Silent synapses, LTP, and the indirect parallel-fibre pathway: computational consequences of optimal cerebellar noise-processing

Adaptive Filter Model of Cerebellum for Biological Muscle Control With Spike Train Inputs

Prior applications of the cerebellar adaptive filter model have included a range of tasks within simulated and robotic systems. However, this has been limited to systems driven by continuous signals. Here, the adaptive filter model of the cerebellum is applied to the control of a system driven by spiking inputs by considering the problem of controlling muscle force. The performance of the standard adaptive filter algorithm is compared with the algorithm with a modified learning rule that minimizes inputs and a simple proportional-integral-derivative (PID) controller. Control performance is evaluated in terms of the number of spikes, the accuracy of spike input locations, and the accuracy of muscle force output. Results show that the cerebellar adaptive filter model can be applied without change to the control of systems driven by spiking inputs. The cerebellar algorithm results in good agreement between input spikes and force outputs and significantly improves on a PID controller. Input minimization can be used to reduce the number of spike inputs, but at the expense of a decrease in accuracy of spike input location and force output. This work extends the applications of the cerebellar algorithm and demonstrates the potential of the adaptive filter model to be used to improve functional electrical stimulation muscle control.

Dual STDP processes at Purkinje cells contribute to distinct improvements in accuracy and speed of saccadic eye movements

Saccadic eye-movements play a crucial role in visuo-motor control by allowing rapid foveation onto new targets. However, the neural processes governing saccades adaptation are not fully understood. Saccades, due to the short-time of execution (20-100 ms) and the absence of sensory information for online feedback control, must be controlled in a ballistic manner. Incomplete measurements of the movement trajectory, such as the visual endpoint error, are supposedly used to form internal predictions about the movement kinematics resulting in predictive control. In order to characterize the synaptic and neural circuit mechanisms underlying predictive saccadic control, we have reconstructed the saccadic system in a digital controller embedding a spiking neural network of the cerebellum with spike timing-dependent plasticity (STDP) rules driving parallel fiber-Purkinje cell long-term potentiation and depression (LTP and LTD). This model implements a control policy based on a dual plasticity mechanism, resulting in the identification of the roles of LTP and LTD in regulating the overall quality of saccade kinematics: it turns out that LTD increases the accuracy by decreasing visual error and LTP increases the peak speed. The control policy also required cerebellar PCs to be divided into two subpopulations, characterized by burst or pause responses. To our knowledge, this is the first model that explains in mechanistic terms the visual error and peak speed regulation of ballistic eye movements in forward mode exploiting spike-timing to regulate firing in different populations of the neuronal network. This elementary model of saccades could be extended and applied to other more complex cases in which single jerks are concatenated to compose articulated and coordinated movements.

A multizone cerebellar chip for bioinspired adaptive robot control and sensorimotor processing

The cerebellum is a neural structure essential for learning, which is connected via multiple zones to many different regions of the brain, and is thought to improve human performance in a large range of sensory, motor and even cognitive processing tasks. An intriguing possibility for the control of complex robotic systems would be to develop an artificial cerebellar chip with multiple zones that could be similarly connected to a variety of subsystems to optimize performance. The novel aim of this paper, therefore, is to propose and investigate a multizone cerebellar chip applied to a range of tasks in robot adaptive control and sensorimotor processing. The multizone cerebellar chip was evaluated using a custom robotic platform consisting of an array of tactile sensors driven by dielectric electroactive polymers mounted upon a standard industrial robot arm. The results demonstrate that the performance in each task was improved by the concurrent, stable learning in each cerebellar zone. This paper, therefore, provides the first empirical demonstration that a synthetic, multizone, cerebellar chip could be embodied within existing robotic systems to improve performance in a diverse range of tasks, much like the cerebellum in a biological system.

Cerebellar adaptive mechanisms explain the optimal control of saccadic eye movements

Cerebellar synaptic plasticity is vital for adaptability and fine tuning of goal-directed movements. The perceived sensory errors between desired and actual movement outcomes are commonly considered to induce plasticity in the cerebellar synapses, with an objective to improve desirability of the executed movements. In rapid goal-directed eye movements called saccades, the only available sensory feedback is the direction of reaching error information received only at end of the movement. Moreover, this sensory error dependent plasticity can only improve the accuracy of the movements, while ignoring other essential characteristics such as reaching in minimum-time. In this work we propose a rate based, cerebellum inspired adaptive filter model to address refinement of both accuracy and movement-time of saccades. We use optimal control approach in conjunction with information constraints posed by the cerebellum to derive bio-plausible supervised plasticity rules. We implement and validate this bio-inspired scheme on a humanoid robot. We found out that, separate plasticity mechanisms in the model cerebellum separately control accuracy and movement-time. These plasticity mechanisms ensure that optimal saccades are produced by just receiving the direction of end reaching error as an evaluative signal. Furthermore, the model emulates encoding in the cerebellum of movement kinematics as observed in biological experiments.

Sensorimotor maps can be dynamically calibrated using an adaptive-filter model of the cerebellum

Substantial experimental evidence suggests the cerebellum is involved in calibrating sensorimotor maps. Consistent with this involvement is the well-known, but little understood, massive cerebellar projection to maps in the superior colliculus. Map calibration would be a significant new role for the cerebellum given the ubiquity of map representations in the brain, but how it could perform such a task is unclear. Here we investigated a dynamic method for map calibration, based on electrophysiological recordings from the superior colliculus, that used a standard adaptive-filter cerebellar model. The method proved effective for complex distortions of both unimodal and bimodal maps, and also for predictive map-based tracking of moving targets. These results provide the first computational evidence for a novel role for the cerebellum in dynamic sensorimotor map calibration, of potential importance for coordinate alignment during ongoing motor control, and for map calibration in future biomimetic systems. This computational evidence also provides testable experimental predictions concerning the role of the connections between cerebellum and superior colliculus in previously observed dynamic coordinate transformations.

The human brain contains a structure known as the cerebellum, which contains a vast number of neurons–around 80% of the total ~90 billion. We believe the cerebellum is involved in learning motor skills, and so is vitally important for accurately controlling the movements of our body, amongst other things. However, like most regions of the brain, we still do not fully understand the role of the cerebellum and evidence for new roles is appearing all the time. One such new role is in the calibration of sensorimotor maps in the brain that link our sensory perception to motor function, such as when a visual stimulus causes a redirect of our gaze. We investigated this problem by connecting a mathematical model of the cerebellar cortical microcircuit to simulated sensory maps in the superior colliculus that are used to control orienting movements. We found the error signal generated by inaccurate orienting movements could be used to accurately calibrate sensorimotor maps, and to allow predictive tracking of moving targets. This finding points to a potentially widespread role for the cerebellum in calibrating the sensorimotor maps that are ubiquitous in the brain and could prove useful in controlling the movements of multi-joint robots.

Early Cerebellar Network Shifting in Spinocerebellar Ataxia Type 6

Spinocerebellar ataxia 6 (SCA6), an autosomal dominant degenerative disease, is characterized by diplopia, gait ataxia, and incoordination due to severe progressive degeneration of Purkinje cells in the vestibulo- and spinocerebellum. Ocular motor deficits are common, including difficulty fixating on moving objects, nystagmus and disruption of smooth pursuit movements. In presymptomatic SCA6, there are alterations in saccades and smooth-pursuit movements. We sought to assess functional and structural changes in cerebellar connectivity associated with a visual task, hypothesizing that gradual changes would parallel disease progression. We acquired functional magnetic resonance imaging and diffusion tensor imaging data during a passive smooth-pursuit task in 14 SCA6 patients, representing a range of disease duration and severity, and performed a cross-sectional comparison of cerebellar networks compared with healthy controls. We identified a shift in activation from vermis in presymptomatic individuals to lateral cerebellum in moderate-to-severe cases. Concomitantly, effective connectivity between regions of cerebral cortex and cerebellum was at its highest in moderate cases, and disappeared in severe cases. Finally, we noted structural differences in the cerebral and cerebellar peduncles. These unique results, spanning both functional and structural domains, highlight widespread changes in SCA6 and compensatory mechanisms associated with cerebellar physiology that could be utilized in developing new therapies.

Fluorescent false neurotransmitter reveals functionally silent dopamine vesicle clusters in the striatum

Neurotransmission at dopaminergic synapses has been studied with techniques that provide high temporal resolution, but cannot resolve individual synapses. To elucidate the spatial dynamics and heterogeneity of individual dopamine boutons, we developed fluorescent false neurotransmitter 200 (FFN200), a vesicular monoamine transporter 2 (VMAT2) substrate that selectively traces monoamine exocytosis in both neuronal cell culture and brain tissue. By monitoring electrically evoked Ca(2+) transients with GCaMP3 and FFN200 release simultaneously, we found that only a small fraction of dopamine boutons that exhibited Ca(2+) influx engaged in exocytosis, a result confirmed with activity-dependent loading of the endocytic probe FM1-43. Thus, only a low fraction of striatal dopamine axonal sites with uptake-competent VMAT2 vesicles are capable of transmitter release. This is consistent with the presence of functionally 'silent' dopamine vesicle clusters and represents, to the best of our knowledge, the first report suggestive of presynaptically silent neuromodulatory synapses.

At the Edge of Chaos: How Cerebellar Granular Layer Network Dynamics Can Provide the Basis for Temporal Filters

Models of the cerebellar microcircuit often assume that input signals from the mossy-fibers are expanded and recoded to provide a foundation from which the Purkinje cells can synthesize output filters to implement specific input-signal transformations. Details of this process are however unclear. While previous work has shown that recurrent granule cell inhibition could in principle generate a wide variety of random outputs suitable for coding signal onsets, the more general application for temporally varying signals has yet to be demonstrated. Here we show for the first time that using a mechanism very similar to reservoir computing enables random neuronal networks in the granule cell layer to provide the necessary signal separation and extension from which Purkinje cells could construct basis filters of various time-constants. The main requirement for this is that the network operates in a state of criticality close to the edge of random chaotic behavior. We further show that the lack of recurrent excitation in the granular layer as commonly required in traditional reservoir networks can be circumvented by considering other inherent granular layer features such as inverted input signals or mGluR2 inhibition of Golgi cells. Other properties that facilitate filter construction are direct mossy fiber excitation of Golgi cells, variability of synaptic weights or input signals and output-feedback via the nucleocortical pathway. Our findings are well supported by previous experimental and theoretical work and will help to bridge the gap between system-level models and detailed models of the granular layer network.

Plasticity of cerebellar Purkinje cells in behavioral training of body balance control

Neural responses to sensory inputs caused by self-generated movements (reafference) and external passive stimulation (exafference) differ in various brain regions. The ability to differentiate such sensory information can lead to movement execution with better accuracy. However, how sensory responses are adjusted in regard to this distinguishability during motor learning is still poorly understood. The cerebellum has been hypothesized to analyze the functional significance of sensory information during motor learning, and is thought to be a key region of reafference computation in the vestibular system. In this study, we investigated Purkinje cell (PC) spike trains as cerebellar cortical output when rats learned to balance on a suspended dowel. Rats progressively reduced the amplitude of body swing and made fewer foot slips during a 5-min balancing task. Both PC simple (SSs; 17 of 26) and complex spikes (CSs; 7 of 12) were found to code initially on the angle of the heads with respect to a fixed reference. Using periods with comparable degrees of movement, we found that such SS coding of information in most PCs (10 of 17) decreased rapidly during balance learning. In response to unexpected perturbations and under anesthesia, SS coding capability of these PCs recovered. By plotting SS and CS firing frequencies over 15-s time windows in double-logarithmic plots, a negative correlation between SS and CS was found in awake, but not anesthetized, rats. PCs with prominent SS coding attenuation during motor learning showed weaker SS-CS correlation. Hence, we demonstrate that neural plasticity for filtering out sensory reafference from active motion occurs in the cerebellar cortex in rats during balance learning. SS-CS interaction may contribute to this rapid plasticity as a form of receptive field plasticity in the cerebellar cortex between two receptive maps of sensory inputs from the external world and of efference copies from the will center for volitional movements.

Questioning the role of sparse coding in the brain

Coding principles are central to understanding the organization of brain circuitry. Sparse coding offers several advantages, but a near-consensus has developed that it only has beneficial properties, and these are partially unique to sparse coding. We find that these advantages come at the cost of several trade-offs, with the lower capacity for generalization being especially problematic, and the value of sparse coding as a measure and its experimental support are both questionable. Furthermore, silent synapses and inhibitory interneurons can permit learning speed and memory capacity that was previously ascribed to sparse coding only. Combining these properties without exaggerated sparse coding improves the capacity for generalization and facilitates learning of models of a complex and high-dimensional reality.

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.

Reevaluation of the role of parallel fiber synapses in delay eyeblink conditioning in mice using Cbln1 as a tool

The delay eyeblink conditioning (EBC) is a cerebellum-dependent type of associative motor learning. However, the exact roles played by the various cerebellar synapses, as well as the underlying molecular mechanisms, remain to be determined. It is also unclear whether long-term potentiation (LTP) or long-term depression (LTD) at parallel fiber (PF)–Purkinje cell (PC) synapses is involved in EBC. In this study, to clarify the role of PF synapses in the delay EBC, we used mice in which a gene encoding Cbln1 was disrupted (cbln1^-/- mice), which display severe reduction of PF–PC synapses. We showed that delay EBC was impaired in cbln1^-/- mice. Although PF-LTD was impaired, PF-LTP was normally induced in cbln1^-/- mice. A single recombinant Cbln1 injection to the cerebellar cortex in vivo completely, though transiently, restored the morphology and function of PF–PC synapses and delay EBC in cbln1^-/- mice. Interestingly, the cbln1^-/- mice retained the memory for at least 30 days, after the Cbln1 injection’s effect on PF synapses had abated. Furthermore, delay EBC memory could be extinguished even after the Cbln1 injection’s effect were lost. These results indicate that intact PF–PC synapses and PF-LTD, not PF-LTP, are necessary to acquire delay EBC in mice. In contrast, extracerebellar structures or remaining PF–PC synapses in cbln1^-/- mice may be sufficient for the expression, maintenance, and extinction of its memory trace.

An adaptive filter model of cerebellar zone C3 as a basis for safe limb control?

The review asks how the adaptive filter model of the cerebellum might be relevant to experimental work on zone C3, one of the most extensively studied regions of cerebellar cortex. As far as features of the cerebellar microcircuit are concerned, the model appears to fit very well with electrophysiological discoveries concerning the importance of molecular layer interneurons and their plasticity, the significance of long-term potentiation and the striking number of silent parallel fibre synapses. Regarding external connectivity and functionality, a key feature of the adaptive filter model is its use of the decorrelation algorithm, which renders it uniquely suited to problems of sensory noise cancellation. However, this capacity can be extended to the avoidance of sensory interference, by appropriate movements of, for example, the eyes in the vestibulo-ocular reflex. Avoidance becomes particularly important when painful signals are involved, and as the climbing fibre input to zone C3 is extremely responsive to nociceptive stimuli, it is proposed that one function of this zone is the avoidance of pain by, for example, adjusting movements of the body to avoid self-harm. This hypothesis appears consistent with evidence from humans and animals concerning the role of the intermediate cerebellum in classically conditioned withdrawal reflexes, but further experiments focusing on conditioned avoidance are required to test the hypothesis more stringently. The proposed architecture may also be useful for automatic self-adjusting damage avoidance in robots, an important consideration for next generation 'soft' robots designed to interact with people.

Processing of multi-dimensional sensorimotor information in the spinal and cerebellar neuronal circuitry: a new hypothesis

Why are sensory signals and motor command signals combined in the neurons of origin of the spinocerebellar pathways and why are the granule cells that receive this input thresholded with respect to their spike output? In this paper, we synthesize a number of findings into a new hypothesis for how the spinocerebellar systems and the cerebellar cortex can interact to support coordination of our multi-segmented limbs and bodies. A central idea is that recombination of the signals available to the spinocerebellar neurons can be used to approximate a wide array of functions including the spatial and temporal dependencies between limb segments, i.e. information that is necessary in order to achieve coordination. We find that random recombination of sensory and motor signals is not a good strategy since, surprisingly, the number of granule cells severely limits the number of recombinations that can be represented within the cerebellum. Instead, we propose that the spinal circuitry provides useful recombinations, which can be described as linear projections through aspects of the multi-dimensional sensorimotor input space. Granule cells, potentially with the aid of differentiated thresholding from Golgi cells, enhance the utility of these projections by allowing the Purkinje cell to establish piecewise-linear approximations of non-linear functions. Our hypothesis provides a novel view on the function of the spinal circuitry and cerebellar granule layer, illustrating how the coordinating functions of the cerebellum can be crucially supported by the recombinations performed by the neurons of the spinocerebellar systems.

Evaluating the adaptive-filter model of the cerebellum

The adaptive-filter model of the cerebellar microcircuit is in widespread use, combining as it does an explanation of key microcircuit features with well-specified computational power. Here we consider two methods for its evaluation. One is to test its predictions concerning relations between cerebellar inputs and outputs. Where the relevant experimental data are available, e.g. for the floccular role in image stabilization, the predictions appear to be upheld. However, for the majority of cerebellar microzones these data have yet to be obtained. The second method is to test model predictions about details of the microcircuit. We focus on features apparently incompatible with the model, in particular non-linear patterns in Purkinje cell simple-spike firing. Analysis of these patterns suggests the following three conclusions. (i) It is important to establish whether they can be observed during task-related behaviour. (ii) Highly non-linear models based on these patterns are unlikely to be universal, because they would be incompatible with the (approximately) linear nature of floccular function. (iii) The control tasks for which these models are computationally suited need to be identified. At present, therefore, the adaptive filter remains a candidate model of at least some cerebellar microzones, and its evaluation suggests promising lines for future enquiry.

Functional implications of tactile projection patterns to the lateral hemispheres of the cerebellum of the albino rat: the legacy of Wally Welker

In the late 1970s, Wally Welker and his colleagues published a series of papers describing the first high-resolution physiological maps of tactile mossy fiber projections to the granule cell layer of the rat. Over the subsequent decade, his laboratory continued to explore the implications of these results for cerebellar connectivity and function while also extending the basic mapping results to a number of additional mammalian species. In each case, the maps revealed several surprising features, including a dominance of tactile (cutaneous inputs), robust short latency responses from the sensory periphery, and a fractured patchy somatotopic organization of receptive fields. This paper summarizes the major results of these micromapping experiments and reconsiders their implications for cerebellar function in light of more recent experimental data. The paper also explores the relationship between these fundamental discoveries and Wally Welker's theory-neutral approach to experimental science.

Activation of silent and weak synapses by cAMP-dependent protein kinase in cultured cerebellar granule neurons

Presynaptic long term potentiation of synaptic transmission activates silent synapses and potentiates existing active synapses. We sought to visualise these two processes by studying the cAMP-dependent protein kinase (PKA) potentiation of presynaptic vesicle cycling in cultured cerebellar granule neurons.Using FM dyes to label the pool of recycling synaptic vesicles,we found that trains of electrical stimulation which do not potentiate already active synapses are sufficient to rapidly activate a discrete population comprising silent and very low activity synapses. Silent synapse activation required PKA activity and conversely, active synapses could be silenced by PKA inhibition. Surprisingly, the recycling pool of synaptic vesicles in recently activated synapses was larger than in already active synapses and equivalent to synapses treated with forskolin. Imaging of synaptic vesicle cycling and cytosolic Ca(2+) in individual nerve terminals confirmed that silent synapses have evoked Ca(2+) transients comparable to those of active synapses. Furthermore, across populations of active synapses, changes in Ca(2+) influx did not correlate with changes in the size of the pool of recycling synaptic vesicles. Finally, we found that stimulation of synapsin phosphorylation, but not RIM1α, by PKA was frequency dependent and long lasting. These data are consistent with the idea that PKA regulates synaptic vesicle recycling downstream of Ca(2+) influx and that this pathway is highly active in recently activated synapses.

Purkinje cell-specific knockout of the protein phosphatase PP2B impairs potentiation and cerebellar motor learning

Cerebellar motor learning is required to obtain procedural skills. Studies have provided supportive evidence for a potential role of kinase-mediated long-term depression (LTD) at the parallel fiber to Purkinje cell synapse in cerebellar learning. Recently, phosphatases have been implicated in the induction of potentiation of Purkinje cell activities in vitro, but it remains to be shown whether and how phosphatase-mediated potentiation contributes to motor learning. Here, we investigated its possible role by creating and testing a Purkinje cell-specific knockout of calcium/calmodulin-activated protein-phosphatase-2B (L7-PP2B). The selective deletion of PP2B indeed abolished postsynaptic long-term potentiation in Purkinje cells and their ability to increase their excitability, whereas LTD was unaffected. The mutants showed impaired "gain-decrease" and "gain-increase" adaptation of their vestibulo-ocular reflex (VOR) as well as impaired acquisition of classical delay conditioning of their eyeblink response. Thus, our data indicate that PP2B may indeed mediate potentiation in Purkinje cells and contribute prominently to cerebellar motor learning.

Memory consolidation in the cerebellar cortex

Several forms of learning, including classical conditioning of the eyeblink, depend upon the cerebellum. In examining mechanisms of eyeblink conditioning in rabbits, reversible inactivations of the control circuitry have begun to dissociate aspects of cerebellar cortical and nuclear function in memory consolidation. It was previously shown that post-training cerebellar cortical, but not nuclear, inactivations with the GABA_A agonist muscimol prevented consolidation but these findings left open the question as to how final memory storage was partitioned across cortical and nuclear levels. Memory consolidation might be essentially cortical and directly disturbed by actions of the muscimol, or it might be nuclear, and sensitive to the raised excitability of the nuclear neurons following the loss of cortical inhibition. To resolve this question, we simultaneously inactivated cerebellar cortical lobule HVI and the anterior interpositus nucleus of rabbits during the post-training period, so protecting the nuclei from disinhibitory effects of cortical inactivation. Consolidation was impaired by these simultaneous inactivations. Because direct application of muscimol to the nuclei alone has no impact upon consolidation, we can conclude that post-training, consolidation processes and memory storage for eyeblink conditioning have critical cerebellar cortical components. The findings are consistent with a recent model that suggests the distribution of learning-related plasticity across cortical and nuclear levels is task-dependent. There can be transfer to nuclear or brainstem levels for control of high-frequency responses but learning with lower frequency response components, such as in eyeblink conditioning, remains mainly dependent upon cortical memory storage.

The energy use associated with neural computation in the cerebellum

The brain's energy supply determines its information processing power, and generates functional imaging signals, which are often assumed to reflect principal neuron spiking. Using measured cellular properties, we analysed how energy expenditure relates to neural computation in the cerebellar cortex. Most energy is used on information processing by non-principal neurons: Purkinje cells use only 18% of the signalling energy. Excitatory neurons use 73% and inhibitory neurons 27% of the energy. Despite markedly different computational architectures, the granular and molecular layers consume approximately the same energy. The blood vessel area supplying glucose and O(2) is spatially matched to energy consumption. The energy cost of storing motor information in the cerebellum was also estimated.

Adaptive-filter models of the cerebellum: computational analysis

Many current models of the cerebellar cortical microcircuit are equivalent to an adaptive filter using the covariance learning rule. The adaptive filter is a development of the original Marr-Albus framework that deals naturally with continuous time-varying signals, thus addressing the issue of 'timing' in cerebellar function, and it can be connected in a variety of ways to other parts of the system, consistent with the microzonal organization of cerebellar cortex. However, its computational capacities are not well understood. Here we summarise the results of recent work that has focused on two of its intrinsic properties. First, an adaptive filter seeks to decorrelate its (mossy fibre) inputs from a (climbing fibre) teaching signal. This procedure can be used both for sensory processing, e.g. removal of interference from sensory signals, and for learning accurate motor commands, by decorrelating an efference copy of those commands from a sensory signal of inaccuracy. As a model of the cerebellum the adaptive filter thus forms a natural link between events at the cellular level, such as forms of synaptic plasticity and the learning rules they embody, and intelligent behaviour at the system level. Secondly, it has been shown that the covariance learning rule enables the filter to handle input and intrinsic noise optimally. Such optimality may underlie the recently described role of the cerebellum in producing accurate smooth pursuit eye movements in the face of sensory noise. Moreover, it has the consequence of driving most input weights to very small values, consistent with experimental data that many parallel-fibre synapses are normally silent. The effectiveness of silent synapses can only be altered by LTP, so learning tasks depending on a reduction of Purkinje cell firing require the synapses to be embedded in a second, inhibitory pathway from parallel fibre to Purkinje cell. This pathway and the appropriate climbing-fibre related plasticity have been described experimentally, and its presence has implications for asymmetries and hysteresis in behavioural learning rates that are also consistent with experimental observations. These computational properties of the adaptive filter suggest that it is both powerful and realistic enough to be a suitable candidate model of the cerebellar cortical microcircuit.

Mechanisms of human cerebellar dysmetria: experimental evidence and current conceptual bases

The human cerebellum contains more neurons than any other region in the brain and is a major actor in motor control. Cerebellar circuitry is unique by its stereotyped architecture and its modular organization. Understanding the motor codes underlying the organization of limb movement and the rules of signal processing applied by the cerebellar circuits remains a major challenge for the forthcoming decades. One of the cardinal deficits observed in cerebellar patients is dysmetria, designating the inability to perform accurate movements. Patients overshoot (hypermetria) or undershoot (hypometria) the aimed target during voluntary goal-directed tasks. The mechanisms of cerebellar dysmetria are reviewed, with an emphasis on the roles of cerebellar pathways in controlling fundamental aspects of movement control such as anticipation, timing of motor commands, sensorimotor synchronization, maintenance of sensorimotor associations and tuning of the magnitudes of muscle activities. An overview of recent advances in our understanding of the contribution of cerebellar circuitry in the elaboration and shaping of motor commands is provided, with a discussion on the relevant anatomy, the results of the neurophysiological studies, and the computational models which have been proposed to approach cerebellar function.