ArticleNature communications2025
A neural implementation model of feedback-based motor learning.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Corticospinal effects and motor performance changes following resistance training in healthy adults: a systematic review and meta-analysis of transcranial magnetic stimulation.Frontiers in physiology · 2026Pooled it
- Comparative efficacy of early exercise interventions for recovery outcomes after total knee arthroplasty: a systematic review and network meta-analysis.Frontiers in medicine · 2026Pooled it
- Sensory-guided human-machine joint learning accelerates the acquisition of motor imagery brain computer interface control.Nature communications · 2026Article
- Biochemical Pathways of Neuroplasticity in Sport Skill Acquisition: From Neuroscience to Coaching Practice.Brain sciences · 2026Review
- Article
- Perceptual Temporal Structure Supports Rhythm Learning and Enhances Theta Oscillations When Perception and Action Are Dissociated.Brain sciences · 2026Article
- Autocrine feedback maintains homeostatic neuropeptide expression in a peptidergic hub neuron.bioRxiv : the preprint server for biology · 2026Article
- The role of feedback for sensorimotor decisions under risk.Journal of vision · 2026Article
- Projection-specific Routing of Odor Information in the Olfactory Cortex.bioRxiv : the preprint server for biology · 2025Article
- Embodied sensorimotor control: computational modeling of the neural control of movement.ArXiv · 2025Article
- Modulation of error sensitivity during motor learning across time, space and environment variability.bioRxiv : the preprint server for biology · 2025Article
- A neural manifold view of the brain.Nature neuroscience · 2025Review
- Error encoding in human speech motor cortex.bioRxiv : the preprint server for biology · 2025Article
- Single-unit activations confer inductive biases for emergent circuit solutions to cognitive tasks.Nature machine intelligence · 2025Article
Corrections and comments
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Authors and funding
5 authors.
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Abstract
Animals use feedback to rapidly correct ongoing movements in the presence of a perturbation. Repeated exposure to a predictable perturbation leads to behavioural adaptation that compensates for its effects. Here, we tested the hypothesis that all the processes necessary for motor adaptation may emerge as properties of a controller that adaptively updates its policy. We trained a recurrent neural network to control its own output through an error-based feedback signal, which allowed it to rapidly counteract external perturbations. Implementing a biologically plausible plasticity rule based on this same feedback signal enabled the network to learn to compensate for persistent perturbations through a trial-by-trial process. The network activity changes during learning matched those from populations of neurons from monkey primary motor cortex - known to mediate both movement correction and motor adaptation - during the same task. Furthermore, our model natively reproduced several key aspects of behavioural studies in humans and monkeys. Thus, key features of trial-by-trial motor adaptation can arise from the internal properties of a recurrent neural circuit that adaptively controls its output based on ongoing feedback.
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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.