Synaptic mechanisms of sensorimotor learning in the cerebellum

doi:10.1016/j.conb.2011.06.011

Current Opinion in Neurobiology

Volume 21, Issue 4, August 2011, Pages 609-615

https://doi.org/10.1016/j.conb.2011.06.011 Get rights and content

The cerebellum plays an essential role in motor learning. The ability to identify specific sensory and motor signals carried by neurons with known connectivity makes the cerebellum an attractive system for investigating how synaptic plasticity relates to learning. Early studies focused primarily on a single form of plasticity, long-term depression at parallel fiber–Purkinje cell synapses. Recent work has highlighted both the diversity of synaptic plasticity that exists within the cerebellum and the fact that individual plasticity mechanisms can have unexpected consequences when they act within neural circuits.

Highlights

► Synapses throughout the cerebellum exhibit long-term plasticity. ► In vivo whole cell recordings provide glimpses into granule cell sensory processing. ► Multiple forms of plasticity at parallel fiber synapses interact in unexpected ways. ► Mossy fiber synapses in the deep cerebellar nuclei are bidirectionally plastic. ► Understanding how diverse plasticity mechanisms work together to support learning is a current goal.

Introduction

Understanding the neural basis of motor learning requires locating sites in the brain where the necessary sensory and motor signals converge. Once located, descriptions of use-dependent plasticity mechanisms at these sites can provide candidate neural substrates for learning. In the cerebellum, the ability to map sensory and motor signals onto identified cell types within a well-defined circuit has led to strong hypotheses about the roles of individual neurons and synaptic plasticity mechanisms in learning [1, 2, 3, 4, 5, 6, 7]. Here we review recent developments in synaptic physiology and plasticity throughout the cerebellar circuit, in the context of the neural signals present at each stage and implications for learning.

Section snippets

The cerebellar circuit

The striking cellular and synaptic organization of the cerebellar cortex (Figure 1) inspired the development of a cerebellar learning theory [1, 2, 3] so influential that tests of its validity still dominate the field 40 years later [8, 9••, 10••]. The Marr–Albus–Ito theory was based on the convergence of disparate input pathways at the level of cerebellar Purkinje cells [11]. Mossy fiber and climbing fiber pathways convey two very different kinds of sensory signals to the cerebellum (Figure 1

Input stage: the granule cell layer

Cerebellar granule cells are the most numerous neurons in the brain. Their processing and transmission of sensory inputs to the cerebellar cortex is central to theories of cerebellar learning. Yet detailed information about granule cell activity patterns was lacking until relatively recently. In vivo whole cell patch clamp recordings from granule cells and their mossy fiber inputs have dramatically improved our understanding of information processing in the granular layer of cerebellar cortex [

The molecular layer

Purkinje cell inhibition of target neurons in the deep cerebellar/vestibular nuclei represents the only output of the cerebellar cortex. In that sense, the key to understanding the output of the cerebellar cortex lies in understanding the determinants of Purkinje cell activity. This is no simple task, however, as Purkinje cell activity is a consequence of intrinsic pacemaking as well as integration of synaptic inputs from excitatory parallel fibers, climbing fibers, and molecular layer

Output stage: the deep nucleus

Not long after the Marr–Albus–Ito theory, a competing hypothesis emerged from studies of the primate vestibulo-ocular reflex: that Purkinje cell activity, and not climbing fiber activity, provided the error signal that triggered learning [4, 9••]. Arguments for this idea stemmed from the nature of the sensory and motor signals carried by neurons within the vestibulo-ocular reflex circuit. According to the Miles–Lisberger model, learning-related plasticity would occur not at parallel fiber

Conclusion

Recent studies have emphasized that what happens at the level of the parallel fiber or the Purkinje cell is shaped by processing that occurs upstream, and in turn has important implications for events downstream. In vivo recordings from granule cells have transformed the way we think about parallel fiber activity, and demonstrations of long-term plasticity in Purkinje target neurons have expanded the possibilities for deep nucleus plasticity in learning.

With multiple forms of plasticity now

References and recommended reading

Papers of particular interest, published within the annual period of review, have been highlighted as:

• of special interest
•• of outstanding interest

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The cerebellum, that stripey ‘little brain’, sits at the back of your head, under your visual cortex, and contains more than half of the neurons in your entire nervous system. The cerebellum is highly conserved across vertebrates, and its evolutionary expansion has tended to proceed in concert with expansion of cerebral cortex. The crystalline neuronal architecture of the cerebellar cortex was first described by Cajal a century ago, and its functional connectivity was elucidated in exquisite anatomical and physiological detail by the mid-20^th century. The ability to clearly identify molecularly distinct cerebellar cell types that constitute discrete circuit elements is perhaps unparalleled among brain areas, even within the context of modern circuit neuroscience. Although traditionally thought of as primarily a motor structure, the cerebellum is highly interconnected with diverse brain areas and, as I will explain in this Primer, is well-poised to influence a wide range of motor and cognitive functions.
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I also know that there is a new literature of cerebellar learning in the VOR of mice (e.g. Rowan et al., 2018; Kimpo et al., 2014; Kodama et al., 2012) that has both supported and extended the model proposed originally from my laboratory’s research on the VOR of monkeys. Plasticity can occur in almost every synapse and neuron in the cerebellum (Hansel et al., 2001; Carey, 2011; Gao et al., 2012), cautioning that behavioral learning is likely caused by a symphony of plasticity mechanisms so that any proposal based on one or two sites and mechanisms of plasticity is doomed to be incomplete. The relative importance of molecular variation across defined cell types (e.g. Sugihara and Shinoda, 2004; White et al., 2014; Rodriques et al., 2019; Kozareva et al., 2020) is at a deeper level of analysis that is ignored (so far) by my focus on the neural circuit mechanisms of behavioral learning.
As a tribute to Masao Ito, we propose a model of cerebellar learning that incorporates and extends his original model. We suggest four principles that align well with conclusions from multiple cerebellar learning systems. (1) Climbing fiber inputs to the cerebellum drive early, fast, poorly-retained learning in the parallel fiber to Purkinje cell synapse. (2) Learned Purkinje cell outputs drive late, slow, well-retained learning in non-Purkinje cell inputs to neurons in the cerebellar nucleus, transferring learning from the cortex to the nucleus. (3) Recurrent feedback from Purkinje cells to the inferior olive, through interneurons in the cerebellar nucleus, limits the magnitude of fast, early learning in the cerebellar cortex. (4) Functionally different inputs are subjected to plasticity in the cerebellar cortex versus the cerebellar nucleus. A computational neural circuit model that is based on these principles mimics a large amount of neural and behavioral data obtained from the smooth pursuit eye movements of monkeys.
Motor learning induces plastic changes in Purkinje cell dendritic spines in the rat cerebellum
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El lóbulo paramediano del cerebelo está involucrado en el desempeño correcto de las habilidades motoras a través de la práctica. Las espinas dendríticas son estructuras dinámicas que regulan la estimulación sináptica excitadora. En este trabajo se estudiaron los posibles cambios plásticos en espinas de células de Purkinje del lóbulo paramediano cerebelar de ratas, durante el aprendizaje motor.
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La curva de aprendizaje reflejó una disminución consistente de los errores cometidos en el transcurso de los días de entrenamiento. Así mismo, se observaron más espinas dendríticas en los días 2 y 6 y, en particular, más espinas delgadas en los días 1, 3 y 6, menos espinas en hongo el día 3, menos espinas gordas el día 1 y más espinas anchas los días 4 y 6.
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The paramedian lobule of the cerebellum is involved in learning to correctly perform motor skills through practice. Dendritic spines are dynamic structures that regulate excitatory synaptic stimulation. We studied plastic changes occurring in the dendritic spines of Purkinje cells from the paramedian lobule of rats during motor learning.
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In contrast, the single-trial learning observed after an SS event requires a short-term mechanism of heterosynaptic and non-associative plasticity that is able to change how the mouse will respond to the LED stimulus after the aversive airpuff is delivered by itself (without the LED) (Bailey, Giustetto, Huang, Hawkins, & Kandel, 2000). Although it is well known that there are multiple sites of short-term plasticity in the neural pathways supporting CR performance (Bracha & Bloedel, 1996), including many inside the cerebellum (Carey, 2011; Gao, van Beugen, & De Zeeuw, 2012), more work will be necessary to understand how (if) they contribute to single-trial learning during eyeblink conditioning. The properties of single-trial learning uncovered in our experiments appear to be ideally suited for driving mechanisms of motor adaptation that can achieve optimal performance in the face of environmental disturbances with a fast timescale (Kording, Tenenbaum, & Shadmehr, 2007; Wei & Kording, 2009).
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Synaptic mechanisms of sensorimotor learning in the cerebellum

Highlights

Introduction

Section snippets

The cerebellar circuit

Input stage: the granule cell layer

The molecular layer

Output stage: the deep nucleus

Conclusion

References and recommended reading

Math Biosci

Brain Res

Curr Opin Neurobiol

J Neurosci

Neuron

Neuroscience

J Neurophysiol

J Neurosci

Neuron

J Physiol (Lond)

J Neurophysiol

Neuron

Cerebellum

Nat Neurosci

Neuron

Nat Neurosci

Neuron

Nature

J Neurosci

Nature

Neuron

J Neurosci

Learn Mem

J Neurophysiol

Neuron

PLoS ONE

Nat Neurosci

J Neurosci

A theory of cerebellar cortex

J Physiol (Lond)

Plasticity in the vestibulo-ocular reflex: a new hypothesis

Annu Rev Neurosci

The cerebellum: a neuronal learning machine?

Science

Beyond parallel fiber LTD: the diversity of synaptic and non-synaptic plasticity in the cerebellum

Nat Neurosci

Links from complex spikes to local plasticity and motor learning in the cerebellum of awake-behaving monkeys

Nat Neurosci

Elimination of climbing fiber instructive signals during motor learning

Nat Neurosci

Reevaluating the role of LTD in cerebellar motor learning

Neuron

The making of a complex spike: ionic composition and plasticity

Ann N Y Acad Sci

Synaptic memories upside down: bidirectional plasticity at cerebellar parallel fiber-Purkinje cell synapses

Neuron

Integration of quanta in cerebellar granule cells during sensory processing

Nature

High-fidelity transmission of sensory information by single cerebellar mossy fibre boutons

Nature

The contribution of single synapses to sensory representation in vivo

Science

Sensory transmission in cerebellar granule cells relies on similarly coded mossy fiber inputs

Proc Natl Acad Sci USA

Spillover of glutamate onto synaptic AMPA receptors enhances fast transmission at a cerebellar synapse

Neuron

Ultrastructural contributions to desensitization at cerebellar mossy fiber to granule cell synapses

J Neurosci

Desensitization properties of AMPA receptors at the cerebellar mossy fiber granule cell synapse

J Neurosci

Fast vesicle reloading and a large pool sustain high bandwidth transmission at a central synapse

Nature

Glutamate spillover suppresses inhibition by activating presynaptic mGluRs

Nature

Multimodal integration in granule cells as a basis for associative plasticity and sensory prediction in a cerebellum-like circuit

Neuron

The cerebellar microcircuit as an adaptive filter: experimental and computational evidence

Nat Rev Neurosci

Dynamics of fast and slow inhibition from cerebellar golgi cells allow flexible control of synaptic integration

Neuron