ReviewJournal of neurophysiology2020
Population coding in the cerebellum: a machine learning perspective.
Review in Journal of neurophysiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
22 citing papers in PubMed, 30 citations in OpenAlex.
- Critical period plasticity enables credit assignment.bioRxiv : the preprint server for biology · 2026Article
- Cerebellar perturbation impairs human working memory and degrades spatial tuning throughout cortex.bioRxiv : the preprint server for biology · 2026Article
- A neural language for the cerebellum: control of behavior via competing populations of Purkinje cells.Journal of neurophysiology · 2026Review
- Consensus Paper: Models of Cerebellar Functions.Cerebellum (London, England) · 2026Review
- Comparison of Signals from Cerebellar Purkinje Cells and Deep Nuclei during Temporal Prediction in Primates.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Contextual effects during sensorimotor adaptation are an emergent property of population coding in a cerebellar-inspired model.Science advances · 2025Article
- Cerebellar circuit computations for predictive motor control.Nature reviews. Neuroscience · 2025Review
- The olivary input to the cerebellum dissociates sensory events from movement plans.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- A cerebellar population coding model for sensorimotor learning.bioRxiv : the preprint server for biology · 2024Article
- Mesoscale simulations predict the role of synergistic cerebellar plasticity during classical eyeblink conditioning.PLoS computational biology · 2024Article
- Emergence of syntax and word prediction in an artificial neural circuit of the cerebellum.Nature communications · 2024Article
- Article
- Mechanisms of sensorimotor adaptation in a hierarchical state feedback control model of speech.PLoS computational biology · 2023Article
- Rate versus synchrony codes for cerebellar control of motor behavior.bioRxiv : the preprint server for biology · 2023Article
- Dual STDP processes at Purkinje cells contribute to distinct improvements in accuracy and speed of saccadic eye movements.PLoS computational biology · 2022Article
- Synchronous spiking of cerebellar Purkinje cells during control of movements.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Visuomotor control in mice and primates.Neuroscience and biobehavioral reviews · 2021Review
- The cost of correcting for error during sensorimotor adaptation.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Article
- Adaptive control of movement deceleration during saccades.PLoS computational biology · 2021Article
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
The cere resembles a feedforward, three-layer network of neurons in which the "hidden layer" consists of Purkinje cells (P-cells) and the output layer consists of deep cerebellar nucleus (DCN) neurons. In this analogy, the output of each DCN neuron is a prediction that is compared with the actual observation, resulting in an error signal that originates in the inferior olive. Efficient learning requires that the error signal reach the DCN neurons, as well as the P-cells that project onto them. However, this basic rule of learning is violated in the cerebellum: the olivary projections to the DCN are weak, particularly in adulthood. Instead, an extraordinarily strong signal is sent from the olive to the P-cells, producing complex spikes. Curiously, P-cells are grouped into small populations that converge onto single DCN neurons. Why are the P-cells organized in this way, and what is the membership criterion of each population? Here, I apply elementary mathematics from machine learning and consider the fact that P-cells that form a population exhibit a special property: they can synchronize their complex spikes, which in turn suppress activity of DCN neuron they project to. Thus complex spikes cannot only act as a teaching signal for a P-cell, but through complex spike synchrony, a P-cell population may act as a surrogate teacher for the DCN neuron that produced the erroneous output. It appears that grouping of P-cells into small populations that share a preference for error satisfies a critical requirement of efficient learning: providing error information to the output layer neuron (DCN) that was responsible for the error, as well as the hidden layer neurons (P-cells) that contributed to it. This population coding may account for several remarkable features of behavior during learning, including multiple timescales, protection from erasure, and spontaneous recovery of memory.
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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.