Links Between Single-Trial Changes and Learning Rate in Eyelid Conditioning

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.

Cannabinoids modulate associative cerebellar learning via alterations in behavioral state

Cannabinoids are notorious and profound modulators of behavioral state. In the brain, endocannabinoids act via Type 1-cannabinoid receptors (CB1) to modulate synaptic transmission and mediate multiple forms of synaptic plasticity. CB1 knockout (CB1KO) mice display a range of behavioral phenotypes, in particular hypoactivity and various deficits in learning and memory, including cerebellum-dependent delay eyeblink conditioning. Here we find that the apparent effects of CB1 deletion on cerebellar learning are not due to direct effects on CB1-dependent plasticity, but rather, arise as a secondary consequence of altered behavioral state. Hypoactivity of CB1KO mice accounts for their impaired eyeblink conditioning across both animals and trials. Moreover, learning in these mutants is rescued by walking on a motorized treadmill during training. Finally, cerebellar granule-cell-specific CB1KOs exhibit normal eyeblink conditioning, and both global and granule-cell-specific CB1KOs display normal cerebellum-dependent locomotor coordination and learning. These findings highlight the modulation of behavioral state as a powerful independent means through which individual genes contribute to complex behaviors.

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.

Bidirectional short-term plasticity during single-trial learning of cerebellar-driven eyelid movements in mice

The brain is constantly monitoring its own performance, using error signals to trigger mechanisms of plasticity that help improve future behavior. Indeed, adaptive changes in behavior have been observed after a single error trial in many learning tasks, including cerebellum-dependent eyeblink conditioning. Here, we demonstrate that the plasticity underlying single-trial learning during eyeblink conditioning in mice is bidirectionally regulated by positive and negative prediction errors, has an ephemeral effect on behavior (decays in <1 min), and can be triggered in the absence of errors in performance. We suggest that these three properties of single-trial learning may be particularly useful for driving mechanisms of motor adaptation that can achieve optimal performance in the face of environmental disturbances with a fast timescale.

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.

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.

A cerebellar adaptation to uncertain inputs

Noise and variability are inherent and unavoidable features of neural processing. Despite this physiological challenge, brain systems function well, suggesting the existence of adaptations that cope with noise. We report a novel adaptation that the cerebellum implements to maintain correct responses in the face of ambiguous inputs. We found that under these conditions, the cerebellum used a probabilistic binary choice: Although the probability of behavioral response gradually increased or decreased depending on the degree of similarity between current and trained inputs, the size of response remained constant. That way the cerebellum kept responses adaptive to trained input corrupted by noise while minimizing false responses to novel stimuli. Recordings and analysis of Purkinje cells activity showed that the binary choice is made in the cerebellar cortex. Results from large-scale simulation suggest that internal feedback from cerebellar nucleus back to cerebellar cortex plays a critical role in implementation of binary choice.

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

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

Adaptive Acceleration of Visually Evoked Smooth Eye Movements in Mice

The optokinetic response (OKR) consists of smooth eye movements following global motion of the visual surround, which suppress image slip on the retina for visual acuity. The effective performance of the OKR is limited to rather slow and low-frequency visual stimuli, although it can be adaptably improved by cerebellum-dependent mechanisms. To better understand circuit mechanisms constraining OKR performance, we monitored how distinct kinematic features of the OKR change over the course of OKR adaptation, and found that eye acceleration at stimulus onset primarily limited OKR performance but could be dramatically potentiated by visual experience. Eye acceleration in the temporal-to-nasal direction depended more on the ipsilateral floccular complex of the cerebellum than did that in the nasal-to-temporal direction. Gaze-holding following the OKR was also modified in parallel with eye-acceleration potentiation. Optogenetic manipulation revealed that synchronous excitation and inhibition of floccular complex Purkinje cells could effectively accelerate eye movements in the nasotemporal and temporonasal directions, respectively. These results collectively delineate multiple motor pathways subserving distinct aspects of the OKR in mice and constrain hypotheses regarding cellular mechanisms of the cerebellum-dependent tuning of movement acceleration.

SIGNIFICANCE STATEMENT

Although visually evoked smooth eye movements, known as the optokinetic response (OKR), have been studied in various species for decades, circuit mechanisms of oculomotor control and adaptation remain elusive. In the present study, we assessed kinematics of the mouse OKR through the course of adaptation training. Our analyses revealed that eye acceleration at visual-stimulus onset primarily limited working velocity and frequency range of the OKR, yet could be dramatically potentiated during OKR adaptation. Potentiation of eye acceleration exhibited different properties between the nasotemporal and temporonasal OKRs, indicating distinct visuomotor circuits underlying the two. Lesions and optogenetic manipulation of the cerebellum provide constraints on neural circuits mediating visually driven eye acceleration and its adaptation.

Movement Rate Is Encoded and Influenced by Widespread, Coherent Activity of Cerebellar Molecular Layer Interneurons

Inhibition from molecular layer interneurons (MLIs) is thought to play an important role in cerebellar function by sharpening the precision of Purkinje cell spike output. Yet the coding features of MLIs during behavior are poorly understood. To study MLI activity, we used in vivo Ca²⁺ imaging in head-fixed mice during the performance of a rhythmic motor behavior, licking during water consumption. MLIs were robustly active during lick-related movement across a lobule-specific region of the cerebellum showing high temporal correspondence within their population. Average MLI Ca²⁺ activity strongly correlated with movement rate but not to the intentional, or unexpected, adjustment of lick position or to sensory feedback that varied with task condition. Chemogenetic suppression of MLI output reduced lick rate and altered tongue movements, indicating that activity of these interneurons not only encodes temporal aspects of movement kinematics but also influences motor outcome pointing to an integral role in online control of rhythmic behavior.SIGNIFICANCE STATEMENT The cerebellum helps fine-tune coordinated motor actions via signaling from projection neurons called Purkinje cells. Molecular layer interneurons (MLIs) provide powerful inhibition onto Purkinje cells, but little is understood about how this inhibitory circuit is engaged during behavior or what type of information is transmitted through these neurons. Our work establishes that MLIs in the lateral cerebellum are broadly activated during movement with calcium activity corresponding to movement rate. We also show that suppression of MLI output slows and disorganizes the precise movement pattern. Therefore, MLIs are an important circuit element in the cerebellum allowing for accurate motor control.