Serial processing of kinematic signals by cerebellar circuitry during voluntary whisking

Simple spike patterns and synaptic mechanisms encoding sensory and motor signals in Purkinje cells and the cerebellar nuclei

Whisker stimulation in awake mice evokes transient suppression of simple spike probability in crus I/II Purkinje cells. Here, we investigated how simple spike suppression arises synaptically, what it encodes, and how it affects cerebellar output. In vitro, monosynaptic parallel fiber (PF)-excitatory postsynaptic currents (EPSCs) facilitated strongly, whereas disynaptic inhibitory postsynaptic currents (IPSCs) remained stable, maximizing relative inhibitory strength at the onset of PF activity. Short-term plasticity thus favors the inhibition of Purkinje spikes before PFs facilitate. In vivo, whisker stimulation evoked a 2-6 ms synchronous spike suppression, just 6-8 ms (∼4 synaptic delays) after sensory onset, whereas active whisker movements elicited broadly timed spike rate increases that did not modulate sensory-evoked suppression. Firing in the cerebellar nuclei (CbN) inversely correlated with disinhibition from sensory-evoked simple spike suppressions but was decoupled from slow, non-synchronous movement-associated elevations of Purkinje firing rates. Synchrony thus allows the CbN to high-pass filter Purkinje inputs, facilitating sensory-evoked cerebellar outputs that can drive movements.

Local synaptic inhibition mediates cerebellar granule cell pattern separation and enables learned sensorimotor associations

The cerebellar cortex has a key role in generating predictive sensorimotor associations. To do so, the granule cell layer is thought to establish unique sensorimotor representations for learning. However, how this is achieved and how granule cell population responses contribute to behavior have remained unclear. To address these questions, we have used in vivo calcium imaging and granule cell-specific pharmacological manipulation of synaptic inhibition in awake, behaving mice. These experiments indicate that inhibition sparsens and thresholds sensory responses, limiting overlap between sensory ensembles and preventing spiking in many granule cells that receive excitatory input. Moreover, inhibition can be recruited in a stimulus-specific manner to powerfully decorrelate multisensory ensembles. Consistent with these results, granule cell inhibition is required for accurate cerebellum-dependent sensorimotor behavior. These data thus reveal key mechanisms for granule cell layer pattern separation beyond those envisioned by classical models.

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.

Expression of a Form of Cerebellar Motor Memory Requires Learned Alterations to the Activity of Inhibitory Molecular Layer Interneurons

Procedural memories formed in the cerebellum in response to motor errors depend on changes to Purkinje cell (PC) spiking patterns that correct movement when the erroneous context is repeated. Because molecular layer interneurons (MLIs) inhibit PCs, learning-induced changes to MLI output may participate in reshaping PC spiking patterns. However, it remains unclear whether error-driven learning alters MLI activity and whether such changes are necessary for the memory engram. We addressed this knowledge gap by measuring and manipulating MLI activity in the flocculus of both sexes of mice before and after vestibulo-ocular reflex (VOR) adaptation. We found that MLIs are activated during vestibular stimuli and that their population response exhibits a phase shift after the instantiation of gain-increase VOR adaptation, a type of error-driven learning thought to require climbing-fiber-mediated instructive signaling. Although acute optogenetic suppression of MLI activity did not affect baseline VOR performance, it negated the expression of gain-increase learning, demonstrating a specific role of MLI activity changes in motor memory expression. This effect was transitory; after a multiday consolidation period, the expression of VOR gain-increase learning was no longer sensitive to MLI activity suppression. Together, our results indicate that error-driven alteration of MLI activity is necessary for labile, climbing-fiber-induced motor memory expression.SIGNIFICANCE STATEMENT In the cerebellum, motor learning induces an associative memory of the sensorimotor context of an erroneous movement that, when recalled, results in a new pattern of output that improves subsequent trials of performance. Our study shows that error-driven motor learning induces changes to the activity pattern of cerebellar molecular layer interneurons (MLIs) and that this new pattern of activity is required to express the corrective motor memory.

Synaptic basis of a sub-second representation of time in a neural circuit model

Temporal sequences of neural activity are essential for driving well-timed behaviors, but the underlying cellular and circuit mechanisms remain elusive. We leveraged the well-defined architecture of the cerebellum, a brain region known to support temporally precise actions, to explore theoretically whether the experimentally observed diversity of short-term synaptic plasticity (STP) at the input layer could generate neural dynamics sufficient for sub-second temporal learning. A cerebellar circuit model equipped with dynamic synapses produced a diverse set of transient granule cell firing patterns that provided a temporal basis set for learning precisely timed pauses in Purkinje cell activity during simulated delay eyelid conditioning and Bayesian interval estimation. The learning performance across time intervals was influenced by the temporal bandwidth of the temporal basis, which was determined by the input layer synaptic properties. The ubiquity of STP throughout the brain positions it as a general, tunable cellular mechanism for sculpting neural dynamics and fine-tuning behavior.

Cerebellum as a kernel machine: A novel perspective on expansion recoding in granule cell layer

Sensorimotor information provided by mossy fibers (MF) is mapped to high-dimensional space by a huge number of granule cells (GrC) in the cerebellar cortex's input layer. Significant studies have demonstrated the computational advantages and primary contributor of this expansion recoding. Here, we propose a novel perspective on the expansion recoding where each GrC serve as a kernel basis function, thereby the cerebellum can operate like a kernel machine that implicitly use high dimensional (even infinite) feature spaces. We highlight that the generation of kernel basis function is indeed biologically plausible scenario, considering that the key idea of kernel machine is to memorize important input patterns. We present potential regimes for developing kernels under constrained resources and discuss the advantages and disadvantages of each regime using various simulation settings.

Deconstruction of Vermal Cerebellum in Ramp Locomotion in Mice

The cerebellum is involved in encoding balance, posture, speed, and gravity during locomotion. However, most studies are carried out on flat surfaces, and little is known about cerebellar activity during free ambulation on slopes. Here, it has been imaged the neuronal activity of cerebellar molecular interneurons (MLIs) and Purkinje cells (PCs) using a miniaturized microscope while a mouse is walking on a slope. It has been found that the neuronal activity of vermal MLIs specifically enhanced during uphill and downhill locomotion. In addition, a subset of MLIs is activated during entire uphill or downhill positions on the slope and is modulated by the slope inclines. In contrast, PCs showed counter-balanced neuronal activity to MLIs, which reduced activity at the ramp peak. So, PCs may represent the ramp environment at the population level. In addition, chemogenetic inactivation of lobule V of the vermis impaired uphill locomotion. These results revealed a novel micro-circuit in the vermal cerebellum that regulates ambulatory behavior in 3D terrains.

The Cerebellar Cortex

The cerebellar cortex is an important system for relating neural circuits and learning. Its promise reflects the longstanding idea that it contains simple, repeated circuit modules with only a few cell types and a single plasticity mechanism that mediates learning according to classical Marr-Albus models. However, emerging data have revealed surprising diversity in neuron types, synaptic connections, and plasticity mechanisms, both locally and regionally within the cerebellar cortex. In light of these findings, it is not surprising that attempts to generate a holistic model of cerebellar learning across different behaviors have not been successful. While the cerebellum remains an ideal system for linking neuronal function with behavior, it is necessary to update the cerebellar circuit framework to achieve its great promise. In this review, we highlight recent advances in our understanding of cerebellar-cortical cell types, synaptic connections, signaling mechanisms, and forms of plasticity that enrich cerebellar processing.

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

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

Olivocerebellar control of movement symmetry

Coordination of bilateral movements is essential for a large variety of animal behaviors. The olivocerebellar system is critical for the control of movement, but its role in bilateral coordination has yet to be elucidated. Here, we examined whether Purkinje cells encode and influence synchronicity of left-right whisker movements. We found that complex spike activity is correlated with a prominent left-right symmetry of spontaneous whisker movements within parts, but not all, of Crus1 and Crus2. Optogenetic stimulation of climbing fibers in the areas with high and low correlations resulted in symmetric and asymmetric whisker movements, respectively. Moreover, when simple spike frequency prior to the complex spike was higher, the complex spike-related symmetric whisker protractions were larger. This finding alludes to a role for rebound activity in the cerebellar nuclei, which indeed turned out to be enhanced during symmetric protractions. Tracer injections suggest that regions associated with symmetric whisker movements are anatomically connected to the contralateral cerebellar hemisphere. Together, these data point toward the existence of modules on both sides of the cerebellar cortex that can differentially promote or reduce the symmetry of left and right movements in a context-dependent fashion.

Interactions among diameter, myelination, and the Na/K pump affect axonal resilience to high-frequency spiking

The reliability of spike propagation in axons is determined by complex interactions among ionic currents, ion pumps, and morphological properties. We use compartment-based modeling to reveal that interactions of diameter, myelination, and the Na/K pump determine the reliability of high-frequency spike propagation. By acting as a “reservoir” of nodal Na⁺ influx, myelinated compartments efficiently increase propagation reliability. Although spike broadening was thought to oppose fast spiking, its effect on spike propagation is complicated, depending on the balance of Na⁺ channel inactivation gate recovery, Na⁺ influx, and axial charge. Our findings suggest that slow Na⁺ removal influences axonal resilience to high-frequency spike propagation and that different strategies may be required to overcome this constraint in different neurons.

Axons reliably conduct action potentials between neurons and/or other targets. Axons have widely variable diameters and can be myelinated or unmyelinated. Although the effect of these factors on propagation speed is well studied, how they constrain axonal resilience to high-frequency spiking is incompletely understood. Maximal firing frequencies range from ∼1 Hz to >300 Hz across neurons, but the process by which Na/K pumps counteract Na⁺ influx is slow, and the extent to which slow Na⁺ removal is compatible with high-frequency spiking is unclear. Modeling the process of Na⁺ removal shows that large-diameter axons are more resilient to high-frequency spikes than are small-diameter axons, because of their slow Na⁺ accumulation. In myelinated axons, the myelinated compartments between nodes of Ranvier act as a “reservoir” to slow Na⁺ accumulation and increase the reliability of axonal propagation. We now find that slowing the activation of K⁺ current can increase the Na⁺ influx rate, and the effect of minimizing the overlap between Na⁺ and K⁺ currents on spike propagation resilience depends on complex interactions among diameter, myelination, and the Na/K pump density. Our results suggest that, in neurons with different channel gating kinetic parameters, different strategies may be required to improve the reliability of axonal propagation.

Cerebellar granule cell axons support high-dimensional representations

In classical theories of cerebellar cortex, high-dimensional sensorimotor representations are used to separate neuronal activity patterns, improving associative learning and motor performance. Recent experimental studies suggest that cerebellar granule cell (GrC) population activity is low-dimensional. To examine sensorimotor representations from the point of view of downstream Purkinje cell 'decoders', we used three-dimensional acousto-optic lens two-photon microscopy to record from hundreds of GrC axons. Here we show that GrC axon population activity is high dimensional and distributed with little fine-scale spatial structure during spontaneous behaviors. Moreover, distinct behavioral states are represented along orthogonal dimensions in neuronal activity space. These results suggest that the cerebellar cortex supports high-dimensional representations and segregates behavioral state-dependent computations into orthogonal subspaces, as reported in the neocortex. Our findings match the predictions of cerebellar pattern separation theories and suggest that the cerebellum and neocortex use population codes with common features, despite their vastly different circuit structures.

Accounting for uncertainty: inhibition for neural inference in the cerebellum

Sensorimotor coordination is thought to rely on cerebellar-based internal models for state estimation, but the underlying neural mechanisms and specific contribution of the cerebellar components is unknown. A central aspect of any inferential process is the representation of uncertainty or conversely precision characterizing the ensuing estimates. Here, we discuss the possible contribution of inhibition to the encoding of precision of neural representations in the granular layer of the cerebellar cortex. Within this layer, Golgi cells influence excitatory granule cells, and their action is critical in shaping information transmission downstream to Purkinje cells. In this review, we equate the ensuing excitation-inhibition balance in the granular layer with the outcome of a precision-weighted inferential process, and highlight the physiological characteristics of Golgi cell inhibition that are consistent with such computations.

The Cellular Electrophysiological Properties Underlying Multiplexed Coding in Purkinje Cells

Neuronal firing patterns are crucial to underpin circuit level behaviors. In cerebellar Purkinje cells (PCs), both spike rates and pauses are used for behavioral coding, but the cellular mechanisms causing code transitions remain unknown. We use a well-validated PC model to explore the coding strategy that individual PCs use to process parallel fiber (PF) inputs. We find increasing input intensity shifts PCs from linear rate-coders to burst-pause timing-coders by triggering localized dendritic spikes. We validate dendritic spike properties with experimental data, elucidate spiking mechanisms, and predict spiking thresholds with and without inhibition. Both linear and burst-pause computations use individual branches as computational units, which challenges the traditional view of PCs as linear point neurons. Dendritic spike thresholds can be regulated by voltage state, compartmentalized channel modulation, between-branch interaction and synaptic inhibition to expand the dynamic range of linear computation or burst-pause computation. In addition, co-activated PF inputs between branches can modify somatic maximum spike rates and pause durations to make them carry analog signals. Our results provide new insights into the strategies used by individual neurons to expand their capacity of information processing.

SIGNIFICANCE STATEMENT Understanding how neurons process information is a fundamental question in neuroscience. Purkinje cells (PCs) were traditionally regarded as linear point neurons. We used computational modeling to unveil their electrophysiological properties underlying the multiplexed coding strategy that is observed during behaviors. We demonstrate that increasing input intensity triggers localized dendritic spikes, shifting PCs from linear rate-coders to burst-pause timing-coders. Both coding strategies work at the level of individual dendritic branches. Our work suggests that PCs have the ability to implement branch-specific multiplexed coding at the cellular level, thereby increasing the capacity of cerebellar coding and learning.

Molecular Layer Interneurons: Key Elements of Cerebellar Network Computation and Behavior

Molecular layer interneurons (MLIs) play an important role in cerebellar information processing by controlling Purkinje cell (PC) activity via inhibitory synaptic transmission. A local MLI network, constructed from both chemical and electrical synapses, is organized into spatially structured clusters that amplify feedforward and lateral inhibition to shape the temporal and spatial patterns of PC activity. Several recent in vivo studies indicate that such MLI circuits contribute not only to sensorimotor information processing, but also to precise motor coordination and cognitive processes. Here, we review current understanding of the organization of MLI circuits and their roles in the function of the mammalian cerebellum.

Simulation of a Human-Scale Cerebellar Network Model on the K Computer

Computer simulation of the human brain at an individual neuron resolution is an ultimate goal of computational neuroscience. The Japanese flagship supercomputer, K, provides unprecedented computational capability toward this goal. The cerebellum contains 80% of the neurons in the whole brain. Therefore, computer simulation of the human-scale cerebellum will be a challenge for modern supercomputers. In this study, we built a human-scale spiking network model of the cerebellum, composed of 68 billion spiking neurons, on the K computer. As a benchmark, we performed a computer simulation of a cerebellum-dependent eye movement task known as the optokinetic response. We succeeded in reproducing plausible neuronal activity patterns that are observed experimentally in animals. The model was built on dedicated neural network simulation software called MONET (Millefeuille-like Organization NEural neTwork), which calculates layered sheet types of neural networks with parallelization by tile partitioning. To examine the scalability of the MONET simulator, we repeatedly performed simulations while changing the number of compute nodes from 1,024 to 82,944 and measured the computational time. We observed a good weak-scaling property for our cerebellar network model. Using all 82,944 nodes, we succeeded in simulating a human-scale cerebellum for the first time, although the simulation was 578 times slower than the wall clock time. These results suggest that the K computer is already capable of creating a simulation of a human-scale cerebellar model with the aid of the MONET simulator.

Prediction signals in the cerebellum: beyond supervised motor learning

While classical views of cerebellar learning have suggested that this structure predominantly operates according to an error-based supervised learning rule to refine movements, emerging evidence suggests that the cerebellum may also harness a wider range of learning rules to contribute to a variety of behaviors, including cognitive processes. Together, such evidence points to a broad role for cerebellar circuits in generating and testing predictions about movement, reward, and other non-motor operations. However, this expanded view of cerebellar processing also raises many new questions about how such apparent diversity of function arises from a structure with striking homogeneity. Hence, this review will highlight both current evidence for predictive cerebellar circuit function that extends beyond the classical view of error-driven supervised learning, as well as open questions that must be addressed to unify our understanding cerebellar circuit function.

Neural Evidence of the Cerebellum as a State Predictor

We here provide neural evidence that the cerebellar circuit can predict future inputs from present outputs, a hallmark of an internal forward model. Recent computational studies hypothesize that the cerebellum performs state prediction known as a forward model. To test the forward-model hypothesis, we analyzed activities of 94 mossy fibers (inputs to the cerebellar cortex), 83 Purkinje cells (output from the cerebellar cortex to dentate nucleus), and 73 dentate nucleus cells (cerebellar output) in the cerebro-cerebellum, all recorded from a monkey performing step-tracking movements of the right wrist. We found that the firing rates of one population could be reconstructed as a weighted linear sum of those of preceding populations. We then went on to investigate if the current outputs of the cerebellum (dentate cells) could predict the future inputs of the cerebellum (mossy fibers). The firing rates of mossy fibers at time t + t₁ could be well reconstructed from as a weighted sum of firing rates of dentate cells at time t, thereby proving that the dentate activities contained predictive information about the future inputs. The average goodness-of-fit (R²) decreased moderately from 0.89 to 0.86 when t₁ was increased from 20 to 100 ms, hence indicating that the prediction is able to compensate the latency of sensory feedback. The linear equations derived from the firing rates resembled those of a predictor known as Kalman filter composed of prediction and filtering steps. In summary, our analysis of cerebellar activities supports the forward-model hypothesis of the cerebellum.

Climbing Fibers Provide Graded Error Signals in Cerebellar Learning

The cerebellum plays a critical role in coordinating and learning complex movements. Although its importance has been well recognized, the mechanisms of learning remain hotly debated. According to the classical cerebellar learning theory, depression of parallel fiber synapses instructed by error signals from climbing fibers, drives cerebellar learning. The uniqueness of long-term depression (LTD) in cerebellar learning has been challenged by evidence showing multi-site synaptic plasticity. In Purkinje cells, long-term potentiation (LTP) of parallel fiber synapses is now well established and it can be achieved with or without climbing fiber signals, making the role of climbing fiber input more puzzling. The central question is how individual Purkinje cells extract global errors based on climbing fiber input. Previous data seemed to demonstrate that climbing fibers are inefficient instructors, because they were thought to carry “binary” error signals to individual Purkinje cells, which significantly constrains the efficiency of cerebellar learning in several regards. In recent years, new evidence has challenged the traditional view of “binary” climbing fiber responses, suggesting that climbing fibers can provide graded information to efficiently instruct individual Purkinje cells to learn. Here we review recent experimental and theoretical progress regarding modulated climbing fiber responses in Purkinje cells. Analog error signals are generated by the interaction of varying climbing fibers inputs with simultaneous other synaptic input and with firing states of targeted Purkinje cells. Accordingly, the calcium signals which trigger synaptic plasticity can be graded in both amplitude and spatial range to affect the learning rate and even learning direction. We briefly discuss how these new findings complement the learning theory and help to further our understanding of how the cerebellum works.

Classical conditioning drives learned reward prediction signals in climbing fibers across the lateral cerebellum

Classical models of cerebellar learning posit that climbing fibers operate according to a supervised learning rule to instruct changes in motor output by signaling the occurrence of movement errors. However, cerebellar output is also associated with non-motor behaviors, and recently with modulating reward association pathways in the VTA. To test how the cerebellum processes reward related signals in the same type of classical conditioning behavior typically studied to evaluate reward processing in the VTA and striatum, we have used calcium imaging to visualize instructional signals carried by climbing fibers across the lateral cerebellum in mice before and after learning. We find distinct climbing fiber responses in three lateral cerebellar regions that can each signal reward prediction. These instructional signals are well suited to guide cerebellar learning based on reward expectation and enable a cerebellar contribution to reward driven behaviors, suggesting a broad role for the lateral cerebellum in reward-based learning.

Switching On Depression and Potentiation in the Cerebellum

Long-term depression (LTD) and long-term potentiation (LTP) in the cerebellum are important for motor learning. However, the signaling mechanisms controlling whether LTD or LTP is induced in response to synaptic stimulation remain obscure. Using a unified model of LTD and LTP at the cerebellar parallel fiber-Purkinje cell (PF-PC) synapse, we delineate the coordinated pre- and postsynaptic signaling that determines the direction of plasticity. We show that LTP is the default response to PF stimulation above a well-defined frequency threshold. However, if the calcium signal surpasses the threshold for CaMKII activation, then an ultrasensitive "on switch" activates an extracellular signal-regulated kinase (ERK)-based positive feedback loop that triggers LTD instead. This postsynaptic feedback loop is sustained by another, trans-synaptic, feedback loop that maintains nitric oxide production throughout LTD induction. When full depression is achieved, an automatic "off switch" inactivates the feedback loops, returning the network to its basal state and demarcating the end of the early phase of LTD.

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.

Short-term plasticity at cerebellar granule cell to molecular layer interneuron synapses expands information processing

Information processing by cerebellar molecular layer interneurons (MLIs) plays a crucial role in motor behavior. MLI recruitment is tightly controlled by the profile of short-term plasticity (STP) at granule cell (GC)-MLI synapses. While GCs are the most numerous neurons in the brain, STP diversity at GC-MLI synapses is poorly documented. Here, we studied how single MLIs are recruited by their distinct GC inputs during burst firing. Using slice recordings at individual GC-MLI synapses of mice, we revealed four classes of connections segregated by their STP profile. Each class differentially drives MLI recruitment. We show that GC synaptic diversity is underlain by heterogeneous expression of synapsin II, a key actor of STP and that GC terminals devoid of synapsin II are associated with slow MLI recruitment. Our study reveals that molecular, structural and functional diversity across GC terminals provides a mechanism to expand the coding range of MLIs.

Conversion of Graded Presynaptic Climbing Fiber Activity into Graded Postsynaptic Ca2+ Signals by Purkinje Cell Dendrites

The brain must make sense of external stimuli to generate relevant behavior. We used a combination of in vivo approaches to investigate how the cerebellum processes sensory-related information. We found that the inferior olive encodes contexts of sensory-associated external cues in a graded manner, apparent in the presynaptic activity of their axonal projections (climbing fibers) in the cerebellar cortex. Individual climbing fibers were broadly responsive to different sensory modalities but relayed sensory-related information to the cortex in a lobule-dependent manner. Purkinje cell dendrites faithfully transformed this climbing fiber activity into dendrite-wide Ca²⁺ signals without a direct contribution from the mossy fiber pathway. These results demonstrate that the size of climbing-fiber-evoked Ca²⁺ signals in Purkinje cell dendrites is largely determined by the firing level of climbing fibers. This coding scheme emphasizes the overwhelming role of the inferior olive in generating salient signals useful for instructing plasticity and learning.

Re-evaluating Circuit Mechanisms Underlying Pattern Separation

When animals interact with complex environments, their neural circuits must separate overlapping patterns of activity that represent sensory and motor information. Pattern separation is thought to be a key function of several brain regions, including the cerebellar cortex, insect mushroom body, and dentate gyrus. However, recent findings have questioned long-held ideas on how these circuits perform this fundamental computation. Here, we re-evaluate the functional and structural mechanisms underlying pattern separation. We argue that the dimensionality of the space available for population codes representing sensory and motor information provides a common framework for understanding pattern separation. We then discuss how these three circuits use different strategies to separate activity patterns and facilitate associative learning in the presence of trial-to-trial variability.

Contributions of the Cerebellum for Predictive and Instructional Control of Movement

The cerebellum with its layered structure and stereotyped and conserved connectivity has long puzzled neurobiologists. While it is well established that the cerebellum functions in regulating balance, motor coordination and motor learning, how it achieves these end results has not been very clear. Recent technical advances have made it possible to tease apart the contributions of cerebellar cell types to movement in behaving animals. We review these studies focusing on the three major cerebellar cell types, namely: granule cells, Purkinje neurons and the cells of the deep cerebellar nuclei. Further, we also review our current understanding of cortico-cerebellar and basal ganglia-cerebellar interactions that play vital roles in motor planning and motor learning.

Twitches, Blinks, and Fidgets: Important Generators of Ongoing Neural Activity

Animals and humans continuously engage in small, spontaneous motor actions, such as blinking, whisking, and postural adjustments ("fidgeting"). These movements are accompanied by changes in neural activity in sensory and motor regions of the brain. The frequency of these motions varies in time, is affected by sensory stimuli, arousal levels, and pathology. These fidgeting behaviors can be entrained by sensory stimuli. Fidgeting behaviors will cause distributed, bilateral functional activation in the 0.01 to 0.1 Hz frequency range that will show up in functional magnetic resonance imaging and wide-field calcium neuroimaging studies, and will contribute to the observed functional connectivity among brain regions. However, despite the large potential of these behaviors to drive brain-wide activity, these fidget-like behaviors are rarely monitored. We argue that studies of spontaneous and evoked brain dynamics in awake animals and humans should closely monitor these fidgeting behaviors. Differences in these fidgeting behaviors due to arousal or pathology will "contaminate" ongoing neural activity, and lead to apparent differences in functional connectivity. Monitoring and accounting for the brain-wide activations by these behaviors is essential during experiments to differentiate fidget-driven activity from internally driven neural dynamics.

Inhibition gates supralinear Ca2+ signaling in Purkinje cell dendrites during practiced movements

Motor learning involves neural circuit modifications in the cerebellar cortex, likely through re-weighting of parallel fiber inputs onto Purkinje cells (PCs). Climbing fibers instruct these synaptic modifications when they excite PCs in conjunction with parallel fiber activity, a pairing that enhances climbing fiber-evoked Ca²⁺ signaling in PC dendrites. In vivo, climbing fibers spike continuously, including during movements when parallel fibers are simultaneously conveying sensorimotor information to PCs. Whether parallel fiber activity enhances climbing fiber Ca²⁺ signaling during motor behaviors is unknown. In mice, we found that inhibitory molecular layer interneurons (MLIs), activated by parallel fibers during practiced movements, suppressed parallel fiber enhancement of climbing fiber Ca²⁺ signaling in PCs. Similar results were obtained in acute slices for brief parallel fiber stimuli. Interestingly, more prolonged parallel fiber excitation revealed latent supralinear Ca²⁺ signaling. Therefore, the balance of parallel fiber and MLI input onto PCs regulates concomitant climbing fiber Ca²⁺ signaling.

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.

Recent advances in understanding the mechanisms of cerebellar granule cell development and function and their contribution to behavior

The cerebellum is the focus of an emergent series of debates because its circuitry is now thought to encode an unexpected level of functional diversity. The flexibility that is built into the cerebellar circuit allows it to participate not only in motor behaviors involving coordination, learning, and balance but also in non-motor behaviors such as cognition, emotion, and spatial navigation. In accordance with the cerebellum’s diverse functional roles, when these circuits are altered because of disease or injury, the behavioral outcomes range from neurological conditions such as ataxia, dystonia, and tremor to neuropsychiatric conditions, including autism spectrum disorders, schizophrenia, and attention-deficit/hyperactivity disorder. Two major questions arise: what types of cells mediate these normal and abnormal processes, and how might they accomplish these seemingly disparate functions? The tiny but numerous cerebellar granule cells may hold answers to these questions. Here, we discuss recent advances in understanding how the granule cell lineage arises in the embryo and how a stem cell niche that replenishes granule cells influences wiring when the postnatal cerebellum is injured. We discuss how precisely coordinated developmental programs, gene expression patterns, and epigenetic mechanisms determine the formation of synapses that integrate multi-modal inputs onto single granule cells. These data lead us to consider how granule cell synaptic heterogeneity promotes sensorimotor and non-sensorimotor signals in behaving animals. We discuss evidence that granule cells use ultrafast neurotransmission that can operate at kilohertz frequencies. Together, these data inspire an emerging view for how granule cells contribute to the shaping of complex animal behaviors.

Purkinje Cell Representations of Behavior: Diary of a Busy Neuron

Fundamental for understanding cerebellar function is determining the representations in Purkinje cell activity, the sole output of the cerebellar cortex. Up to the present, the most accurate descriptions of the information encoded by Purkinje cells were obtained in the context of motor behavior and reveal a high degree of heterogeneity of kinematic and performance error signals encoded. The most productive framework for organizing Purkinje cell firing representations is provided by the forward internal model hypothesis. Direct tests of this hypothesis show that individual Purkinje cells encode two different forward models simultaneously, one for effector kinematics and one for task performance. Newer results demonstrate that the timing of simple spike encoding of motor parameters spans an extend interval of up to ±2 seconds. Furthermore, complex spike discharge is not limited to signaling errors, can be predictive, and dynamically controls the information in the simple spike firing to meet the demands of upcoming behavior. These rich, diverse, and changing representations highlight the integrative aspects of cerebellar function and offer the opportunity to generalize the cerebellar computational framework over both motor and non-motor domains.

Concerted Interneuron Activity in the Cerebellar Molecular Layer During Rhythmic Oromotor Behaviors

Molecular layer interneurons (MLIs, stellate and basket cells) of the cerebellar cortex are linked together by chemical and electrical synapses and exert a potent feedforward inhibition on Purkinje cells. The functional role of MLIs during specific motor tasks is uncertain. Here, we used two-photon imaging to study the patterns of activity of neighboring individual MLIs in the Crus II region of awake female mice during two types of oromotor activity, licking and bruxing, using specific expression of the genetically encoded calcium indicator protein GCaMP6s. We found that both stellate and basket cells engaged in synchronized waves of calcium activity during licking and bruxing, with high degrees of correlation among the signals collected in individual MLIs. In contrast, no calcium activity was observed during whisking. MLI activity tended to lag behind the onset of sustained licking episodes, indicating a regulatory action of MLIs during licking. Furthermore, during licking, stellate cell activity was anisotropic: the coordination was constant along the direction of parallel fibers (PFs), but fell off with distance along the orthogonal direction. These results suggest a PF drive for Ca²⁺ signals during licking. In contrast, during bruxing, MLI activity was neither clearly organized spatially nor temporally correlated with oromotor activity. In conclusion, MLI activity exhibits a high degree of correlation both in licking and in bruxing, but spatiotemporal patterns of activity display significant differences for the two types of behavior.SIGNIFICANCE STATEMENT It is known that, during movement, the activity of molecular layer interneurons (MLIs) of the cerebellar cortex is enhanced. However, MLI-MLI interactions are complex because they depend both from excitatory electrical synapses and from potentially inhibitory chemical synapses. Accordingly, the pattern of MLI activity during movement has been unclear. Here, during two oromotor tasks, licking and bruxism, individual neighboring MLIs displayed highly coordinated activity, showing that the positive influences binding MLIs together are predominant. We further find that spatiotemporal patterns differ between licking and bruxing, suggesting that the precise pattern of MLI activity depends on the nature of the motor task.