Neural learning rules for the vestibulo-ocular reflex

Synaptic weight dynamics underlying memory consolidation: implications for learning rules, circuit organization, and circuit function

Systems consolidation is a common feature of learning and memory systems, in which a long-term memory initially stored in one brain region becomes persistently stored in another region. We studied the dynamics of systems consolidation in simple circuit architectures with two sites of plasticity, one in an early-learning and one in a late-learning brain area. We show that the synaptic dynamics of the circuit during consolidation of an analog memory can be understood as a temporal integration process, by which transient changes in activity driven by plasticity in the early-learning area are accumulated into persistent synaptic changes at the late-learning site. This simple principle naturally leads to a speed-accuracy tradeoff in systems consolidation and provides insight into how the circuit mitigates the stability-plasticity dilemma of storing new memories while preserving core features of older ones. Furthermore, it imposes two constraints on the circuit. First, the plasticity rule at the late-learning site must stably support a continuum of possible outputs for a given input. We show that this is readily achieved by heterosynaptic but not standard Hebbian rules. Second, to turn off the consolidation process and prevent erroneous changes at the late-learning site, neural activity in the early-learning area must be reset to its baseline activity. We propose two biologically plausible implementations for this reset that suggest novel roles for core elements of the cerebellar circuit.

Significance Statement

How are memories transformed over time? We propose a simple organizing principle for how long term memories are moved from an initial to a final site of storage. We show that successful transfer occurs when the late site of memory storage is endowed with synaptic plasticity rules that stably accumulate changes in activity occurring at the early site of memory storage. We instantiate this principle in a simple computational model that is representative of brain circuits underlying a variety of behaviors. The model suggests how a neural circuit can store new memories while preserving core features of older ones, and suggests novel roles for core elements of the cerebellar circuit.

Climbing fibers provide essential instructive signals for associative learning

Supervised learning depends on instructive signals that shape the output of neural circuits to support learned changes in behavior. Climbing fiber (CF) inputs to the cerebellar cortex represent one of the strongest candidates in the vertebrate brain for conveying neural instructive signals. However, recent studies have shown that Purkinje cell stimulation can also drive cerebellar learning and the relative importance of these two neuron types in providing instructive signals for cerebellum-dependent behaviors remains unresolved. In the present study we used cell-type-specific perturbations of various cerebellar circuit elements to systematically evaluate their contributions to delay eyeblink conditioning in mice. Our findings reveal that, although optogenetic stimulation of either CFs or Purkinje cells can drive learning under some conditions, even subtle reductions in CF signaling completely block learning to natural stimuli. We conclude that CFs and corresponding Purkinje cell complex spike events provide essential instructive signals for associative cerebellar learning.

Interactions between circuit architecture and plasticity in a closed-loop cerebellar system

Determining the sites and directions of plasticity underlying changes in neural activity and behavior is critical for understanding mechanisms of learning. Identifying such plasticity from neural recording data can be challenging due to feedback pathways that impede reasoning about cause and effect. We studied interactions between feedback, neural activity, and plasticity in the context of a closed-loop motor learning task for which there is disagreement about the loci and directions of plasticity: vestibulo-ocular reflex learning. We constructed a set of circuit models that differed in the strength of their recurrent feedback, from no feedback to very strong feedback. Despite these differences, each model successfully fit a large set of neural and behavioral data. However, the patterns of plasticity predicted by the models fundamentally differed, with the direction of plasticity at a key site changing from depression to potentiation as feedback strength increased. Guided by our analysis, we suggest how such models can be experimentally disambiguated. Our results address a long-standing debate regarding cerebellum-dependent motor learning, suggesting a reconciliation in which learning-related changes in the strength of synaptic inputs to Purkinje cells are compatible with seemingly oppositely directed changes in Purkinje cell spiking activity. More broadly, these results demonstrate how changes in neural activity over learning can appear to contradict the sign of the underlying plasticity when either internal feedback or feedback through the environment is present.

A system of feed-forward cerebellar circuits that extend and diversify sensory signaling

Sensory signals are processed by the cerebellum to coordinate movements. Numerous cerebellar functions are thought to require the maintenance of a sensory representation that extends beyond the input signal. Granule cells receive sensory input, but they do not prolong the signal and are thus unlikely to maintain a sensory representation for much longer than the inputs themselves. Unipolar brush cells (UBCs) are excitatory interneurons that project to granule cells and transform sensory input into prolonged increases or decreases in firing, depending on their ON or OFF UBC subtype. Further extension and diversification of the input signal could be produced by UBCs that project to one another, but whether this circuitry exists is unclear. Here we test whether UBCs innervate one another and explore how these small networks of UBCs could transform spiking patterns. We characterized two transgenic mouse lines electrophysiologically and immunohistochemically to confirm that they label ON and OFF UBC subtypes and crossed them together, revealing that ON and OFF UBCs innervate one another. A Brainbow reporter was used to label UBCs of the same ON or OFF subtype with different fluorescent proteins, which showed that UBCs innervate their own subtypes as well. Computational models predict that these feed-forward networks of UBCs extend the length of bursts or pauses and introduce delays-transformations that may be necessary for cerebellar functions from modulation of eye movements to adaptive learning across time scales.

Prediction error drives associative learning and conditioned behavior in a spiking model of Drosophila larva

Predicting reinforcement from sensory cues is beneficial for goal-directed behavior. In insect brains, underlying associations between cues and reinforcement, encoded by dopaminergic neurons, are formed in the mushroom body. We propose a spiking model of the Drosophila larva mushroom body. It includes a feedback motif conveying learned reinforcement expectation to dopaminergic neurons, which can compute prediction error as the difference between expected and present reinforcement. We demonstrate that this can serve as a driving force in learning. When combined with synaptic homeostasis, our model accounts for theoretically derived features of acquisition and loss of associations that depend on the intensity of the reinforcement and its temporal proximity to the cue. From modeling olfactory learning over the time course of behavioral experiments and simulating the locomotion of individual larvae toward or away from odor sources in a virtual environment, we conclude that learning driven by prediction errors can explain larval behavior.

A system of feed-forward cerebellar circuits that extend and diversify sensory signaling

Sensory signals are processed by the cerebellum to coordinate movements. Numerous cerebellar functions are thought to require the maintenance of a sensory representation that extends beyond the input signal. Granule cells receive sensory input, but they do not prolong the signal and are thus unlikely to maintain a sensory representation for much longer than the inputs themselves. Unipolar brush cells (UBCs) are excitatory interneurons that project to granule cells and transform sensory input into prolonged increases or decreases in firing, depending on their ON or OFF UBC subtype. Further extension and diversification of the input signal could be produced by UBCs that project to one another, but whether this circuitry exists is unclear. Here we test whether UBCs innervate one another and explore how these small networks of UBCs could transform spiking patterns. We characterized two transgenic mouse lines electrophysiologically and immunohistochemically to confirm that they label ON and OFF UBC subtypes and crossed them together, revealing that ON and OFF UBCs innervate one another. A Brainbow reporter was used to label UBCs of the same ON or OFF subtype with different fluorescent proteins, which showed that UBCs innervate their own subtypes as well. Computational models predict that these feed-forward networks of UBCs extend the length of bursts or pauses and introduce delays-transformations that may be necessary for cerebellar functions from modulation of eye movements to adaptive learning across time scales.

Etomidate Depresses Spontaneous Complex Spikes Activity of Cerebellar Purkinje Cells via Cannabinoid 1 Receptor in vivo in Mice

INTRODUCTION

Complex spikes (CSs) activity of cerebellar Purkinje cells plays critical roles in motor coordination and motor learning by transferring information to cerebellar cortex, which is an accessible and useful model for neurophysiological investigation. Etomidate is an ultrashort-acting nonbarbiturate intravenous anesthetic, which inhibits the spontaneous activity of cerebellar Purkinje cells through activation of GABAA and glycine receptors in vivo in mice. However, the effect of etomidate on the spontaneous CSs activity of cerebellar Purkinje cells in living mouse is not clear.

METHODS

We here investigated the effects of etomidate on spontaneous CSs activity of cerebellar Purkinje cell in urethane-anesthetized mice by electrophysiology recording technique and pharmacological methods.

RESULTS

Our results showed that cerebellar surface perfusion of etomidate significantly depressed the activity of spontaneous CSs, which exhibited decreases in the number of spikelets and the area under curve (AUC) of the CSs. The etomidate-produced inhibition of CSs activity was persisted in the presence of GABAA and glycine receptors antagonists. However, application of cannabinoid 1 (CB1) receptor antagonist, AM-251, completely blocked the etomidate-induced inhibition of CSs. Furthermore, application of the CB1 receptor agonist, WIN55212-2, induced a decrease of CSs. Moreover, in the presence of a specific protein kinase A (PKA) inhibitor, KT5720, etomidate failed to produce decreases in the spikelets number and the AUC of the spontaneous CSs.

CONCLUSION

These results indicate that cerebellar surface application of etomidate facilitates CB1 receptor activity resulting in a depression of spontaneous CSs activity of Purkinje cells via PKA signaling pathway in mouse cerebellar cortex. Our present results suggest that the etomidate administration may impair the function of cerebellar cortical neuronal circuitry by inhibition of the climbing fiber - Purkinje cells synaptic transmission through activation of CB1 receptors in vivo in mice.

Molecular layer disinhibition unlocks climbing-fiber-instructed motor learning in the cerebellum

Climbing fibers supervise cerebellar learning by providing signals to Purkinje cells (PCs) that instruct adaptive changes to mistakenly performed movements. Yet, climbing fibers are regularly active, even during well performed movements, suggesting that a mechanism dynamically regulates the ability of climbing fibers to induce corrective plasticity in response to motor errors. We found that molecular layer interneurons (MLIs), whose inhibition of PCs powerfully opposes climbing-fiber-mediated excitation, serve this function. Optogenetically suppressing the activity of floccular MLIs in mice during the vestibulo-ocular reflex (VOR) induces a learned increase in gain despite the absence of performance errors. Suppressing MLIs when the VOR is mistakenly underperformed reveled that their inhibitory output is necessary to orchestrate gain-increase learning by conditionally permitting climbing fibers to instruct plasticity induction during ipsiversive head turns. Ablation of an MLI circuit for PC disinhibition prevents gain-increase learning during VOR performance errors which was rescued by re-imposing PC disinhibition through MLI activity suppression. Our findings point to a decisive role for MLIs in gating climbing-fiber-mediated learning through their context-dependent inhibition of PCs.

An emergent temporal basis set robustly supports cerebellar time-series learning

The cerebellum is considered a "learning machine" essential for time interval estimation underlying motor coordination and other behaviors. Theoretical work has proposed that the cerebellum's input recipient structure, the granule cell layer (GCL), performs pattern separation of inputs that facilitates learning in Purkinje cells (P-cells). However, the relationship between input reformatting and learning has remained debated, with roles emphasized for pattern separation features from sparsification to decorrelation. We took a novel approach by training a minimalist model of the cerebellar cortex to learn complex time-series data from time-varying inputs, typical during movements. The model robustly produced temporal basis sets from these inputs, and the resultant GCL output supported better learning of temporally complex target functions than mossy fibers alone. Learning was optimized at intermediate threshold levels, supporting relatively dense granule cell activity, yet the key statistical features in GCL population activity that drove learning differed from those seen previously for classification tasks. These findings advance testable hypotheses for mechanisms of temporal basis set formation and predict that moderately dense population activity optimizes learning.NEW & NOTEWORTHY During movement, mossy fiber inputs to the cerebellum relay time-varying information with strong intrinsic relationships to ongoing movement. Are such mossy fibers signals sufficient to support Purkinje signals and learning? In a model, we show how the GCL greatly improves Purkinje learning of complex, temporally dynamic signals relative to mossy fibers alone. Learning-optimized GCL population activity was moderately dense, which retained intrinsic input variance while also performing pattern separation.

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.

Dopaminergic regulation of vestibulo-cerebellar circuits through unipolar brush cells

While multiple monoamines modulate cerebellar output, the mechanistic details of dopaminergic signaling in the cerebellum remain poorly understood. We show that dopamine type 1 receptors (Drd1) are expressed in unipolar brush cells (UBCs) of the mouse cerebellar vermis. Drd1 activation increases UBC firing rate and post-synaptic NMDAR -mediated currents. Using anatomical tracing and in situ hybridization, we test three hypotheses about the source of cerebellar dopamine. We exclude midbrain dopaminergic nuclei and tyrosine hydroxylase-positive Purkinje (Pkj) cells as potential sources, supporting the possibility of dopaminergic co-release from locus coeruleus (LC) axons. Using an optical dopamine sensor GRABDA2h, electrical stimulation, and optogenetic activation of LC fibers in the acute slice, we find evidence for monoamine release onto Drd1-expressing UBCs. Altogether, we propose that the LC regulates cerebellar cortex activity by co-releasing dopamine onto UBCs to modulate their response to cerebellar inputs. Pkj cells directly inhibit these Drd1-positive UBCs, forming a dopamine-sensitive recurrent vestibulo-cerebellar circuit.

Reward signals in the cerebellum: origins, targets, and functional implications

The cerebellum has long been proposed to play a role in cognitive function, although this has remained controversial. This idea has received renewed support with the recent discovery that signals associated with reward can be observed in the cerebellar circuitry, particularly in goal-directed learning tasks involving an interplay between the cerebellar cortex, basal ganglia, and cerebral cortex. Remarkably, a wide range of reward contingencies-including reward expectation, delivery, size, and omission-can be encoded by specific circuit elements in a manner that reflects the microzonal organization of the cerebellar cortex. The facts that reward signals have been observed in both the mossy fiber and climbing fiber input pathways to the cerebellar cortex and that their convergence may trigger plasticity in Purkinje cells suggest that these interactions may be crucial for the role of the cerebellar cortex in learned behavior. These findings strengthen the emerging consensus that the cerebellum plays a pivotal role in shaping cognitive processing and suggest that the cerebellum may combine both supervised learning and reinforcement learning to optimize goal-directed action. We make specific predictions about how cerebellar circuits can work in concert with the basal ganglia to guide different stages of learning.

A cerebellar-based solution to the nondeterministic time delay problem in robotic control

[Figure: see text].

Autonomous Purkinje cell activation instructs bidirectional motor learning through evoked dendritic calcium signaling

The signals in cerebellar Purkinje cells sufficient to instruct motor learning have not been systematically determined. Therefore, we applied optogenetics in mice to autonomously excite Purkinje cells and measured the effect of this activity on plasticity induction and adaptive behavior. Ex vivo, excitation of channelrhodopsin-2-expressing Purkinje cells elicits dendritic Ca²⁺ transients with high-intensity stimuli initiating dendritic spiking that additionally contributes to the Ca²⁺ response. Channelrhodopsin-2-evoked Ca²⁺ transients potentiate co-active parallel fiber synapses; depression occurs when Ca²⁺ responses were enhanced by dendritic spiking. In vivo, optogenetic Purkinje cell activation drives an adaptive decrease in vestibulo-ocular reflex gain when vestibular stimuli are paired with relatively small-magnitude Purkinje cell Ca²⁺ responses. In contrast, pairing with large-magnitude Ca²⁺ responses increases vestibulo-ocular reflex gain. Optogenetically induced plasticity and motor adaptation are dependent on endocannabinoid signaling, indicating engagement of this pathway downstream of Purkinje cell Ca²⁺ elevation. Our results establish a causal relationship among Purkinje cell Ca²⁺ signal size, opposite-polarity plasticity induction, and bidirectional motor learning.

Plastic reweighting of parallel fiber synaptic strength is a mechanism for the acquisition of cerebellum-dependent motor learning. Here, the authors found that optogenetic activation of PCs generates dendritic Ca2+ signals that induce plasticity in vitro and instruct learned changes to coincident eye movements in vivo.

Incomplete removal of extracellular glutamate controls synaptic transmission and integration at a cerebellar synapse

Synapses of glutamatergic mossy fibers (MFs) onto cerebellar unipolar brush cells (UBCs) generate slow excitatory (ON) or inhibitory (OFF) postsynaptic responses dependent on the complement of glutamate receptors expressed on the UBC's large dendritic brush. Using mouse brain slice recording and computational modeling of synaptic transmission, we found that substantial glutamate is maintained in the UBC synaptic cleft, sufficient to modify spontaneous firing in OFF UBCs and tonically desensitize AMPARs of ON UBCs. The source of this ambient glutamate was spontaneous, spike-independent exocytosis from the MF terminal, and its level was dependent on activity of glutamate transporters EAAT1-2. Increasing levels of ambient glutamate shifted the polarity of evoked synaptic responses in ON UBCs and altered the phase of responses to in vivo-like synaptic activity. Unlike classical fast synapses, receptors at the UBC synapse are virtually always exposed to a significant level of glutamate, which varies in a graded manner during transmission.

Bidirectional learning in upbound and downbound microzones of the cerebellum

Over the past several decades, theories about cerebellar learning have evolved. A relatively simple view that highlighted the contribution of one major form of heterosynaptic plasticity to cerebellar motor learning has given way to a plethora of perspectives that suggest that many different forms of synaptic and non-synaptic plasticity, acting at various sites, can control multiple types of learning behaviour. However, there still seem to be contradictions between the various hypotheses with regard to the mechanisms underlying cerebellar learning. The challenge is therefore to reconcile these different views and unite them into a single overall concept. Here I review our current understanding of the changes in the activity of cerebellar Purkinje cells in different 'microzones' during various forms of learning. I describe an emerging model that indicates that the activity of each microzone is bound to either increase or decrease during the initial stages of learning, depending on the directional and temporal demands of its downstream circuitry and the behaviour involved.

How the Cerebellum and Prefrontal Cortex Cooperate During Trace Eyeblinking Conditioning

Several data have demonstrated that during the widely used experimental paradigm for studying associative learning, trace eye blinking conditioning (TEBC), there is a strong interaction between cerebellum and medial prefrontal cortex (mPFC). Despite this evidence, the neural mechanisms underlying this interaction are still not clear. Here, we propose a neurophysiologically plausible computational model to address this issue. The model is constrained on the basis of two critical anatomo-physiological features: (i) the cerebello-cortical organization through two circuits, respectively, targeting M1 and mPFC; (ii) the different timing in the plasticity mechanisms of these parallel circuits produced by the granule cells time sensitivity according to which different subpopulations are active at different moments during conditioned stimuli. The computer simulations run with the model suggest that these features are critical to understand how the cooperation between cerebellum and mPFC supports motor areas during TEBC. In particular, a greater trace interval produces greater plasticity changes at the slow path synapses involving mPFC with respect to plasticity changes at the fast path involving M1. As a consequence, the greater is the trace interval, the stronger is the mPFC involvement. The model has been validated by reproducing data collected through recent real mice experiments.

Principles of operation of a cerebellar learning circuit

We provide behavioral evidence using monkey smooth pursuit eye movements for four principles of cerebellar learning. Using a circuit-level model of the cerebellum, we link behavioral data to learning's neural implementation. The four principles are: (1) early, fast, acquisition driven by climbing fiber inputs to the cerebellar cortex, with poor retention; (2) learned responses of Purkinje cells guide transfer of learning from the cerebellar cortex to the deep cerebellar nucleus, with excellent retention; (3) functionally different neural signals are subject to learning in the cerebellar cortex versus the deep cerebellar nuclei; and (4) negative feedback from the cerebellum to the inferior olive reduces the magnitude of the teaching signal in climbing fibers and limits learning. Our circuit-level model, based on these four principles, explains behavioral data obtained by strategically manipulating the signals responsible for acquisition and recall of direction learning in smooth pursuit eye movements across multiple timescales.

Neural Correlates of Reinforcement Learning in Mid-lateral Cerebellum

The role of the cerebellum in non-motor learning is poorly understood. Here, we investigated the activity of Purkinje cells (P-cells) in the mid-lateral cerebellum as the monkey learned to associate one arbitrary symbol with the movement of the left hand and another with the movement of the right hand. During learning, but not when the monkey had learned the association, the simple spike responses of P-cells reported the outcome of the animal's most recent decision without concomitant changes in other sensorimotor parameters such as hand movement, licking, or eye movement. At the population level, P-cells collectively maintained a memory of the most recent decision throughout the entire trial. As the monkeys learned the association, the magnitude of this reward-related error signal approached zero. Our results provide a major departure from the current understanding of cerebellar processing and have critical implications for cerebellum's role in cognitive control.

Computational Principles of Supervised Learning in the Cerebellum

Supervised learning plays a key role in the operation of many biological and artificial neural networks. Analysis of the computations underlying supervised learning is facilitated by the relatively simple and uniform architecture of the cerebellum, a brain area that supports numerous motor, sensory, and cognitive functions. We highlight recent discoveries indicating that the cerebellum implements supervised learning using the following organizational principles: ( a) extensive preprocessing of input representations (i.e., feature engineering), ( b) massively recurrent circuit architecture, ( c) linear input-output computations, ( d) sophisticated instructive signals that can be regulated and are predictive, ( e) adaptive mechanisms of plasticity with multiple timescales, and ( f) task-specific hardware specializations. The principles emerging from studies of the cerebellum have striking parallels with those in other brain areas and in artificial neural networks, as well as some notable differences, which can inform future research on supervised learning and inspire next-generation machine-based algorithms.

Noradrenaline depresses spontaneous complex spikes activity of cerebellar Purkinje cells via α2-adrenergic receptor in vivo in mice

Locus coeruleus (LC) noradrenergic neurons afferents release noradrenaline (NA) in the cerebellar cortex for modulating cerebellar neuronal circuitry function. Our previous study found that NA inhibited the spontaneous simple spikes activity of cerebellar Purkinje cells (PC) through activation of molecular layer interneurons (MLIs) in vivo in mice. We here examined the effects of NA on spontaneous complex spikes (CSs) activity of cerebellar PC in urethane-anesthetized mice by electrophysiology recording technique and pharmacological methods. Our results showed that cerebellar surface perfusion of NA significantly reduced the number of spikelets and the area under curve (AUC) of the spontaneous CSs. Application of nonselective adrenergic receptor (AR) antagonist, phentolamine, abolished the NA-induced inhibition of CSs. However applying a nonselective β-AR blocker, propranolol, failed to prevent the NA-induced inhibition of CSs activity. The NA-induced inhibition of CSs activity was not blocked by α1-AR antagonist, prazosin, but it was abolished by α2-AR antagonist, yohimibine. Moreover, application of α2-AR agonist, UK14304 induced a depression of CSs activity and mimicked the NA-induced inhibition of CS activity. These results indicate that NA regulates spontaneous CSs activity of cerebellar PCs via activation of α2-AR in vivo in mice. Our present results suggest that noradrenergic neurons of LC may modulate the outputs of cerebellar PCs via inhibition of CSs activity.

Plasticity leading to cerebellum-dependent learning: two different regions, two different types

In memory research, studying cerebellum-dependent memory is advantageous due to its relatively simple neural architecture compared with that of other memory circuits. To understand how cerebellum-dependent memory develops and is stored in this circuit, numerous hypotheses have been proposed. These hypotheses are generally able to adequately explain most learning and memory processes; however, several reported results are still poorly understood. Recently, the importance of intrinsic plasticity (i.e., plasticity of intrinsic excitability) has been highlighted in several studies. Because the classical view of cerebellum-dependent eye movement learning was focused on synaptic plasticity, it is valuable to consider the intrinsic plasticity for deeper understanding. In the present review, we re-examine the utility and limitations of previous hypotheses, from classic to recent, and propose an updated hypothesis. Integrating intrinsic plasticity into current models of the vestibulo-ocular reflex (VOR) circuit may facilitate deeper understanding of the VOR adaptation process. In particular, during the period of memory transfer, dynamic changes in excitability in both cerebellar Purkinje cells and vestibular nuclear neurons illuminate the role of intrinsic plasticity in the circuit.

Cerebellar Purkinje cells control eye movements with a rapid rate code that is invariant to spike irregularity

The rate and temporal pattern of neural spiking each have the potential to influence computation. In the cerebellum, it has been hypothesized that the irregularity of interspike intervals in Purkinje cells affects their ability to transmit information to downstream neurons. Accordingly, during oculomotor behavior in mice and rhesus monkeys, mean irregularity of Purkinje cell spiking varied with mean eye velocity. However, moment-to-moment variations revealed a tight correlation between eye velocity and spike rate, with no additional information conveyed by spike irregularity. Moreover, when spike rate and irregularity were independently controlled using optogenetic stimulation, the eye movements elicited were well-described by a linear population rate code with 3-5 ms temporal precision. Biophysical and random-walk models identified biologically realistic parameter ranges that determine whether spike irregularity influences responses downstream. The results demonstrate cerebellar control of movements through a remarkably rapid rate code, with no evidence for an additional contribution of spike irregularity.

A Metric for Evaluating Neural Input Representation in Supervised Learning Networks

Supervised learning has long been attributed to several feed-forward neural circuits within the brain, with particular attention being paid to the cerebellar granular layer. The focus of this study is to evaluate the input activity representation of these feed-forward neural networks. The activity of cerebellar granule cells is conveyed by parallel fibers and translated into Purkinje cell activity, which constitutes the sole output of the cerebellar cortex. The learning process at this parallel-fiber-to-Purkinje-cell connection makes each Purkinje cell sensitive to a set of specific cerebellar states, which are roughly determined by the granule-cell activity during a certain time window. A Purkinje cell becomes sensitive to each neural input state and, consequently, the network operates as a function able to generate a desired output for each provided input by means of supervised learning. However, not all sets of Purkinje cell responses can be assigned to any set of input states due to the network's own limitations (inherent to the network neurobiological substrate), that is, not all input-output mapping can be learned. A key limiting factor is the representation of the input states through granule-cell activity. The quality of this representation (e.g., in terms of heterogeneity) will determine the capacity of the network to learn a varied set of outputs. Assessing the quality of this representation is interesting when developing and studying models of these networks to identify those neuron or network characteristics that enhance this representation. In this study we present an algorithm for evaluating quantitatively the level of compatibility/interference amongst a set of given cerebellar states according to their representation (granule-cell activation patterns) without the need for actually conducting simulations and network training. The algorithm input consists of a real-number matrix that codifies the activity level of every considered granule-cell in each state. The capability of this representation to generate a varied set of outputs is evaluated geometrically, thus resulting in a real number that assesses the goodness of the representation.

No neuron is an island: Homeostatic plasticity and over-constraint in a neural circuit

To support computation the activity of neurons must vary within a useful range, which highlights one potential value of homeostatic plasticity. The interconnectedness of the brain, however, introduces the possibility that combinations of homeostatic mechanisms can produce over-constraint in which not all set points can be satisfied. We use a simulation of the cerebellum to investigate the potential for such conflict and its consequences. In this instance the conflict produces perpetual drift and eventual saturation of synaptic weights. We show that these problems can be resolved for this network by a particular combination of sites and rules for plasticity. We also show that simulations that implement these rules for homeostatic plasticity are more resistant to forgetting. These results illustrate the general principle that homeostatic plasticity within a system must not set up conflicts in which mutually exclusive set points exist and that one consequence can be perpetual induction of plasticity.

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.

Realization of Direction Selective Motor Learning in the Artificial Cerebellum: Simulation on the Vestibuloocular Reflex Adaptation

The vestibule-ocular reflex (VOR) has been one of the most popular model systems to investigate the role of the cerebellum in adaptive motor control. VOR motor learning can be experimentally induced by continuous application of combination of head rotating stimulus and optokinetic stimulus. For instance, in phase application of those stimuli decreases VOR gain defined by eye velocity of VOR in the dark divided by head velocity, while out of phase of those increases VOR gain. It has been known that VOR gain is modifiable context-dependently. Namely, VOR gains for leftward and rightward head rotations can be respectively increased and decreased simultaneously. The cerebellar signal processing underlying the context dependent VOR motor learning, however, is not fully uncovered. In the present study, we simulated direction selective VOR motor learning, using the artificial cerebellar neuronal network model that we developed to understand the origin of the cerebellar motor learning.

Dopamine D1 Receptor-Positive Neurons in the Lateral Nucleus of the Cerebellum Contribute to Cognitive Behavior

BACKGROUND

Studies in humans and nonhuman primates have identified a region of the dentate nucleus of the cerebellum, or the lateral cerebellar nucleus (LCN) in rodents, activated during performance of cognitive tasks involving complex spatial and sequential planning. Whether such a subdivision exists in rodents is not known. Dopamine and its receptors, which are implicated in cognitive function, are present in the cerebellar nuclei, but their function is unknown.

METHODS

Using viral and genetic strategies in mice, we examined cellular phenotypes of dopamine D₁ receptor-positive (D1R+) cells in the LCN with whole-cell patch clamp recordings, messenger RNA profiling, and immunohistochemistry to examine D1R expression in mouse LCN and human dentate nucleus of the cerebellum. We used chemogenetics to inhibit D1R+ neurons and examined behaviors including spatial navigation, social recognition memory, prepulse inhibition of the acoustic startle reflex, response inhibition, and working memory to test the necessity of these neurons in these behaviors.

RESULTS

We identified a population of D1R+ neurons that are localized to an anatomically distinct region of the LCN. We also observed D1R+ neurons in human dentate nucleus of the cerebellum, which suggests an evolutionarily conserved population of dopamine-receptive neurons in this region. The genetic, electrophysiological, and anatomical profile of mouse D1R neurons is consistent with a heterogeneous population of gamma-aminobutyric acidergic, and to a lesser extent glutamatergic, cell types. Selective inhibition of D1R+ LCN neurons impairs spatial navigation memory, response inhibition, working memory, and prepulse inhibition of the acoustic startle reflex.

CONCLUSIONS

Collectively, these data demonstrate a functional link between genetically distinct neurons in the LCN and cognitive behaviors.

Graded Control of Climbing-Fiber-Mediated Plasticity and Learning by Inhibition in the Cerebellum

Purkinje cell dendrites convert excitatory climbing fiber input into signals that instruct plasticity and motor learning. Modulation of instructive signaling may increase the range in which learning is encoded, yet the mechanisms that allow for this are poorly understood. We found that optogenetic activation of molecular layer interneurons (MLIs) that inhibit Purkinje cells suppressed climbing-fiber-evoked dendritic Ca²⁺ spiking. Inhibitory suppression of Ca²⁺ spiking depended on the level of MLI activation and influenced the induction of associative synaptic plasticity, converting climbing-fiber-mediated potentiation of parallel fiber-evoked responses into depression. In awake mice, optogenetic activation of floccular climbing fibers in association with head rotation produced an adaptive increase in the vestibulo-ocular reflex (VOR). However, when climbing fibers were co-activated with MLIs, adaptation occurred in the opposite direction, decreasing the VOR. Thus, MLIs can direct a continuous spectrum of plasticity and learning through their influence on Purkinje cell dendritic Ca²⁺ signaling.

Cell type-specific plasticity at parallel fiber synapses onto Purkinje cells in the posterior caudal lobe of the mormyrid fish cerebellum

It has been demonstrated that there are two morphological subtypes of Purkinje cells (PCs)-fan-shaped Purkinje cells (fPCs) and multipolar Purkinje cells (mPCs)-in the posterior caudal lobe of the mormyrid fish cerebellum, but whether these cell types are also functionally distinct is unknown. Here, we have used electrophysiological and pharmacological tools in a slice preparation to demonstrate that pairing parallel fiber (PF) and climbing fiber (CF) inputs at a low frequency induces long-term depression (LTD) in fPCs but long-term potentiation (LTP) in mPCs. The induction of plasticity in both cell types required postsynaptic Ca²⁺ and type 1α metabotropic glutamate receptors. However, the LTD in fPCs was inducted via a calcium/calmodulin-dependent protein kinase II cascade, whereas LTP induction in mPCs required calcineurin. Moreover, the LTD in fPCs and LTP in mPCs were accompanied by changes to the corresponding paired-pulse ratios and their coefficients of variation, suggesting presynaptic modes of expression for the plasticity at PF terminals for both cell types. Hence, the synaptic plasticity at PF synapses onto PCs in the posterior caudal lobe of the mormyrid cerebellum is cell type specific, with both pre- and postsynaptic mechanisms contributing to its induction and expression. NEW & NOTEWORTHY Much has been learnt about the cerebellar long-term depression (LTD) in the cortex. More recent work has shown that long-term potentiation (LTP) is equally important for cerebellar motor learning. Here we report for the first time that plasticity in the mormyrid cerebellum is cell type specific, e.g., following the conventional pairing of parallel and climbing fiber inputs in an in vitro preparation leads to LTD in one Purkinje cell subtype and LTP in another.

No Medium-Term Spinocerebellar Input Plasticity in Deep Cerebellar Nuclear Neurons In Vivo?

The existence of input plasticity in the deep cerebellar nuclear (DCN) cells of the adult cerebellum could have profound implications for our understanding of cerebellar function. Whereas the existence of plastic changes in mossy fiber (mf) synaptic responses in DCN neurons has been demonstrated in juvenile slices, there has so far been no direct demonstration of this form of plasticity in the adult cerebellum in vivo. In the present paper, we recorded from neurons in the anterior interposed nucleus (AIN) and stimulated the spinocerebellar tracts (SCT) directly or via the skin to obtain mf activation and the inferior olive to activate climbing fibers (cfs) in the nonanesthetized, adult, decerebrated cat. We used three different types of protocols that theoretically could be expected to induce plasticity, each of which involved episodically intense afferent activation lasting for 10 min. These were conjunctive mf-cf activation, which effectively induces plasticity in cortical neurons; mf and cf activation in a pattern resembling the protocol for inducing classical conditioning; and conjunctive activation of two excitatory mf inputs. None of these protocols had any statistically significant effect on the evoked responses in the AIN neurons. We conclude that the input plasticity for excitatory mfs in the AIN cells of the adult cerebellum in vivo is likely to be less effective than that of parallel fiber synaptic inputs in cerebellar cortical cells, at least in the timespan of 1 h.

Computational Theory Underlying Acute Vestibulo-ocular Reflex Motor Learning with Cerebellar Long-Term Depression and Long-Term Potentiation

The vestibulo-ocular reflex (VOR) can be viewed as an adaptive control system that maintains compensatory eye movements during head motion. As the cerebellar flocculus is intimately involved in this adaptive motor control of the VOR, the VOR has been a popular model system for investigating cerebellar motor learning. Long-term depression (LTD) and long-term potentiation (LTP) at the parallel fiber-Purkinje cell synapses are considered to play major roles in cerebellar motor learning. A recent study using mutant mice demonstrated cerebellar motor learning with hampered LTD; the study concluded that the parallel fiber-Purkinje cell LTD is not essential. More recently, multiple forms of plasticity have been found in the cerebellum, and they are believed to contribute to cerebellar motor learning. However, it is still unclear how synaptic plasticity modifies the signal processing that underlies motor learning in the flocculus. A computational simulation suggested that the plasticity present in mossy fiber-granule cell synapses improves VOR-related sensory-motor information transferred into granule cells, whereas the plasticity in the molecular layer stores this information as a memory under guidance from climbing fiber teaching signals. Thus, motor learning and memory are thought to be induced mainly by LTD and LTP at parallel fiber-Purkinje cell synapses and by rebound potentiation at molecular interneuron-Purkinje cell synapses among the multiple forms of plasticity in the cerebellum. In this study, we focused on the LTD and LTP at parallel fiber-Purkinje cell synapses. Based on our simulation, we propose that acute VOR motor learning accomplishes by simultaneous enhancement of eye movement signals via LTP and suppression of vestibular signals via LTD to increase VOR gain (gain-up learning). To decrease VOR gain (gain-down learning), these two signals are modified in the opposite directions; namely, LTD suppresses eye movement signals, whereas LTP enhances vestibular signals.

The Timing Is Right for Cerebellar Learning

In this issue of Neuron, Suvrathan et al. (2016) provide a striking demonstration of a plasticity rule with temporal properties precisely matched to the computational requirements of behavioral learning and suggest major revisions to the rules for synaptic plasticity in the cerebellum.

Mechanisms underlying vestibulo-cerebellar motor learning in mice depend on movement direction

KEY POINTS

Directionality, inherent to movements, has behavioural and neuronal correlates. Direction of vestibular stimulation determines motor learning efficiency. Vestibulo-ocular reflex gain-increase correlates with Purkinje cell simple spike potentiation. The locus of neural correlates for vestibulo-ocular reflex adaptation is paradigm specific.

ABSTRACT

Compensatory eye movements elicited by head rotation, also known as vestibulo-ocular reflex (VOR), can be adapted with the use of visual feedback. The cerebellum is essential for this type of movement adaptation, although its neuronal correlates remain to be clarified. In the present study, we show that the direction of vestibular input determines the magnitude of eye movement adaptation induced by mismatched visual input in mice, with larger changes during contraversive head rotation. Moreover, the location of the neural correlate of this changed behaviour depends on the type of paradigm. Gain-increase paradigms induce increased simple spike (SS) activity in ipsilateral cerebellar Purkinje cells (PC), which is in line with eye movements triggered by optogenetic PC activation. By contrast, gain-decrease paradigms do not induce changes in SS activity, indicating that the murine vestibulo-cerebellar cortical circuitry is optimally designed to enhance ipsiversive eye movements.

Evaluation of Teaching Signals for Motor Control in the Cerebellum during Real-World Robot Application

Motor learning in the cerebellum is believed to entail plastic changes at synapses between parallel fibers and Purkinje cells, induced by the teaching signal conveyed in the climbing fiber (CF) input. Despite the abundant research on the cerebellum, the nature of this signal is still a matter of debate. Two types of movement error information have been proposed to be plausible teaching signals: sensory error (SE) and motor command error (ME); however, their plausibility has not been tested in the real world. Here, we conducted a comparison of different types of CF teaching signals in real-world engineering applications by using a realistic neuronal network model of the cerebellum. We employed a direct current motor (simple task) and a two-wheeled balancing robot (difficult task). We demonstrate that SE, ME or a linear combination of the two is sufficient to yield comparable performance in a simple task. When the task is more difficult, although SE slightly outperformed ME, these types of error information are all able to adequately control the robot. We categorize granular cells according to their inputs and the error signal revealing that different granule cells are preferably engaged for SE, ME or their combination. Thus, unlike previous theoretical and simulation studies that support either SE or ME, it is demonstrated for the first time in a real-world engineering application that both SE and ME are adequate as the CF teaching signal in a realistic computational cerebellar model, even when the control task is as difficult as stabilizing a two-wheeled balancing robot.

Timing Rules for Synaptic Plasticity Matched to Behavioral Function

It is widely assumed that the complexity of neural circuits enables them to implement diverse learning tasks using just a few generic forms of synaptic plasticity. In contrast, we report that synaptic plasticity can itself be precisely tuned to the requirements of a learning task. We found that the rules for induction of long-term and single-trial plasticity at parallel fiber-to-Purkinje cell synapses vary across cerebellar regions. In the flocculus, associative plasticity in vitro and in vivo is narrowly tuned for an interval of ∼120 ms, which compensates for the specific processing delay for error signals to reach the flocculus during oculomotor learning. In the vermis, which supports a range of behavioral functions, plasticity is induced by a range of intervals, with individual cells tuned for different intervals. Thus, plasticity at a single, anatomically defined type of synapse can have properties that vary in a way that is precisely matched to function.

Asymmetries in Cerebellar Plasticity and Motor Learning

Synaptic plasticity at the parallel fiber to Purkinje cell synapse has long been considered a cellular correlate for cerebellar motor learning. Functionally, long-term depression and long-term potentiation at these synapses seem to be the reverse of each other, with both pre- and post-synaptic expression occurring in both. However, different cerebellar motor learning paradigms have been shown to be asymmetric and not equally reversible. Here, we discuss the asymmetric reversibility shown in the vestibulo-ocular reflex and eyeblink conditioning and suggest that different cellular plasticity mechanisms might be recruited under different conditions leading to unequal reversibility.

The cerebro-cerebellum: Could it be loci of forward models?

It is widely accepted that the cerebellum acquires and maintain internal models for motor control. An internal model simulates mapping between a set of causes and effects. There are two candidates of cerebellar internal models, forward models and inverse models. A forward model transforms a motor command into a prediction of the sensory consequences of a movement. In contrast, an inverse model inverts the information flow of the forward model. Despite the clearly different formulations of the two internal models, it is still controversial whether the cerebro-cerebellum, the phylogenetically newer part of the cerebellum, provides inverse models or forward models for voluntary limb movements or other higher brain functions. In this article, we review physiological and morphological evidence that suggests the existence in the cerebro-cerebellum of a forward model for limb movement. We will also discuss how the characteristic input-output organization of the cerebro-cerebellum may contribute to forward models for non-motor higher brain functions.

Abnormal Head Impulse Test in a Unilateral Cerebellar Lesion

Background

The findings of head impulse tests (HIT) are usually normal in cerebellar lesions.

Case Report

A 46-year-old male presented with progressive dizziness and imbalance of 3 weeks duration. The patient exhibited catch-up saccades during bedside horizontal HIT to either side, which was more evident during the rightward HIT. However, results of bithermal caloric tests and rotatory chair test were normal. MRI revealed a lesion in the inferior cerebellum near the flocculus.

Conclusions

This case provides additional evidence that damage to the flocculus or its connections may impair the vestibulo-ocular reflex only during high-speed stimuli, especially when the stimuli are applied to the contralesional side. By observing accompanying cerebellar signs, the abnormal HIT findings caused by a cerebellar disorder can be distinguished from those produced by peripheral vestibular disorders.

Extracellular matrix molecules exhibit unique expression pattern in the climbing fiber-generating precerebellar nucleus, the inferior olive

Extracellular matrix (ECM) accumulates around different neuronal compartments of the central nervous system (CNS) or appears in diffuse reticular form throughout the neuropil. In the adult CNS, the perineuronal net (PNN) surrounds the perikarya and dendrites of various neuron types, whereas the axonal coats are aggregations of ECM around the individual synapses, and the nodal ECM is localized at the nodes of Ranvier. Previous studies in our laboratory demonstrated on rats that the heterogeneous distribution and molecular composition of ECM is associated with the variable cytoarchitecture and hodological organization of the vestibular nuclei and may also be related to their specific functions in gaze and posture control as well as in the compensatory mechanisms following vestibular lesion. Here, we investigated the ECM expression pattern in the climbing fiber-generating inferior olive (IO), which is functionally related to the vestibular nuclei. By using histochemical and immunohistochemical methods, the most characteristic finding was the lack of PNNs, presumably due to the absence of synapses on the perikarya and proximal dendrites of IO neurons. On the other hand, the darkly stained dots or ring-like structures in the neuropil might represent the periaxonal coats around the axon terminals of olivary synaptic glomeruli. We have observed positive ECM reaction for the hyaluronan, tenascin-R, hyaluronan and proteoglycan link protein 1 (HAPLN1) and various chondroitin sulfate proteoglycans. The staining intensity and distribution of ECM molecules revealed a number of differences between the functionally different subnuclei of IO. We hypothesized that the different molecular composition and intensity differences of ECM reaction is associated with different control mechanisms of gaze and posture control executed by the visuomotor-vestibular, somatosensory and integrative subnuclei of the IO.

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

Variable timing of synaptic transmission in cerebellar unipolar brush cells

The cerebellum ensures the smooth execution of movements, a task that requires accurate neural signaling on multiple time scales. Computational models of cerebellar timing mechanisms have suggested that temporal information in cerebellum-dependent behavioral tasks is in part computed locally in the cerebellar cortex. These models rely on the local generation of delayed signals spanning hundreds of milliseconds, yet the underlying neural mechanism remains elusive. Here we show that a granular layer interneuron, called the unipolar brush cell, is well suited to represent time intervals in a robust way in the cerebellar cortex. Unipolar brush cells exhibited delayed increases in excitatory synaptic input in response to presynaptic stimulation in mouse cerebellar slices. Depending on the frequency of stimulation, delays extended from zero up to hundreds of milliseconds. Such controllable protraction of delayed currents was the result of an unusual mode of synaptic integration, which was well described by a model of steady-state AMPA receptor activation. This functionality extends the capabilities of the cerebellum for adaptive control of behavior by facilitating appropriate output in a broad temporal window.

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.

Cerebellar Purkinje cell activity drives motor learning

The climbing fiber input to the cerebellar cortex is thought to provide instructive signals that drive the induction of motor skill learning. We found that optogenetic activation of Purkinje cells, the sole output neurons of the cerebellar cortex, can also drive motor learning in mice. This dual control over the induction of learning by climbing fibers and Purkinje cells can expand the learning capacity of motor circuits.

The repetition timing of high frequency afferent stimulation drives the bidirectional plasticity at central synapses in the rat medial vestibular nuclei

In this study we show that high frequency stimulation (HFS, 100Hz) of afferent fibers to the medial vestibular nucleus (MVN) can induce opposite long-term modifications of synaptic responses in the type B neurons depending upon the stimulation pattern. Long burst stimulation (LBS: 2s) and short burst stimulation (SBS: 0.55s) were applied with different burst number (BN) and inter-burst intervals (IBI). It results that both LBS and SBS can induce either N-methyl-d aspartate receptors (NMDARs)-mediated long-term potentiation (LTP) or long-term depression (LTD), depending on temporal organization of repetitive bursts. In particular, the IBI plays a relevant role in guiding the shift from LTP to LTD since by using both LBS and SBS LTP is induced by shorter IBI than LTD. By contrast, the sign of long-term effect does not depend on the mean impulse frequency evaluated within the entire stimulation period. Therefore, the patterns of repetitive vestibular activation with different ratios between periods of increased activity and periods of basal activity may lead to LTP or LTD probably causing different levels of postsynaptic Ca(2+). On the whole, this study demonstrates that glutamatergic vestibular synapse in the MVN can undergo NMDAR-dependent bidirectional plasticity and puts forward a new aspect for understanding the adaptive and compensatory plasticity of the oculomotor responses.

A computational mechanism for unified gain and timing control in the cerebellum

Precise gain and timing control is the goal of cerebellar motor learning. Because the basic neural circuitry of the cerebellum is homogeneous throughout the cerebellar cortex, a single computational mechanism may be used for simultaneous gain and timing control. Although many computational models of the cerebellum have been proposed for either gain or timing control, few models have aimed to unify them. In this paper, we hypothesize that gain and timing control can be unified by learning of the complete waveform of the desired movement profile instructed by climbing fiber signals. To justify our hypothesis, we adopted a large-scale spiking network model of the cerebellum, which was originally developed for cerebellar timing mechanisms to explain the experimental data of Pavlovian delay eyeblink conditioning, to the gain adaptation of optokinetic response (OKR) eye movements. By conducting large-scale computer simulations, we could reproduce some features of OKR adaptation, such as the learning-related change of simple spike firing of model Purkinje cells and vestibular nuclear neurons, simulated gain increase, and frequency-dependent gain increase. These results suggest that the cerebellum may use a single computational mechanism to control gain and timing simultaneously.