ArticleNature communications2024
De novo motor learning creates structure in neural activity that shapes adaptation.
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 14 papers.
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Who cites it
14 citing papers in PubMed.
- Human learning of noninvasive brain-computer interfaces via manifold geometry.Nature neuroscience · 2026Article
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- Motor Skill Learning of a Racing Video Game With Intuitive versus Inverted and Random Controls.Brain and behavior · 2026Article
- Granule cells reorient cortical manifolds to separate contexts but preserve their geometry.bioRxiv : the preprint server for biology · 2026Article
- Brain-computer interfaces as a causal probe for scientific inquiry.Trends in cognitive sciences · 2026Review
- Motor Cortical Output Integrates Distorted Proprioceptive Feedback.bioRxiv : the preprint server for biology · 2025Article
- Fluctuation-learning relationship in recurrent neural networks.Nature communications · 2025Article
- A neural manifold view of the brain.Nature neuroscience · 2025Review
- Learning reshapes the hippocampal representation hierarchy.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A neural implementation model of feedback-based motor learning.Nature communications · 2025Article
- Flexible inference for animal learning rules using neural networks.Advances in neural information processing systems · 2025Article
- De novo sensorimotor learning through reuse of movement components.PLoS computational biology · 2024Article
- Nonlinear manifolds underlie neural population activity during behaviour.bioRxiv : the preprint server for biology · 2024Article
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5 authors.
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Abstract
Animals can quickly adapt learned movements to external perturbations, and their existing motor repertoire likely influences their ease of adaptation. Long-term learning causes lasting changes in neural connectivity, which shapes the activity patterns that can be produced during adaptation. Here, we examined how a neural population's existing activity patterns, acquired through de novo learning, affect subsequent adaptation by modeling motor cortical neural population dynamics with recurrent neural networks. We trained networks on different motor repertoires comprising varying numbers of movements, which they acquired following various learning experiences. Networks with multiple movements had more constrained and robust dynamics, which were associated with more defined neural 'structure'-organization in the available population activity patterns. This structure facilitated adaptation, but only when the changes imposed by the perturbation were congruent with the organization of the inputs and the structure in neural activity acquired during de novo learning. These results highlight trade-offs in skill acquisition and demonstrate how different learning experiences can shape the geometrical properties of neural population activity and subsequent adaptation.
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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.