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Cerebellar circuit computations for predictive motor control

Nature Reviews Neuroscience volume 26, pages 538–553 (2025)Cite this article

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Abstract

The rise of the deep neural network as the workhorse of artificial intelligence has brought increased attention to how network architectures serve specialized functions. The cerebellum, with its largely shallow, feedforward architecture, provides a curious example of such a specialized network. Within the cerebellum, tiny supernumerary granule cells project to a monolayer of giant Purkinje neurons that reweight synaptic inputs under the instructive influence of a unitary synaptic input from climbing fibres. What might this predominantly feedforward organization confer computationally? Here we review evidence for and against the hypothesis that the cerebellum learns basic associative feedforward control policies to speed up motor control and learning. We contrast and link this feedforward control framework with another prominent set of theories proposing that the cerebellum computes internal models. Ultimately, we suggest that the cerebellum may implement control through mechanisms that resemble internal models but involve model-free implicit mappings of high-dimensional sensorimotor contexts to motor output.

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Fig. 1: Effects and use of sensory delays in motor control systems.
Fig. 2: Comparison between alternative control system models.
Fig. 3: Delay eyelid conditioning as a canonical cerebellar feedforward control paradigm.
Fig. 4: Resolving Purkinje cell coding diversity.
Fig. 5: Two models of the cerebellar control of movement.
Fig. 6: High-dimensional granule cell population codes give rise to diverse signals.

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Acknowledgements

The authors thank members of the Person laboratory and our three reviewers for their thoughtful and constructive suggestions. We also thank A. Haith for insightful conversations about motor policy learning. The authors acknowledge support to K.P.N. (NS134561) and A.L.P. (NS114430 and NS131839).

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  1. Department of Physiology and Biophysics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

    Katrina P. Nguyen & Abigail L. Person

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Glossary

Adaptive filter

A neural circuit computation that adapts its outputs to changing inputs, achieving flexible goals.

Associative learning

A learning process that links together two stimuli. This term is often used in context of Pavlovian processes in which one stimulus predicts the occurrence of a second.

Cerebellar ataxia

A suite of motor discoordination profiles characteristic of cerebellar dysfunction.

Cerebellum proper

The cerebellum as distinct from cerebellar-like structures such as the dorsal cochlear nucleus or electrosensory lobule of the electric fish. The cerebellum proper is unique in that it possess climbing fibre inputs.

Classical conditioning

A Pavlovian learning paradigm in which a neutral ‘conditioning’ stimulus (CS) is repeatedly paired with a reflex-inducing ‘unconditioned’ stimulus (US). Animals learn to associate the CS with the US through the process of associative learning.

Complex spike

A burst of action potentials generated by Purkinje cells (PCs) in response to climbing fibre inputs. We use the term here to include dendritic Ca2+ events driven by climbing fibre inputs to PCs, even though these signals are mechanistically distinct from the spikelets emitted by the soma.

Control theory

A field of engineering that formalizes processes for generating a control variable to behave in a desired way by using inputs in various ways. These inputs can be feedback or copies of output commands, relevant examples for biological versions of controllers.

Efference copy

A copy of a motor command being sent to the periphery.

Eligibility trace

A hypothesized molecular mechanism that renders a synapse eligible for synaptic plasticity. Eligibility traces are thought to be labile in time and can be specific to individual synapses.

Internal models

Hypothesized frameworks in which the brain generates world-based and body-based models used for functions as diverse as motor control and social cognition.

Long-term depression

Use-dependent weakening of synaptic strength.

Long-term potentiation

Use-dependent strengthening of synaptic strength.

Lookup table

An engineering term depicting a discrete mapping of an input onto an output, as in a table indexed by rows and columns.

Microzone

A small cerebellar module defined by interconnected olivary climbing fibre projections to a subset of Purkinje cells (PCs), their convergent targets in the cerebellar nuclei and their projections back to the same olivary region.

Population coding

The principle that neurons encode information as ensembles with temporally evolving dynamics rather than individually.

Saccades

Brief, nearly ballistic eye movements from one target to another.

Simple spikes

An action potential type in Purkinje cells (PCs). Simple spikes are the typical type of sodium spike that propagates down the axon. They are in contrast to complex spikes, which have distinct characteristics.

Supervised learning

A machine learning perspective in which networks are trained through labelled instructive signals.

Temporal difference learning

A reinforcement learning algorithm that estimates future outcomes based on the difference between predicted and actual outcomes using intermediate cues. In the case of cerebellar learning, this refers to a condition stimulus becoming instructive through learned representation in the climbing fibre pathway.

Trace eyelid conditioning

A variation on a classical conditioning paradigm in which the end of the conditioned stimulus (CS) is separated in time from the unconditioned stimulus (US). By contrast, in delay eyelid conditioning (DEC) the CS and US co-occur.

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Nguyen, K.P., Person, A.L. Cerebellar circuit computations for predictive motor control. Nat. Rev. Neurosci. 26, 538–553 (2025). https://doi.org/10.1038/s41583-025-00936-z

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