Cerebellar implementation of movement sequences through feedback

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.

Mesoscale simulations predict the role of synergistic cerebellar plasticity during classical eyeblink conditioning

According to the motor learning theory by Albus and Ito, synaptic depression at the parallel fibre to Purkinje cells synapse (pf-PC) is the main substrate responsible for learning sensorimotor contingencies under climbing fibre control. However, recent experimental evidence challenges this relatively monopolistic view of cerebellar learning. Bidirectional plasticity appears crucial for learning, in which different microzones can undergo opposite changes of synaptic strength (e.g. downbound microzones-more likely depression, upbound microzones-more likely potentiation), and multiple forms of plasticity have been identified, distributed over different cerebellar circuit synapses. Here, we have simulated classical eyeblink conditioning (CEBC) using an advanced spiking cerebellar model embedding downbound and upbound modules that are subject to multiple plasticity rules. Simulations indicate that synaptic plasticity regulates the cascade of precise spiking patterns spreading throughout the cerebellar cortex and cerebellar nuclei. CEBC was supported by plasticity at the pf-PC synapses as well as at the synapses of the molecular layer interneurons (MLIs), but only the combined switch-off of both sites of plasticity compromised learning significantly. By differentially engaging climbing fibre information and related forms of synaptic plasticity, both microzones contributed to generate a well-timed conditioned response, but it was the downbound module that played the major role in this process. The outcomes of our simulations closely align with the behavioural and electrophysiological phenotypes of mutant mice suffering from cell-specific mutations that affect processing of their PC and/or MLI synapses. Our data highlight that a synergy of bidirectional plasticity rules distributed across the cerebellum can facilitate finetuning of adaptive associative behaviours at a high spatiotemporal resolution.

Contiguity in perception: origins in cellular associative computations

Our brains are good at detecting and learning associative structures; according to some linguistic theories, this capacity even constitutes a prerequisite for the development of syntax and compositionality in language and verbalized thought. I will argue that the search for associative motifs in input patterns is an evolutionary old brain function that enables contiguity in sensory perception and orientation in time and space. It has its origins in an elementary material property of cells that is particularly evident at chemical synapses: input-assigned calcium influx that activates calcium sensor proteins involved in memory storage. This machinery for the detection and learning of associative motifs generates knowledge about input relationships and integrates this knowledge into existing networks through updates in connectivity patterns.

Positive feedback of efferent copy via pontine nucleus facilitates cerebellum-mediated associative learning

The cerebellum is critical for motor coordination and learning. However, the role of feedback circuitry in this brain region has not been fully explored. Here, we characterize a nucleo-ponto-cortical feedback pathway in classical delayed eyeblink conditioning (dEBC) of rats. We find that the efference copy is conveyed from the interposed cerebellar nucleus (Int) to cerebellar cortex through pontine nucleus (PN). Inhibiting or exciting the projection from the Int to the PN can decelerate or speed up acquisition of dEBC, respectively. Importantly, we identify two subpopulations of PN neurons (PN1 and PN2) that convey and integrate the feedback signals with feedforward sensory signals. We also show that the feedforward and feedback pathways via different types of PN neurons contribute to the plastic changes and cooperate synergistically to the learning of dEBC. Our results suggest that this excitatory nucleo-ponto-cortical feedback plays a significant role in modulating associative motor learning in cerebellum.

Functional regionalization of the differentiating cerebellar Purkinje cell population occurs in an activity-dependent manner

Introduction

The cerebellum is organized into functional regions each dedicated to process different motor or sensory inputs for controlling different locomotor behaviors. This functional regionalization is prominent in the evolutionary conserved single-cell layered Purkinje cell (PC) population. Fragmented gene expression domains suggest a genetic organization of PC layer regionalization during cerebellum development. However, the establishment of such functionally specific domains during PC differentiation remained elusive.

Methods and results

We show the progressive emergence of functional regionalization of PCs from broad responses to spatially restricted regions in zebrafish by means of in vivo Ca2+-imaging during stereotypic locomotive behavior. Moreover, we reveal that formation of new dendritic spines during cerebellar development using in vivo imaging parallels the time course of functional domain development. Pharmacological as well as cell-type specific optogenetic inhibition of PC neuronal activity results in reduced PC dendritic spine density and an altered stagnant pattern of functional domain formation in the PC layer.

Discussion

Hence, our study suggests that functional regionalization of the PC layer is driven by physiological activity of maturing PCs themselves.

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.

Influence of data sampling methods on the representation of neural spiking activity in vivo

In vivo single-unit recordings distinguish the basal spiking properties of neurons in different experimental settings and disease states. Here, we examined over 300 spike trains recorded from Purkinje cells and cerebellar nuclei neurons to test whether data sampling approaches influence the extraction of rich descriptors of firing properties. Our analyses included neurons recorded in awake and anesthetized control mice, and disease models of ataxia, dystonia, and tremor. We find that recording duration circumscribes overall representations of firing rate and pattern. Notably, shorter recording durations skew estimates for global firing rate variability toward lower values. We also find that only some populations of neurons in the same mouse are more similar to each other than to neurons recorded in different mice. These data reveal that recording duration and approach are primary considerations when interpreting task-independent single neuron firing properties. If not accounted for, group differences may be concealed or exaggerated.

From Analog to Digital Computing: Is Homo sapiens’ Brain on Its Way to Become a Turing Machine?

The abstract basis of modern computation is the formal description of a finite state machine, the Universal Turing Machine, based on manipulation of integers and logic symbols. In this contribution to the discourse on the computer-brain analogy, we discuss the extent to which analog computing, as performed by the mammalian brain, is like and unlike the digital computing of Universal Turing Machines. We begin with ordinary reality being a permanent dialog between continuous and discontinuous worlds. So it is with computing, which can be analog or digital, and is often mixed. The theory behind computers is essentially digital, but efficient simulations of phenomena can be performed by analog devices; indeed, any physical calculation requires implementation in the physical world and is therefore analog to some extent, despite being based on abstract logic and arithmetic. The mammalian brain, comprised of neuronal networks, functions as an analog device and has given rise to artificial neural networks that are implemented as digital algorithms but function as analog models would. Analog constructs compute with the implementation of a variety of feedback and feedforward loops. In contrast, digital algorithms allow the implementation of recursive processes that enable them to generate unparalleled emergent properties. We briefly illustrate how the cortical organization of neurons can integrate signals and make predictions analogically. While we conclude that brains are not digital computers, we speculate on the recent implementation of human writing in the brain as a possible digital path that slowly evolves the brain into a genuine (slow) Turing machine.

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.

Ventromedial Thalamus-Projecting DCN Neurons Modulate Associative Sensorimotor Responses in Mice

The deep cerebellar nuclei (DCN) integrate various inputs to the cerebellum and form the final cerebellar outputs critical for associative sensorimotor learning. However, the functional relevance of distinct neuronal subpopulations within the DCN remains poorly understood. Here, we examined a subpopulation of mouse DCN neurons whose axons specifically project to the ventromedial (Vm) thalamus (DCN^Vm neurons), and found that these neurons represent a specific subset of DCN units whose activity varies with trace eyeblink conditioning (tEBC), a classical associative sensorimotor learning task. Upon conditioning, the activity of DCN^Vm neurons signaled the performance of conditioned eyeblink responses (CRs). Optogenetic activation and inhibition of the DCN^Vm neurons in well-trained mice amplified and diminished the CRs, respectively. Chemogenetic manipulation of the DCN^Vm neurons had no effects on non-associative motor coordination. Furthermore, optogenetic activation of the DCN^Vm neurons caused rapid elevated firing activity in the cingulate cortex, a brain area critical for bridging the time gap between sensory stimuli and motor execution during tEBC. Together, our data highlights DCN^Vm neurons' function and delineates their kinematic parameters that modulate the strength of associative sensorimotor responses.

Mid-lateral cerebellar complex spikes encode multiple independent reward-related signals during reinforcement learning

Although the cerebellum has been implicated in simple reward-based learning recently, the role of complex spikes (CS) and simple spikes (SS), their interaction and their relationship to complex reinforcement learning and decision making is still unclear. Here we show that in a context where a non-human primate learned to make novel visuomotor associations, classifying CS responses based on their SS properties revealed distinct cell-type specific encoding of the probability of failure after the stimulus onset and the non-human primate's decision. In a different context, CS from the same cerebellar area also responded in a cell-type and learning independent manner to the stimulus that signaled the beginning of the trial. Both types of CS signals were independent of changes in any motor kinematics and were unlikely to instruct the concurrent SS activity through an error based mechanism, suggesting the presence of context dependent, flexible, multiple independent channels of neural encoding by CS and SS. This diversity in neural information encoding in the mid-lateral cerebellum, depending on the context and learning state, is well suited to promote exploration and acquisition of wide range of cognitive behaviors that entail flexible stimulus-action-reward relationships but not necessarily motor learning.

Diversity and dynamism in the cerebellum

The past several years have brought revelations and paradigm shifts in research on the cerebellum. Historically viewed as a simple sensorimotor controller with homogeneous architecture, the cerebellum is increasingly implicated in cognitive functions. It possesses an impressive diversity of molecular, cellular and circuit mechanisms, embedded in a dynamic, recurrent circuit architecture. Recent insights about the diversity and dynamism of the cerebellum provide a roadmap for the next decade of cerebellar research, challenging some old concepts, reinvigorating others and defining major new research directions.

Cortico-cerebellar interactions during goal-directed behavior

Preparatory activity is observed across multiple interconnected brain regions before goal-directed movement. Preparatory activity reflects discrete activity states representing specific future actions. It is unclear how this activity is mediated by multi-regional interactions. Recent evidence suggests that the cerebellum, classically associated with fine motor control, contributes to preparatory activity in the neocortex. We review recent advances and offer perspective on the function of cortico-cerebellar interactions during goal-directed behavior. We propose that the cerebellum learns to facilitate transitions between neocortical activity states. Transitions between activity states enable flexible and appropriately timed behavioral responses.

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.

The thalamus integrates the macrosystems of the brain to facilitate complex, adaptive brain network dynamics

The human brain is a complex, adaptive system comprised of billions of cells with trillions of connections. The interactions between the elements of the system oppose this seemingly limitless capacity by constraining the system's dynamic repertoire, enforcing distributed neural states that balance integration and differentiation. How this trade-off is mediated by the brain, and how the emergent, distributed neural patterns give rise to cognition and awareness, remains poorly understood. Here, I argue that the thalamus is well-placed to arbitrate the interactions between distributed neural assemblies in the cerebral cortex. Different classes of thalamocortical connections are hypothesized to promote either feed-forward or feedback processing modes in the cerebral cortex. This activity can be conceptualized as emerging dynamically from an evolving attractor landscape, with the relative engagement of distinct distributed circuits providing differing constraints over the manner in which brain state trajectories change over time. In addition, inputs to the distinct thalamic populations from the cerebellum and basal ganglia, respectively, are proposed to differentially shape the attractor landscape, and hence, the temporal evolution of cortical assemblies. The coordinated engagement of these neural macrosystems is then shown to share key characteristics with prominent models of cognition, attention and conscious awareness. In this way, the crucial role of the thalamus in mediating the distributed, multi-scale network organization of the central nervous system can be related to higher brain function.

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.

Intrinsic Excitability Increase in Cerebellar Purkinje Cells after Delay Eye-Blink Conditioning in Mice

Cerebellar-based learning is thought to rely on synaptic plasticity, particularly at synaptic inputs to Purkinje cells. Recently, however, other complementary mechanisms have been identified. Intrinsic plasticity is one such mechanism, and depends in part on the downregulation of calcium-dependent SK-type K+ channels, which contribute to a medium-slow afterhyperpolarization (AHP) after spike bursts, regulating membrane excitability. In the hippocampus, intrinsic plasticity plays a role in trace eye-blink conditioning; however, corresponding excitability changes in the cerebellum in associative learning, such as in trace or delay eye-blink conditioning, are less well studied. Whole-cell patch-clamp recordings were obtained from Purkinje cells in cerebellar slices prepared from male mice ∼48 h after they learned a delay eye-blink conditioning task. Over a period of repeated training sessions, mice received either paired trials of a tone coterminating with a periorbital shock (conditioning) or trials in which these stimuli were randomly presented in an unpaired manner (pseudoconditioning). Purkinje cells from conditioned mice show a significantly reduced AHP after trains of parallel fiber stimuli and after climbing fiber evoked complex spikes. The number of spikelets in the complex spike waveform is increased after conditioning. Moreover, we find that SK-dependent intrinsic plasticity is occluded in conditioned, but not pseudoconditioned mice. These findings show that excitability is enhanced in Purkinje cells after delay eye-blink conditioning, and point toward a downregulation of SK channels as a potential underlying mechanism. The observation that this learning effect lasts at least up to 2 d after training shows that intrinsic plasticity regulates excitability in the long term.SIGNIFICANCE STATEMENT Plasticity of membrane excitability ("intrinsic plasticity") has been observed in invertebrate and vertebrate neurons, coinduced with synaptic plasticity or in isolation. Although the cellular phenomenon per se is well established, it remains unclear what role intrinsic plasticity plays in learning and if it even persists long enough to serve functions in engram physiology beyond aiding synaptic plasticity. Here, we demonstrate that cerebellar Purkinje cells upregulate excitability in delay eye-blink conditioning, a form of motor learning. This plasticity is observed 48 h after training and alters synaptically evoked spike firing and integrative properties of these neurons. These findings show that intrinsic plasticity enhances the spike firing output of Purkinje cells and persists over the course of days.

SK2 channels in cerebellar Purkinje cells contribute to excitability modulation in motor-learning-specific memory traces

Neurons store information by changing synaptic input weights. In addition, they can adjust their membrane excitability to alter spike output. Here, we demonstrate a role of such "intrinsic plasticity" in behavioral learning in a mouse model that allows us to detect specific consequences of absent excitability modulation. Mice with a Purkinje-cell-specific knockout (KO) of the calcium-activated K+ channel SK2 (L7-SK2) show intact vestibulo-ocular reflex (VOR) gain adaptation but impaired eyeblink conditioning (EBC), which relies on the ability to establish associations between stimuli, with the eyelid closure itself depending on a transient suppression of spike firing. In these mice, the intrinsic plasticity of Purkinje cells is prevented without affecting long-term depression or potentiation at their parallel fiber (PF) input. In contrast to the typical spike pattern of EBC-supporting zebrin-negative Purkinje cells, L7-SK2 neurons show reduced background spiking but enhanced excitability. Thus, SK2 plasticity and excitability modulation are essential for specific forms of motor learning.

Sensorimotor Coding of Vermal Granule Neurons in the Developing Mammalian Cerebellum

The vermal cerebellum is a hub of sensorimotor integration critical for postural control and locomotion, but the nature and developmental organization of afferent information to this region have remained poorly understood in vivo Here, we use in vivo two-photon calcium imaging of the vermal cerebellum in awake behaving male and female mice to record granule neuron responses to diverse sensorimotor cues targeting visual, auditory, somatosensory, and motor domains. Use of an activity-independent marker revealed that approximately half (54%) of vermal granule neurons were activated during these recordings. A multikernel linear model distinguished the relative influences of external stimuli and co-occurring movements on neural responses, indicating that, among the subset of activated granule neurons, locomotion (44%-56%) and facial air puffs (50%) were more commonly and reliably encoded than visual (31%-32%) and auditory (19%-28%) stimuli. Strikingly, we also uncover populations of granule neurons that respond differentially to voluntary and forced locomotion, whereas other granule neurons in the same region respond similarly to locomotion in both conditions. Finally, by combining two-photon calcium imaging with birth date labeling of granule neurons via in vivo electroporation, we find that early- and late-born granule neurons convey similarly diverse sensorimotor information to spatially distinct regions of the molecular layer. Collectively, our findings elucidate the nature and developmental organization of sensorimotor information in vermal granule neurons of the developing mammalian brain.SIGNIFICANCE STATEMENT Cerebellar granule neurons comprise over half the neurons in the brain, and their coding properties have been the subject of theoretical and experimental interest for over a half-century. In this study, we directly test long-held theories about encoding of sensorimotor stimuli in the cerebellum and compare the in vivo coding properties of early- and late-born granule neurons. Strikingly, we identify populations of granule neurons that differentially encode voluntary and forced locomotion and find that, although the birth order of granule neurons specifies the positioning of their parallel fiber axons, both early- and late-born granule neurons convey a functionally diverse sensorimotor code. These findings constitute important conceptual advances in understanding the principles underlying cerebellar circuit development and function.

Corollary Discharge Signals in the Cerebellum

The cerebellum is known to make movements fast, smooth, and accurate. Many hypotheses emphasize the role of the cerebellum in computing learned predictions important for sensorimotor calibration and feedforward control of movements. Hypotheses of the computations performed by the cerebellum in service of motor control borrow heavily from control systems theory, with models that frequently invoke copies of motor commands, called corollary discharge. This review describes evidence for corollary discharge inputs to the cerebellum and highlights the hypothesized roles for this information in cerebellar motor-related computations. Insights into the role of corollary discharge in motor control, described here, are intended to inform the exciting but still untested roles of corollary discharge in cognition, perception, and thought control relevant in psychiatric disorders.

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.

How the cerebellum learns to build a sequence

Rabbits can learn the biological analogue of a simple recursive function by relying only on the neurons of the cerebellum.