometry and dynamics of task-tailored representations.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Bridging Species Differences in Rule Switching: How Humans and Monkeys Solve the Same Wisconsin Card Sorting Task.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Compositional architecture: Orthogonal neural codes for task context and spatial memory in prefrontal cortex.bioRxiv : the preprint server for biology · 2025Article
- An invariant schema emerges within a neural network during hierarchical learning of visual boundaries.bioRxiv : the preprint server for biology · 2025Article
- Article
- Practice Reshapes the Geometry and Dynamics of Task-tailored Representations.bioRxiv : the preprint server for biology · 2024Article
- Flexible multitask computation in recurrent networks utilizes shared dynamical motifs.Nature neuroscience · 2024Article
- Emergent neural dynamics and geometry for generalization in a transitive inference task.PLoS computational biology · 2024Article
- Building compositional tasks with shared neural subspaces.bioRxiv : the preprint server for biology · 2024Article
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Authors and funding
5 authors.
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Abstract
Learning-to-learn, a progressive speedup of learning while solving a series of similar problems, represents a core process of knowledge acquisition that draws attention in both neuroscience and artificial intelligence. To investigate its underlying brain mechanism, we trained a recurrent neural network model on arbitrary sensorimotor mappings known to depend on the prefrontal cortex. The network displayed an exponential time course of accelerated learning. The neural substrate of a schema emerges within a low-dimensional subspace of population activity; its reuse in new problems facilitates learning by limiting connection weight changes. Our work highlights the weight-driven modifications of the vector field, which determines the population trajectory of a recurrent network and behavior. Such plasticity is especially important for preserving and reusing the learned schema in spite of undesirable changes of the vector field due to the transition to learning a new problem; the accumulated changes across problems account for the learning-to-learn dynamics.
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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.