Number of spikes in climbing fibers determines the direction of cerebellar learning

The Effect of Nucleo-Olivary Stimulation on Climbing Fiber EPSPs in Purkinje Cells

Climbing fibers, connecting the inferior olive and Purkinje cells, form the nervous system's strongest neural connection. These fibers activate after critical events like motor errors or anticipation of rewards, leading to bursts of excitatory postsynaptic potentials (EPSPs) in Purkinje cells. The number of EPSPs is a crucial variable when the brain is learning a new motor skill. Yet, we do not know what determines the number of EPSPs. Here, we measured the effect of nucleo-olivary stimulation on periorbital elicited climbing fiber responses through in-vivo intracellular Purkinje cell recordings in decerebrated ferrets. The results show that while nucleo-olivary stimulation decreased the probability of a response occurring at all, it did not reduce the number of EPSPs. The results suggest that nucleo-olivary stimulation does not influence the number of EPSPs in climbing fiber bursts.

The Cerebellum as an Embodying Machine

Whereas emotion theorists often keep their distance from the embodied approach, theorists of embodiment tend to treat emotion as a mainly physiologic process. However, intimate links between emotions and the body suggest that emotions are privileged phenomena to attempt to reintegrate mind and body and that the body helps the mind in shaping emotional responses. To date, research has favored the cerebrum over other parts of the brain as a substrate of embodied emotions. However, given the widely demonstrated contribution of the cerebellum to emotional processing, research in affective neuroscience should consider embodiment theory as a useful approach for evaluating the cerebellar role in emotion and affect. The aim of this review is to insert the cerebellum among the structures needed to embody emotions, providing illustrative examples of cerebellar involvement in embodied emotions (as occurring in empathic abilities) and in impaired identification and expression of embodied emotions (as occurring in alexithymia).

Postsynaptic plasticity of Purkinje cells in mice is determined by molecular identity

Cerebellar learning is expressed as upbound or downbound changes in simple spike activity of Purkinje cell subpopulations, but the underlying mechanism remains enigmatic. By visualizing murine Purkinje cells with different molecular identities, we demonstrate that the potential for induction of long-term depression is prominent in downbound and minimal in the upbound subpopulation. These differential propensities depend on the expression profile, but not on the synaptic inputs, of the individual Purkinje cell involved, highlighting the functional relevance of intrinsic properties for memory formation.

Brain-Inspired Spiking Neural Network Controller for a Neurorobotic Whisker System

It is common for animals to use self-generated movements to actively sense the surrounding environment. For instance, rodents rhythmically move their whiskers to explore the space close to their body. The mouse whisker system has become a standard model for studying active sensing and sensorimotor integration through feedback loops. In this work, we developed a bioinspired spiking neural network model of the sensorimotor peripheral whisker system, modeling trigeminal ganglion, trigeminal nuclei, facial nuclei, and central pattern generator neuronal populations. This network was embedded in a virtual mouse robot, exploiting the Human Brain Project's Neurorobotics Platform, a simulation platform offering a virtual environment to develop and test robots driven by brain-inspired controllers. Eventually, the peripheral whisker system was adequately connected to an adaptive cerebellar network controller. The whole system was able to drive active whisking with learning capability, matching neural correlates of behavior experimentally recorded in mice.

NMDARs in granule cells contribute to parallel fiber-Purkinje cell synaptic plasticity and motor learning

Long-term synaptic plasticity is believed to be the cellular substrate of learning and memory. Synaptic plasticity rules are defined by the specific complement of receptors at the synapse and the associated downstream signaling mechanisms. In young rodents, at the cerebellar synapse between granule cells (GC) and Purkinje cells (PC), bidirectional plasticity is shaped by the balance between transcellular nitric oxide (NO) driven by presynaptic N-methyl-D-aspartate receptor (NMDAR) activation and postsynaptic calcium dynamics. However, the role and the location of NMDAR activation in these pathways is still debated in mature animals. Here, we show in adult rodents that NMDARs are present and functional in presynaptic terminals where their activation triggers NO signaling. In addition, we find that selective genetic deletion of presynaptic, but not postsynaptic, NMDARs prevents synaptic plasticity at parallel fiber-PC (PF-PC) synapses. Consistent with this finding, the selective deletion of GC NMDARs affects adaptation of the vestibulo-ocular reflex. Thus, NMDARs presynaptic to PCs are required for bidirectional synaptic plasticity and cerebellar motor learning.

Gating by Functionally Indivisible Cerebellar Circuits: a Hypothesis

The attempt to understand the cerebellum has been dominated for years by supervised learning models. The central idea is that a learning algorithm modifies transmission strength at repeatedly co-active synapses, creating memories stored as finely calibrated synaptic weights. As a result, Purkinje cells, usually the de facto output cells of these models, acquire a modified response to input in a remembered pattern. This paper proposes an alternative model of pattern memory in which the function of a match is permissive, allowing but not driving output, and accordingly controlling the timing of output but not the rate of firing by Purkinje cells. Learning does not result in graded synaptic weights. There is no supervised learning algorithm or memory of individual patterns, which, like graded weights, are unnecessary to explain the evidence. Instead, patterns are classed as simply either known or not, at the level of input to a functional population of 100s of Purkinje cells (a microzone). The standard is strict. If only a handful of Purkinje cells receive a mismatch output of the whole circuit is blocked. Only if there is a full and accurate match are projection neurons in deep nuclei, which carry the output of most circuits, released from default inhibitory restraint. Purkinje cell firing at those times is a linear function of input rates. There is no effect of modification of synaptic transmission except to either allow or block output.

Population coding in the cerebellum: a machine learning perspective

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.

Direct translation of climbing fiber burst-mediated sensory coding into post-synaptic Purkinje cell dendritic calcium

Climbing fibers (CFs) generate complex spikes (CS) and Ca²⁺ transients in cerebellar Purkinje cells (PCs), serving as instructive signals. The so-called 'all-or-none' character of CSs has been questioned since the CF burst was described. Although recent studies have indicated a sensory-driven enhancement of PC Ca²⁺ signals, how CF responds to sensory events and contributes to PC dendritic Ca²⁺ and CS remains unexplored. Here, single or simultaneous Ca²⁺ imaging of CFs and PCs in awake mice revealed the presynaptic CF Ca²⁺ amplitude encoded the sensory input's strength and directly influenced post-synaptic PC dendritic Ca²⁺ amplitude. The sensory-driven variability in CF Ca²⁺ amplitude depended on the number of spikes in the CF burst. Finally, the spike number of the CF burst determined the PC Ca²⁺ influx and CS properties. These results reveal the direct translation of sensory information-coding CF inputs into PC Ca²⁺, suggesting the sophisticated role of CFs as error signals.

50 Years Since the Marr, Ito, and Albus Models of the Cerebellum

Fifty years have passed since David Marr, Masao Ito, and James Albus proposed seminal models of cerebellar functions. These models share the essential concept that parallel-fiber-Purkinje-cell synapses undergo plastic changes, guided by climbing-fiber activities during sensorimotor learning. However, they differ in several important respects, including holistic versus complementary roles of the cerebellum, pattern recognition versus control as computational objectives, potentiation versus depression of synaptic plasticity, teaching signals versus error signals transmitted by climbing-fibers, sparse expansion coding by granule cells, and cerebellar internal models. In this review, we evaluate different features of the three models based on recent computational and experimental studies. While acknowledging that the three models have greatly advanced our understanding of cerebellar control mechanisms in eye movements and classical conditioning, we propose a new direction for computational frameworks of the cerebellum, that is, hierarchical reinforcement learning with multiple internal models.

Using deep neural networks to detect complex spikes of cerebellar Purkinje cells

Purkinje cell “complex spikes,” fired at perplexingly low rates, play a crucial role in cerebellum-based motor learning. Careful interpretations of these spikes require manually detecting them, since conventional online or offline spike sorting algorithms are optimized for classifying much simpler waveform morphologies. We present a novel deep learning approach for identifying complex spikes, which also measures additional relevant neurophysiological features, with an accuracy level matching that of human experts yet with very little time expenditure.

α2δ-2 Protein Controls Structure and Function at the Cerebellar Climbing Fiber Synapse

α2δ proteins (Cacna2d1-4) are auxiliary subunits of voltage-dependent calcium channels that also drive synapse formation and maturation. Because cerebellar Purkinje cells (PCs) predominantly, if not exclusively, express one isoform of this family, α2δ-2 (Cacna2d2), we used PCs as a model system to examine roles of α2δ in excitatory synaptic function in male and female Cacna2d2 knock-out (KO) mice. Whole-cell recordings of PCs from acute cerebellar slices revealed altered climbing fiber (CF)-evoked complex spike generation, as well as increased amplitude and faster decay of CF-evoked EPSCs. CF terminals in the KO were localized more proximally on PC dendrites, as indicated by VGLUT2⁺ immunoreactive puncta, and computational modeling demonstrated that the increased EPSC amplitude can be partly attributed to the more proximal location of CF terminals. In addition, CFs in KO mice exhibited increased multivesicular transmission, corresponding to greater sustained responses during repetitive stimulation, despite a reduction in the measured probability of release. Electron microscopy demonstrated that mutant CF terminals had twice as many vesicle release sites, providing a morphologic explanation for the enhanced glutamate release. Though KO CFs evoked larger amplitude EPSCs, the charge transfer was the same as wild-type as a result of increased glutamate reuptake, producing faster decay kinetics. Together, the larger, faster EPSCs in the KO explain the altered complex spike responses, which degrade information transfer from PCs and likely contribute to ataxia in Cacna2d2 KO mice. Our results also illustrate the multidimensional synaptic roles of α2δ proteins.SIGNIFICANCE STATEMENT α2δ proteins (Cacna2d1-4) regulate synaptic transmission and synaptogenesis, but coexpression of multiple α2δ isoforms has obscured a clear understanding of how various α2δ proteins control synaptic function. We focused on roles of the α2δ-2 protein (Cacna2d2), the deletion of which causes cerebellar ataxia and epilepsy in mice and humans. Because cerebellar Purkinje cells (PCs) only express this single isoform, we studied excitatory climbing fiber synaptic function onto PCs in Cacna2d2 KO mice. Using optical and electrophysiological analysis, we provide a detailed description of the changes in PCs lacking α2δ-2, and provide a comprehensive mechanistic explanation for how functional synaptic phenotypes contribute to the altered cerebellar output.

Complex spike clusters and false-positive rejection in a cerebellar supervised learning rule

KEY POINTS

Spike doublets comprise ∼10% of in vivo complex spike events under spontaneous conditions and ∼20% (up to 50%) under evoked conditions. Under near-physiological slice conditions, single complex spikes do not induce parallel fibre long-term depression. Doublet stimulation is required to induce long-term depression with an optimal parallel-fibre to first-complex-spike timing interval of 150 ms.

ABSTRACT

The classic example of biological supervised learning occurs at cerebellar parallel fibre (PF) to Purkinje cell synapses, comprising the most abundant synapse in the mammalian brain. Long-term depression (LTD) at these synapses is driven by climbing fibres (CFs), which fire continuously about once per second and therefore generate potential false-positive events. We show that pairs of complex spikes are required to induce LTD. In vivo, sensory stimuli evoked complex-spike doublets with intervals ≤150 ms in up to 50% of events. Using realistic [Ca2+ ]o and [Mg2+ ]o concentrations in slices, we determined that complex-spike doublets delivered 100-150 ms after PF stimulus onset were required to trigger PF-LTD, which is consistent with the requirements for eyeblink conditioning. Inter-complex spike intervals of 50-150 ms provided optimal decoding. This stimulus pattern prolonged evoked spine calcium signals and promoted CaMKII activation. Doublet activity may provide a means for CF instructive signals to stand out from background firing.

Depressed by Learning-Heterogeneity of the Plasticity Rules at Parallel Fiber Synapses onto Purkinje Cells

Climbing fiber-driven long-term depression (LTD) of parallel fiber synapses onto cerebellar Purkinje cells has long been investigated as a putative mechanism of motor learning. We recently discovered that the rules governing the induction of LTD at these synapses vary across different regions of the cerebellum. Here, we discuss the design of LTD induction protocols in light of this heterogeneity in plasticity rules. The analytical advantages of the cerebellum provide an opportunity to develop a deeper understanding of how the specific plasticity rules at synapses support the implementation of learning.

Complex Spike Wars: a New Hope

The climbing fiber-Purkinje cell circuit is one of the most powerful and highly conserved in the central nervous system. Climbing fibers exert a powerful excitatory action that results in a complex spike in Purkinje cells and normal functioning of the cerebellum depends on the integrity of climbing fiber-Purkinje cell synapse. Over the last 50 years, multiple hypotheses have been put forward on the role of the climbing fibers and complex spikes in cerebellar information processing and motor control. Central to these theories is the nature of the interaction between the low-frequency complex spike discharge and the high-frequency simple spike firing of Purkinje cells. This review examines the major hypotheses surrounding the action of the climbing fiber-Purkinje cell projection, discussing both supporting and conflicting findings. The review describes newer findings establishing that climbing fibers and complex spikes provide predictive signals about movement parameters and that climbing fiber input controls the encoding of behavioral information in the simple spike firing of Purkinje cells. Finally, we propose the dynamic encoding hypothesis for complex spike function that strives to integrate established and newer findings.

Cerebellar learning using perturbations

The cerebellum aids the learning of fast, coordinated movements. According to current consensus, erroneously active parallel fibre synapses are depressed by complex spikes signalling movement errors. However, this theory cannot solve the credit assignment problem of processing a global movement evaluation into multiple cell-specific error signals. We identify a possible implementation of an algorithm solving this problem, whereby spontaneous complex spikes perturb ongoing movements, create eligibility traces and signal error changes guiding plasticity. Error changes are extracted by adaptively cancelling the average error. This framework, stochastic gradient descent with estimated global errors (SGDEGE), predicts synaptic plasticity rules that apparently contradict the current consensus but were supported by plasticity experiments in slices from mice under conditions designed to be physiological, highlighting the sensitivity of plasticity studies to experimental conditions. We analyse the algorithm's convergence and capacity. Finally, we suggest SGDEGE may also operate in the basal ganglia.

Conditioned climbing fiber responses in cerebellar cortex and nuclei

The eyeblink conditioning paradigm captures an elementary form of associative learning in a neural circuitry that is understood to an extraordinary degree. Cerebellar cortical Purkinje cell simple spike suppression is widely regarded as the main process underlying conditioned responses (CRs), leading to disinhibition of neurons in the cerebellar nuclei that innervate eyelid muscles downstream. However, recent work highlights the addition of a conditioned Purkinje cell complex spike response, which at the level of the interposed nucleus seems to translate to a transient spike suppression that can be followed by a rapid spike facilitation. Here, we review the characteristics of these responses at the cerebellar cortical and nuclear level, and discuss possible origins and functions.

Inferior Olivary TMEM16B Mediates Cerebellar Motor Learning

Ca²⁺-activated ion channels shape membrane excitability and Ca²⁺ dynamics in response to cytoplasmic Ca²⁺ elevation. Compared to the Ca²⁺-activated K⁺ channels, known as BK and SK channels, the physiological importance of Ca²⁺-activated Cl^- channels (CaCCs) in neurons has been largely overlooked. Here we report that CaCCs coexist with BK and SK channels in inferior olivary (IO) neurons that send climbing fibers to innervate cerebellar Purkinje cells for the control of motor learning and timing. Ca²⁺ influx through the dendritic high-threshold voltage-gated Ca²⁺ channels activates CaCCs, which contribute to membrane repolarization of IO neurons. Loss of TMEM16B expression resulted in the absence of CaCCs in IO neurons, leading to markedly diminished action potential firing of IO neurons in TMEM16B knockout mice. Moreover, these mutant mice exhibited severe cerebellar motor learning deficits. Our findings thus advance the understanding of the neurophysiology of CaCCs and the ionic basis of IO neuron excitability.

Consensus Paper: Towards a Systems-Level View of Cerebellar Function: the Interplay Between Cerebellum, Basal Ganglia, and Cortex

Despite increasing evidence suggesting the cerebellum works in concert with the cortex and basal ganglia, the nature of the reciprocal interactions between these three brain regions remains unclear. This consensus paper gathers diverse recent views on a variety of important roles played by the cerebellum within the cerebello-basal ganglia-thalamo-cortical system across a range of motor and cognitive functions. The paper includes theoretical and empirical contributions, which cover the following topics: recent evidence supporting the dynamical interplay between cerebellum, basal ganglia, and cortical areas in humans and other animals; theoretical neuroscience perspectives and empirical evidence on the reciprocal influences between cerebellum, basal ganglia, and cortex in learning and control processes; and data suggesting possible roles of the cerebellum in basal ganglia movement disorders. Although starting from different backgrounds and dealing with different topics, all the contributors agree that viewing the cerebellum, basal ganglia, and cortex as an integrated system enables us to understand the function of these areas in radically different ways. In addition, there is unanimous consensus between the authors that future experimental and computational work is needed to understand the function of cerebellar-basal ganglia circuitry in both motor and non-motor functions. The paper reports the most advanced perspectives on the role of the cerebellum within the cerebello-basal ganglia-thalamo-cortical system and illustrates other elements of consensus as well as disagreements and open questions in the field.

The Roles of the Olivocerebellar Pathway in Motor Learning and Motor Control. A Consensus Paper

For many decades, the predominant view in the cerebellar field has been that the olivocerebellar system's primary function is to induce plasticity in the cerebellar cortex, specifically, at the parallel fiber-Purkinje cell synapse. However, it has also long been proposed that the olivocerebellar system participates directly in motor control by helping to shape ongoing motor commands being issued by the cerebellum. Evidence consistent with both hypotheses exists; however, they are often investigated as mutually exclusive alternatives. In contrast, here, we take the perspective that the olivocerebellar system can contribute to both the motor learning and motor control functions of the cerebellum and might also play a role in development. We then consider the potential problems and benefits of it having multiple functions. Moreover, we discuss how its distinctive characteristics (e.g., low firing rates, synchronization, and variable complex spike waveforms) make it more or less suitable for one or the other of these functions, and why having multiple functions makes sense from an evolutionary perspective. We did not attempt to reach a consensus on the specific role(s) the olivocerebellar system plays in different types of movements, as that will ultimately be determined experimentally; however, collectively, the various contributions highlight the flexibility of the olivocerebellar system, and thereby suggest that it has the potential to act in both the motor learning and motor control functions of the cerebellum.

Modulation of Complex-Spike Duration and Probability during Cerebellar Motor Learning in Visually Guided Smooth-Pursuit Eye Movements of Monkeys

Activation of an inferior olivary neuron powerfully excites Purkinje cells via its climbing fiber input and triggers a characteristic high-frequency burst, known as the complex spike (CS). The theory of cerebellar learning postulates that the CS induces long-lasting depression of the strength of synapses from active parallel fibers onto Purkinje cells, and that synaptic depression leads to changes in behavior. Prior reports showed that a CS on one learning trial is linked to a properly timed depression of simple spikes on the subsequent trial, as well as a learned change in pursuit eye movement. Further, the duration of a CS is a graded instruction for single-trial plasticity and behavioral learning. We now show across multiple learning paradigms that both the probability and duration of CS responses are correlated with the magnitudes of neural and behavioral learning in awake behaving monkeys. When the direction of the instruction for learning repeatedly was in the same direction or alternated directions, the duration and probability of CS responses decreased over a learning block along with the magnitude of trial-over-trial neural learning. When the direction of the instruction was randomized, CS duration, CS probability, and neural and behavioral learning remained stable across time. In contrast to depression, potentiation of simple-spike firing rate for ON-direction learning instructions follows a longer time course and plays a larger role as depression wanes. Computational analysis provides a model that accounts fully for the detailed statistics of a complex set of data.

Heterogeneity of Purkinje cell simple spike-complex spike interactions: zebrin- and non-zebrin-related variations

KEY POINTS

Cerebellar Purkinje cells (PCs) generate two types of action potentials, simple and complex spikes. Although they are generated by distinct mechanisms, interactions between the two spike types exist. Zebrin staining produces alternating positive and negative stripes of PCs across most of the cerebellar cortex. Thus, here we compared simple spike-complex spike interactions both within and across zebrin populations. Simple spike activity undergoes a complex modulation preceding and following a complex spike. The amplitudes of the pre- and post-complex spike modulation phases were correlated across PCs. On average, the modulation was larger for PCs in zebrin positive regions. Correlations between aspects of the complex spike waveform and simple spike activity were found, some of which varied between zebrin positive and negative PCs. The implications of the results are discussed with regard to hypotheses that complex spikes are triggered by rises in simple spike activity for either motor learning or homeostatic functions.

ABSTRACT

Purkinje cells (PCs) generate two types of action potentials, called simple and complex spikes (SSs and CSs). We first investigated the CS-associated modulation of SS activity and its relationship to the zebrin status of the PC. The modulation pattern consisted of a pre-CS rise in SS activity, and then, following the CS, a pause, a rebound, and finally a late inhibition of SS activity for both zebrin positive (Z+) and negative (Z-) cells, though the amplitudes of the phases were larger in Z+ cells. Moreover, the amplitudes of the pre-CS rise with the late inhibitory phase of the modulation were correlated across PCs. In contrast, correlations between modulation phases across CSs of individual PCs were generally weak. Next, the relationship between CS spikelets and SS activity was investigated. The number of spikelets/CS correlated with the average SS firing rate only for Z+ cells. In contrast, correlations across CSs between spikelet numbers and the amplitudes of the SS modulation phases were generally weak. Division of spikelets into likely axonally propagated and non-propagated groups (based on their interspikelet interval) showed that the correlation of spikelet number with SS firing rate primarily reflected a relationship with non-propagated spikelets. In sum, the results show both zebrin-related and non-zebrin-related physiological heterogeneity in SS-CS interactions among PCs, which suggests that the cerebellar cortex is more functionally diverse than is assumed by standard theories of cerebellar function.

Learned response sequences in cerebellar Purkinje cells

Associative learning in the cerebellum has previously focused on single movements. In eyeblink conditioning, for instance, a subject learns to blink at the right time in response to a conditional stimulus (CS), such as a tone that is repeatedly followed by an unconditional corneal stimulus (US). During conditioning, the CS and US are transmitted by mossy/parallel fibers and climbing fibers to cerebellar Purkinje cells that acquire a precisely timed pause response that drives the overt blink response. The timing of this conditional Purkinje cell response is determined by the CS-US interval and is independent of temporal patterns in the input signal. In addition to single movements, the cerebellum is also believed to be important for learning complex motor programs that require multiple precisely timed muscle contractions, such as, for example, playing the piano. In the present work, we studied Purkinje cells in decerebrate ferrets that were conditioned using electrical stimulation of mossy fiber and climbing fiber afferents as CS and US, while alternating between short and long interstimulus intervals. We found that Purkinje cells can learn double pause responses, separated by an intermediate excitation, where each pause corresponds to one interstimulus interval. The results show that individual cells can not only learn to time a single response but that they also learn an accurately timed sequential response pattern.

Are Purkinje Cell Pauses Drivers of Classically Conditioned Blink Responses?

Several lines of evidence show that classical or Pavlovian conditioning of blink responses depends on the cerebellum. Recordings from cerebellar Purkinje cells that control the eyelid and the conditioned blink show that during training with a conditioning protocol, a Purkinje cell develops a pause response to the conditional stimulus. This conditioned cellular response has many of the properties that characterise the overt blink. The present paper argues that the learned Purkinje cell pause response is the memory trace and main driver of the overt conditioned blink and that it explains many well-known behavioural phenomena.

Climbing Fibers Control Purkinje Cell Representations of Behavior

A crucial issue in understanding cerebellar function is the interaction between simple spike (SS) and complex spike (CS) discharge, the two fundamentally different activity modalities of Purkinje cells. Although several hypotheses have provided insights into the interaction, none fully explains or is completely consistent with the spectrum of experimental observations. Here, we show that during a pseudo-random manual tracking task in the monkey (Macaca mulatta), climbing fiber discharge dynamically controls the information present in the SS firing, triggering robust and rapid changes in the SS encoding of motor signals in 67% of Purkinje cells. The changes in encoding, tightly coupled to CS occurrences, consist of either increases or decreases in the SS sensitivity to kinematics or position errors and are not due to differences in SS firing rates or variability. Nor are the changes in sensitivity due to CS rhythmicity. In addition, the CS-coupled changes in encoding are not evoked by changes in kinematics or position errors. Instead, CS discharge most often leads alterations in behavior. Increases in SS encoding of a kinematic parameter are associated with larger changes in that parameter than are decreases in SS encoding. Increases in SS encoding of position error are followed by and scale with decreases in error. The results suggest a novel function of CSs, in which climbing fiber input dynamically controls the state of Purkinje cell SS encoding in advance of changes in behavior.SIGNIFICANCE STATEMENT Purkinje cells, the sole output of the cerebellar cortex, manifest two fundamentally different activity modalities, complex spike (CS) discharge and simple spike (SS) firing. Elucidating cerebellar function will require an understanding of the interactions, both short- and long-term, between CS and SS firing. This study shows that CSs dynamically control the information encoded in a Purkinje cell's SS activity by rapidly increasing or decreasing the SS sensitivity to kinematics and/or performance errors independent of firing rate. In many cases, the CS-coupled shift in SS encoding leads a change in behavior. These novel findings on the interaction between CS and SS firing provide for a new hypothesis in which climbing fiber input adjusts the encoding of SS information in advance of a change in behavior.

The Errors of Our Ways: Understanding Error Representations in Cerebellar-Dependent Motor Learning

The cerebellum is essential for error-driven motor learning and is strongly implicated in detecting and correcting for motor errors. Therefore, elucidating how motor errors are represented in the cerebellum is essential in understanding cerebellar function, in general, and its role in motor learning, in particular. This review examines how motor errors are encoded in the cerebellar cortex in the context of a forward internal model that generates predictions about the upcoming movement and drives learning and adaptation. In this framework, sensory prediction errors, defined as the discrepancy between the predicted consequences of motor commands and the sensory feedback, are crucial for both on-line movement control and motor learning. While many studies support the dominant view that motor errors are encoded in the complex spike discharge of Purkinje cells, others have failed to relate complex spike activity with errors. Given these limitations, we review recent findings in the monkey showing that complex spike modulation is not necessarily required for motor learning or for simple spike adaptation. Also, new results demonstrate that the simple spike discharge provides continuous error signals that both lead and lag the actual movements in time, suggesting errors are encoded as both an internal prediction of motor commands and the actual sensory feedback. These dual error representations have opposing effects on simple spike discharge, consistent with the signals needed to generate sensory prediction errors used to update a forward internal model.

Oscillations, Timing, Plasticity, and Learning in the Cerebellum

The highly stereotyped, crystal-like architecture of the cerebellum has long served as a basis for hypotheses with regard to the function(s) that it subserves. Historically, most clinical observations and experimental work have focused on the involvement of the cerebellum in motor control, with particular emphasis on coordination and learning. Two main models have been suggested to account for cerebellar functioning. According to Llinás's theory, the cerebellum acts as a control machine that uses the rhythmic activity of the inferior olive to synchronize Purkinje cell populations for fine-tuning of coordination. In contrast, the Ito-Marr-Albus theory views the cerebellum as a motor learning machine that heuristically refines synaptic weights of the Purkinje cell based on error signals coming from the inferior olive. Here, we review the role of timing of neuronal events, oscillatory behavior, and synaptic and non-synaptic influences in functional plasticity that can be recorded in awake animals in various physiological and pathological models in a perspective that also includes non-motor aspects of cerebellar function. We discuss organizational levels from genes through intracellular signaling, synaptic network to system and behavior, as well as processes from signal production and processing to memory, delegation, and actual learning. We suggest an integrative concept for control and learning based on articulated oscillation templates.

The dynamic relationship between cerebellar Purkinje cell simple spikes and the spikelet number of complex spikes

Key points

Purkinje cells are the sole output of the cerebellar cortex and fire two distinct types of action potential: simple spikes and complex spikes.

Previous studies have mainly considered complex spikes as unitary events, even though the waveform is composed of varying numbers of spikelets.

The extent to which differences in spikelet number affect simple spike activity (and vice versa) remains unclear.

We found that complex spikes with greater numbers of spikelets are preceded by higher simple spike firing rates but, following the complex spike, simple spikes are reduced in a manner that is graded with spikelet number.

This dynamic interaction has important implications for cerebellar information processing, and suggests that complex spike spikelet number may maintain Purkinje cells within their operational range.

Abstract

Purkinje cells are central to cerebellar function because they form the sole output of the cerebellar cortex. They exhibit two distinct types of action potential: simple spikes and complex spikes. It is widely accepted that interaction between these two types of impulse is central to cerebellar cortical information processing. Previous investigations of the interactions between simple spikes and complex spikes have mainly considered complex spikes as unitary events. However, complex spikes are composed of an initial large spike followed by a number of secondary components, termed spikelets. The number of spikelets within individual complex spikes is highly variable and the extent to which differences in complex spike spikelet number affects simple spike activity (and vice versa) remains poorly understood. In anaesthetized adult rats, we have found that Purkinje cells recorded from the posterior lobe vermis and hemisphere have high simple spike firing frequencies that precede complex spikes with greater numbers of spikelets. This finding was also evident in a small sample of Purkinje cells recorded from the posterior lobe hemisphere in awake cats. In addition, complex spikes with a greater number of spikelets were associated with a subsequent reduction in simple spike firing rate. We therefore suggest that one important function of spikelets is the modulation of Purkinje cell simple spike firing frequency, which has implications for controlling cerebellar cortical output and motor learning.

Purkinje cells are the sole output of the cerebellar cortex and fire two distinct types of action potential: simple spikes and complex spikes.

Previous studies have mainly considered complex spikes as unitary events, even though the waveform is composed of varying numbers of spikelets.

The extent to which differences in spikelet number affect simple spike activity (and vice versa) remains unclear.

We found that complex spikes with greater numbers of spikelets are preceded by higher simple spike firing rates but, following the complex spike, simple spikes are reduced in a manner that is graded with spikelet number.

This dynamic interaction has important implications for cerebellar information processing, and suggests that complex spike spikelet number may maintain Purkinje cells within their operational range.

Climbing Fiber Regulation of Spontaneous Purkinje Cell Activity and Cerebellum-Dependent Blink Responses(1,2,3)

It has been known for a long time that GABAergic Purkinje cells in the cerebellar cortex, as well as their target neurons in the cerebellar nuclei, are spontaneously active. The cerebellar output will, therefore, depend on how input is integrated into this spontaneous activity. It has been shown that input from climbing fibers originating in the inferior olive controls the spontaneous activity in Purkinje cells. While blocking climbing fiber input to the Purkinje cells causes a dramatic increase in the firing rate, increased climbing fiber activity results in reduced Purkinje cell activity. However, the exact calibration of this regulation has not been examined systematically. Here we examine the relation between climbing fiber stimulation frequency and Purkinje cell activity in unanesthetized decerebrated ferrets. The results revealed a gradual suppression of Purkinje cell activity, starting at climbing fiber stimulation frequencies as low as 0.5 Hz. At 4 Hz, Purkinje cells were completely silenced. This effect lasted an average of 2 min after the stimulation rate was reduced to a lower level. We also examined the effect of sustained climbing fiber stimulation on overt behavior. Specifically, we analyzed conditioned blink responses, which are known to be dependent on the cerebellum, while stimulating the climbing fibers at different frequencies. In accordance with the neurophysiological data, the conditioned blink responses were suppressed at stimulation frequencies of ≥4 Hz.

Purkinje cell activity during classical conditioning with different conditional stimuli explains central tenet of Rescorla–Wagner model [corrected]

A central tenet of Rescorla and Wagner's model of associative learning is that the reinforcement value of a paired trial diminishes as the associative strength between the presented stimuli increases. Despite its fundamental importance to behavioral sciences, the neural mechanisms underlying the model have not been fully explored. Here, we present findings that, taken together, can explain why a stronger association leads to a reduced reinforcement value, within the context of eyeblink conditioning. Specifically, we show that learned pause responses in Purkinje cells, which trigger adaptively timed conditioned eyeblinks, suppress the unconditional stimulus (US) signal in a graded manner. Furthermore, by examining how Purkinje cells respond to two distinct conditional stimuli and to a compound stimulus, we provide evidence that could potentially help explain the somewhat counterintuitive overexpectation phenomenon, which was derived from the Rescorla-Wagner model.

Duration of Purkinje cell complex spikes increases with their firing frequency

Climbing fiber (CF) triggered complex spikes (CS) are massive depolarization bursts in the cerebellar Purkinje cell (PC), showing several high frequency spikelet components (±600 Hz). Since its early observations, the CS is known to vary in shape. In this study we describe CS waveforms, extracellularly recorded in awake primates (Macaca mulatta) performing saccades. Every PC analyzed showed a range of CS shapes with profoundly different duration and number of spikelets. The initial part of the CS was rather constant but the later part differed greatly, with a pronounced jitter of the last spikelets causing a large variation in total CS duration. Waveforms did not effect the following pause duration in the simple spike (SS) train, nor were SS firing rates predictive of the waveform shapes or vice versa. The waveforms did not differ between experimental conditions nor was there a preferred sequential order of CS shapes throughout the recordings. Instead, part of their variability, the timing jitter of the CS’s last spikelets, strongly correlated with interval length to the preceding CS: shorter CS intervals resulted in later appearance of the last spikelets in the CS burst, and vice versa. A similar phenomenon was observed in rat PCs recorded in vitro upon repeated extracellular stimulation of CFs at different frequencies in slice experiments. All together these results strongly suggest that the variability in the timing of the last spikelet is due to CS frequency dependent changes in PC excitability.

Adaptive robotic control driven by a versatile spiking cerebellar network

The cerebellum is involved in a large number of different neural processes, especially in associative learning and in fine motor control. To develop a comprehensive theory of sensorimotor learning and control, it is crucial to determine the neural basis of coding and plasticity embedded into the cerebellar neural circuit and how they are translated into behavioral outcomes in learning paradigms. Learning has to be inferred from the interaction of an embodied system with its real environment, and the same cerebellar principles derived from cell physiology have to be able to drive a variety of tasks of different nature, calling for complex timing and movement patterns. We have coupled a realistic cerebellar spiking neural network (SNN) with a real robot and challenged it in multiple diverse sensorimotor tasks. Encoding and decoding strategies based on neuronal firing rates were applied. Adaptive motor control protocols with acquisition and extinction phases have been designed and tested, including an associative Pavlovian task (Eye blinking classical conditioning), a vestibulo-ocular task and a perturbed arm reaching task operating in closed-loop. The SNN processed in real-time mossy fiber inputs as arbitrary contextual signals, irrespective of whether they conveyed a tone, a vestibular stimulus or the position of a limb. A bidirectional long-term plasticity rule implemented at parallel fibers-Purkinje cell synapses modulated the output activity in the deep cerebellar nuclei. In all tasks, the neurorobot learned to adjust timing and gain of the motor responses by tuning its output discharge. It succeeded in reproducing how human biological systems acquire, extinguish and express knowledge of a noisy and changing world. By varying stimuli and perturbations patterns, real-time control robustness and generalizability were validated. The implicit spiking dynamics of the cerebellar model fulfill timing, prediction and learning functions.

Memory trace and timing mechanism localized to cerebellar Purkinje cells

The standard view of the mechanisms underlying learning is that they involve strengthening or weakening synaptic connections. Learned response timing is thought to combine such plasticity with temporally patterned inputs to the neuron. We show here that a cerebellar Purkinje cell in a ferret can learn to respond to a specific input with a temporal pattern of activity consisting of temporally specific increases and decreases in firing over hundreds of milliseconds without a temporally patterned input. Training Purkinje cells with direct stimulation of immediate afferents, the parallel fibers, and pharmacological blocking of interneurons shows that the timing mechanism is intrinsic to the cell itself. Purkinje cells can learn to respond not only with increased or decreased firing but also with an adaptively timed activity pattern.

Coding of stimulus strength via analog calcium signals in Purkinje cell dendrites of awake mice

The climbing fiber input to Purkinje cells acts as a teaching signal by triggering a massive influx of dendritic calcium that marks the occurrence of instructive stimuli during cerebellar learning. Here, we challenge the view that these calcium spikes are all-or-none and only signal whether the instructive stimulus has occurred, without providing parametric information about its features. We imaged ensembles of Purkinje cell dendrites in awake mice and measured their calcium responses to periocular airpuffs that serve as instructive stimuli during cerebellar-dependent eyeblink conditioning. Information about airpuff duration and pressure was encoded probabilistically across repeated trials, and in two additional signals in single trials: the synchrony of calcium spikes in the Purkinje cell population, and the amplitude of the calcium spikes, which was modulated by a non-climbing fiber pathway. These results indicate that calcium-based teaching signals in Purkinje cells contain analog information that encodes the strength of instructive stimuli trial-by-trial.

DOI:http://dx.doi.org/10.7554/eLife.03663.001

A region of the brain known as the cerebellum plays a key role in learning how to anticipate an event. For example, if you know that a puff of air is going to be directed at your eye, it's a good idea to close it in advance. However, how much you need to close it depends on how strong that puff of air is. A very strong puff might require closing the eye completely to protect it. In contrast, it is probably better to only partially close the eye if you know a lighter puff of air is coming, so that you can still see.

Extensive research has focused on how neurons in and around the cerebellum work together to achieve this goal. When an event—such as a puff of air—occurs, signals are sent to large neurons in the cerebellum, called Purkinje cells, by ‘climbing fibers’. However, climbing fibers were thought to be able to respond in only two ways: either they fire in a single burst to signal that an event has occurred, or they don't fire. It was therefore unclear how the finer details of the event (for example, the strength of the puff of air) are transmitted to the cerebellum.

Najafi et al. imaged the level of calcium in the cerebellum of mice, as this indicates how active the neurons are. When a puff of air was directed at the eyes of the mice, Najafi et al. saw that the size of the response of the Purkinje cells corresponded with how big the puff of air was. Najafi et al. show that the size of this response, which is based mostly on input from the climbing fibers, is also influenced by input from an additional unknown source.

These findings show that Purkinje cells of the cerebellum receive detailed information about the nature of an event, such as a puff of air. What remains to be seen is whether the cerebellum uses this information to learn the correct response, that is how hard to blink to avoid the expected puff.

DOI:http://dx.doi.org/10.7554/eLife.03663.002

Changes in complex spike activity during classical conditioning

The cerebellar cortex is necessary for adaptively timed conditioned responses (CRs) in eyeblink conditioning. During conditioning, Purkinje cells acquire pause responses or "Purkinje cell CRs" to the conditioned stimuli (CS), resulting in disinhibition of the cerebellar nuclei (CN), allowing them to activate motor nuclei that control eyeblinks. This disinhibition also causes inhibition of the inferior olive (IO), via the nucleo-olivary pathway (N-O). Activation of the IO, which relays the unconditional stimulus (US) to the cortex, elicits characteristic complex spikes in Purkinje cells. Although Purkinje cell activity, as well as stimulation of the CN, is known to influence IO activity, much remains to be learned about the way that learned changes in simple spike firing affects the IO. In the present study, we analyzed changes in simple and complex spike firing, in extracellular Purkinje cell records, from the C3 zone, in decerebrate ferrets undergoing training in a conditioning paradigm. In agreement with the N-O feedback hypothesis, acquisition resulted in a gradual decrease in complex spike activity during the conditioned stimulus, with a delay that is consistent with the long N-O latency. Also supporting the feedback hypothesis, training with a short interstimulus interval (ISI), which does not lead to acquisition of a Purkinje cell CR, did not cause a suppression of complex spike activity. In contrast, observations that extinction did not lead to a recovery in complex spike activity and the irregular patterns of simple and complex spike activity after the conditioned stimulus are less conclusive.

Behavioral correlates of complex spike synchrony in cerebellar microzones

The olivo-cerebellar system is crucial for smooth and well timed execution of movements based on sensory and proprioceptive cues. The inferior olive (IO) plays a pivotal role in this process by synchronizing its activity across neurons internally through connexin36 gap junctions and providing a timing and/or learning signal to the cerebellum. Even though synchrony achieved through electrical coupling in IO cells is generally thought to be important in timing motor output, a direct relation between timing of movement and synchrony of olivary discharges has never been demonstrated within functional microcomplexes using transgenics. Here we combined in vivo, two-photon calcium imaging of complex spikes in microcomplexes of Purkinje cell (PC) dendrites with high-speed filming of tail, trunk, and limb movements in awake wild-type and connexin36-deficient mice. In wild types at rest, functional clusters of PCs were poorly defined with synchrony correlations that were relatively small and spatially limited to mediolateral distances of ∼50 μm, whereas during locomotion synchrony of the same PCs increased in strength and extended over distances spanning multiple microzones that could be correlated to specific components of sharp and well bounded movements. Instead, connexin36-deficient mice exhibited prolonged and desynchronized complex spike activity within PC microcomplexes both at rest and during behavior. Importantly, the mutants also showed concomitant abnormalities in the execution of spinocerebellar reflexes, which were significantly slower and more gradual than in wild-type littermates, particularly following sensory perturbations. Our results highlight the importance of modulation of synchronous activity within and between cerebellar microcomplexes in on-line temporal processing of motor output.

Purkinje-cell plasticity and cerebellar motor learning are graded by complex-spike duration

Behavioral learning is mediated by cellular plasticity such as changes in the strength of synapses at specific sites in neural circuits. The theory of cerebellar motor learning^1,2,3 relies on movement errors signaled by climbing-fiber inputs to cause long-term depression of synapses from parallel fibers to Purkinje cells^4,5. Yet, a recent review⁶ has called into question the widely-held view that the climbing fiber input is an “all-or-none” event. In anesthetized animals, there is wide variation in the duration of the complex-spike (CS) caused in Purkinje cells by a climbing fiber input⁷. Further, the duration of electrically-controlled bursts in climbing fibers grades the amount of plasticity in Purkinje cells^8,9. The duration of bursts depends on the “state” of the inferior olive and therefore could be correlated across climbing fibers^8,10. Here, we provide a potential functional context for these mechanisms during motor learning in behaving monkeys. The magnitudes of both plasticity and motor learning depend on the duration of the CS responses. Further, the duration of CS responses appears to be a meaningful signal that is correlated across the Purkinje cell population during motor learning. We suggest that during learning, longer bursts in climbing fibers lead to longer duration CS responses in Purkinje cells, more calcium entry into Purkinje cells, larger synaptic depression, and stronger learning. The same graded impact of instructive signals for plasticity and learning could occur throughout the nervous system.

Gating of neural error signals during motor learning

Cerebellar climbing fiber activity encodes performance errors during many motor learning tasks, but the role of these error signals in learning has been controversial. We compared two motor learning paradigms that elicited equally robust putative error signals in the same climbing fibers: learned increases and decreases in the gain of the vestibulo-ocular reflex (VOR). During VOR-increase training, climbing fiber activity on one trial predicted changes in cerebellar output on the next trial, and optogenetic activation of climbing fibers to mimic their encoding of performance errors was sufficient to implant a motor memory. In contrast, during VOR-decrease training, there was no trial-by-trial correlation between climbing fiber activity and changes in cerebellar output, and climbing fiber activation did not induce VOR-decrease learning. Our data suggest that the ability of climbing fibers to induce plasticity can be dynamically gated in vivo, even under conditions where climbing fibers are robustly activated by performance errors.

DOI:http://dx.doi.org/10.7554/eLife.02076.001

The cerebellum (or ‘little brain’) is located underneath the cerebral hemispheres. Despite comprising around 10% of the brain’s volume, the cerebellum contains roughly half of the brain’s neurons. Many of the functions of the cerebellum are related to the control and fine-tuning of movement, and people whose cerebellum has been damaged have problems with balance and coordination, and with learning new motor skills.

One of the roles of the cerebellum is to control a reflex known as the vestibulo-ocular reflex, which enables us to keep our gaze fixed on an object as we turn our heads. The cerebellum relays information about head movements to the muscles that control the eyes, instructing the eyes to move in the opposite direction to the head. This keeps the image of the object we are looking at stable on the retina.

The vestibulo-ocular reflex is controlled by a circuit that includes Purkinje cells (which are the main output cells of the cerebellum) and climbing fibres (which originate in the brainstem). Any failure of the vestibulo-ocular reflex to fully compensate for head movements generates an error signal that activates the climbing fibres. These in turn modify the output of Purkinje cells, leading ultimately to adjustments in eye movements.

However, Kimpo et al. have now obtained evidence that Purkinje cells can modulate their response to the instructions they receive from climbing fibres. Monkeys sat in a rotating chair while a visual object they were trained to track with their eyes was moved to induce errors in the vestibulo-ocular reflex. When the object was moved so that a bigger reflexive eye movement was required to stabilize the image, the activation of the climbing fibres in response to the error led to a change in the response of the Purkinje cells, as expected. However, when a smaller reflexive eye movement was needed, the error-driven responses of the climbing fibres did not alter the responses of Purkinje cells. Similar results were obtained using pulses of light to artificially activate climbing fibres and thus simulate error signals.

The work of Kimpo et al. indicates that the cerebellum does not blindly follow the instructions it receives from the brainstem, but can instead modulate its responses to incoming information about performance errors. Further work is now required to identify factors that influence the responsiveness of the cerebellum: such information could ultimately be used to improve learning of motor skills and recovery from injury.

DOI:http://dx.doi.org/10.7554/eLife.02076.002

Bidirectional plasticity of Purkinje cells matches temporal features of learning

Many forms of learning require temporally ordered stimuli. In Pavlovian eyeblink conditioning, a conditioned stimulus (CS) must precede the unconditioned stimulus (US) by at least about 100 ms for learning to occur. Conditioned responses are learned and generated by the cerebellum. Recordings from the cerebellar cortex during conditioning have revealed CS-triggered pauses in the firing of Purkinje cells that likely drive the conditioned blinks. The predominant view of the learning mechanism in conditioning is that long-term depression (LTD) at parallel fiber (PF)-Purkinje cell synapses underlies the Purkinje cell pauses. This raises a serious conceptual challenge because LTD is most effectively induced at short CS-US intervals, which do not support acquisition of eyeblinks. To resolve this discrepancy, we recorded Purkinje cells during conditioning with short or long CS-US intervals. Decerebrated ferrets trained with CS-US intervals ≥150 ms reliably developed Purkinje cell pauses, but training with an interval of 50 ms unexpectedly induced increases in CS-evoked spiking. This bidirectional modulation of Purkinje cell activity offers a basis for the requirement of a minimum CS-US interval for conditioning, but we argue that it cannot be fully explained by LTD, even when previous in vitro studies of stimulus-timing-dependent LTD are taken into account.

Modulation of electrotonic coupling in the inferior olive by inhibitory and excitatory inputs: integration in the glomerulus

Dendritic spines in glomeruli of the inferior olive are coupled by gap junctions and receive both inhibitory and excitatory inputs. In this issue of Neuron, Lefler et al. (2014), Mathy et al. (2014), and Turecek et al. (2014) provide new insight into how these inputs modulate electrical coupling and oscillatory activity.

Sensory-driven enhancement of calcium signals in individual Purkinje cell dendrites of awake mice

Climbing fibers (CFs) are thought to contribute to cerebellar plasticity and learning by triggering a large influx of dendritic calcium in the postsynaptic Purkinje cell (PC) to signal the occurrence of an unexpected sensory event. However, CFs fire about once per second whether or not an event occurs, raising the question of how sensory-driven signals might be distinguished from a background of ongoing spontaneous activity. Here, we report that in PC dendrites of awake mice, CF-triggered calcium signals are enhanced when the trigger is a sensory event. In addition, we show that a large fraction of the total enhancement in each PC dendrite can be accounted for by an additional boost of calcium provided by sensory activation of a non-CF input. We suggest that sensory stimulation may modulate dendritic voltage and calcium concentration in PCs to increase the strength of plasticity signals during cerebellar learning.

Decorrelation learning in the cerebellum: computational analysis and experimental questions

Many cerebellar models use a form of synaptic plasticity that implements decorrelation learning. Parallel fibers carrying signals positively correlated with climbing-fiber input have their synapses weakened (long-term depression), whereas those carrying signals negatively correlated with climbing input have their synapses strengthened (long-term potentiation). Learning therefore ceases when all parallel-fiber signals have been decorrelated from climbing-fiber input. This is a computationally powerful rule for supervised learning and can be cast in a spike-timing dependent plasticity form for comparison with experimental evidence. Decorrelation learning is particularly well suited to sensory prediction, for example, in the reafference problem where external sensory signals are interfered with by reafferent signals from the organism's own movements, and the required circuit appears similar to the one found to mediate classical eye blink conditioning. However, for certain stimuli, avoidance is a much better option than simple prediction, and decorrelation learning can also be used to acquire appropriate avoidance movements. One example of a stimulus to be avoided is retinal slip that degrades visual processing, and decorrelation learning appears to play a role in the vestibulo-ocular reflex that stabilizes gaze in the face of unpredicted head movements. Decorrelation learning is thus suitable for both sensory prediction and motor control. It may also be well suited for generic spatial and temporal coordination, because of its ability to remove the unwanted side effects of movement. Finally, because it can be used with any kind of time-varying signal, the cerebellum could play a role in cognitive processing.

Feedback control of learning by the cerebello-olivary pathway

The ability to anticipate future events and to modify erroneous anticipatory actions is crucial for the survival of any organism. Both theoretical and empirical lines of evidence implicate the cerebellum in this ability. It is often suggested that the cerebellum acquires "expectations" or "internal models." However, except in a metaphorical sense, the cerebellum, which consists of a set of interconnected nerve cells, cannot contain "internal models" or "have expectations." In this chapter, we try to untangle these metaphors by translating them back into neurophysiological cause and effect relationships. We approach this task from within the paradigm of classical conditioning, in which a subject, through repeated presentations of a conditional stimulus, followed by an unconditional stimulus, acquires a conditioned response. Importantly, the conditioned response is timed so that it anticipates the unconditioned response. Available neurophysiological evidence suggests that Purkinje cells, in the cerebellar cortex, generate the conditioned response. In addition, Purkinje cells provide negative feedback to the inferior olive, which is a relay for the unconditional stimulus, via the nucleo-olivary pathway. Purkinje cells can therefore regulate the intensity of the signal derived from the unconditional stimulus, which, in turn, decides subsequent plasticity. Hence, as learning progresses, the olivary signal will become weaker and weaker due to increasing negative feedback from Purkinje cells. Thus, in an important sense, learning-induced changes in Purkinje cell activity constitute an "expectation" or "anticipation" of a future event (the unconditional stimulus), and, consistent with theoretical models, future learning depends on the accuracy of this expectation.