ArticleeLife2023
Task-dependent optimal representations for cerebellar learning.
Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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Who cites it
11 citing papers in PubMed, 14 citations in OpenAlex.
- Predictive and instructive cerebellar encoding of dopamine reward drives motivated behavior.Nature neuroscience · 2026Article
- Article
- Cerebellum-inspired neural network of supervised learning with tensor-based sparse coding for multi-class classification.PLoS computational biology · 2026Article
- Random innervation of cerebellar Purkinje cells as a substrate for diverse representational learning.bioRxiv : the preprint server for biology · 2026Article
- How does the cerebellum contribute to cognitive functions?PLoS biology · 2026Review
- Barcode activity in a recurrent network model of the hippocampus enables efficient memory binding.eLife · 2026Article
- Barcode activity in a recurrent network model of the hippocampus enables efficient memory binding.bioRxiv : the preprint server for biology · 2025Article
- High-dimensional neuronal activity from low-dimensional latent dynamics: a solvable model.bioRxiv : the preprint server for biology · 2025Article
- Specific connectivity optimizes learning in thalamocortical loops.Cell reports · 2024Article
- Optimal routing to cerebellum-like structures.Nature neuroscience · 2023Article
- Structured random receptive fields enable informative sensory encodings.PLoS computational biology · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
The cerebellar granule cell layer has inspired numerous theoretical models of neural representations that support learned behaviors, beginning with the work of Marr and Albus. In these models, granule cells form a sparse, combinatorial encoding of diverse sensorimotor inputs. Such sparse representations are optimal for learning to discriminate random stimuli. However, recent observations of dense, low-dimensional activity across granule cells have called into question the role of sparse coding in these neurons. Here, we generalize theories of cerebellar learning to determine the optimal granule cell representation for tasks beyond random stimulus discrimination, including continuous input-output transformations as required for smooth motor control. We show that for such tasks, the optimal granule cell representation is substantially denser than predicted by classical theories. Our results provide a general theory of learning in cerebellum-like systems and suggest that optimal cerebellar representations are task-dependent.
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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.