ArticleScience advances2025
Contextual effects during sensorimotor adaptation are an emergent property of population coding in a cerebellar-inspired model.
Article in Science advances, 2025. 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.
- Cerebellar rhythms: mechanisms, functions and translational opportunities.Nature reviews. Neuroscience · 2026Review
- Article
- Integrated approaches for investigating the neural bases of movement-highlights from the 34th Annual Meeting of the Society for the Neural Control of Movement.Journal of neurophysiology · 2026Article
- Automatic, But Not Autonomous: Implicit Adaptation Is Modulated by Goal-Directed Attentional Demands.eNeuro · 2026Article
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Authors and funding
2 authors.
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Abstract
The cerebellum is crucial for sensorimotor adaptation, using error information to keep the sensorimotor system well calibrated. Here, we introduce a population-coding model to explore this process, focusing on how implicit adaptation is modulated by context. The model consists of a two-layered network, designed to capture activity in both the cerebellar cortex and deep cerebellar nuclei. A core feature of the model is that within each layer, the processing units are tuned to both movement direction and the direction of movement error. The model recapitulates a large range of contextual effects such as how prior learning, error uncertainty, or temporal delay affect the rate of adaptation. While these effects have traditionally been taken to indicate a form of meta-learning or context-dependent memory within the adaptation system, the current results show that they can emerge from the population activity of a cerebellar-like hierarchical network that does not represent context.
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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.