Court Hull

Climbing fibre (CF) inputs to Purkinje cells (PCs) instruct plasticity and learning in the cerebellum^1-3. Paradoxically, CFs also excite molecular layer interneurons (MLIs)^4,5, a cell type that inhibits PCs and can restrict plasticity and learning^6,7. However, two types of MLI with opposing influences have recently been identified: MLI1s inhibit PCs, reduce dendritic calcium signals and suppress plasticity of granule cell to PC synapses^2,6-9, whereas MLI2s inhibit MLI1s and disinhibit PCs⁸. To determine how CFs can activate MLIs without also suppressing the PC calcium signals necessary for plasticity and learning, we investigated the specificity of CF inputs onto MLIs. Serial electron microscopy reconstructions indicate that CFs contact both MLI subtypes without making conventional synapses, but more CFs contact each MLI2 through more sites with larger contact areas. Slice experiments indicate that CFs preferentially excite MLI2s through glutamate spillover^4,5. In agreement with these anatomical and slice experiments, in vivo Neuropixels recordings show that spontaneous CF activity excites MLI2s, inhibits MLI1s and disinhibits PCs. By contrast, learning-related sensory stimulation produces more complex responses, driving convergent CF and granule cell inputs that could either activate or suppress MLI1s. This balance was robustly shifted towards MLI1 suppression when CFs were synchronously active, in turn elevating the PC dendritic calcium signals necessary for long-term depression. These data provide mechanistic insight into why CF synchrony can be highly effective at inducing cerebellar learning^2,3 by revealing a critical disinhibitory circuit that allows CFs to act through MLIs to enhance PC dendritic calcium signals necessary for plasticity.

Axon collaterals of type 1 molecular layer interneurons (MLI1s) contribute to pinceaux that engulf the initial segments of Purkinje cell (PC) axons and generate extracellular signals that ephaptically inhibit PCs. Here we show that a remarkably large number of MLI1s (~50) contribute to each pinceau, and that this allows networks of synchronously firing MLI1s to use ephaptic signals to control the precise timing of PC firing in vivo.

Spinocerebellar ataxia type 7 (SCA7) is a genetic neurodegenerative disorder caused by a CAG-polyglutamine repeat expansion. Purkinje cells (PCs) are central to the pathology of ataxias, but their low abundance in the cerebellum underrepresents their transcriptomes in sequencing assays. To address this issue, we developed a PC enrichment protocol and sequenced individual nuclei from mice and patients with SCA7. Single-nucleus RNA sequencing in SCA7-266Q mice revealed dysregulation of cell identity genes affecting glia and PCs. Specifically, genes marking zebrin-II PC subtypes accounted for the highest proportion of DEGs in symptomatic SCA7-266Q mice. These transcriptomic changes in SCA7-266Q mice were associated with increased numbers of inhibitory synapses as quantified by immunohistochemistry and reduced spiking of PCs in acute brain slices. Dysregulation of zebrin-II cell subtypes was the predominant signal in PCs of SCA7-266Q mice and was associated with the loss of zebrin-II striping in the cerebellum at motor symptom onset. We furthermore demonstrated zebrin-II stripe degradation in additional mouse models of polyglutamine ataxia and observed decreased zebrin-II expression in the cerebella of patients with SCA7. Our results suggest that a breakdown of zebrin subtype regulation is a shared pathological feature of polyglutamine ataxias.

Classical models of cerebellar computation posit that climbing fibers (CFs) operate according to supervised learning rules, correcting movements by signaling the occurrence of motor errors. However, recent findings suggest that in some behaviors, CF activity can exhibit features that resemble the instructional signals necessary for reinforcement learning, namely reward prediction errors (rPEs). Despite these initial observations, many key properties of reward-related CF responses remain unclear, thus limiting our understanding of how they operate to guide cerebellar learning. Here, we have measured the postsynaptic responses of CFs onto cerebellar Purkinje cells using two-photon calcium imaging to test how they respond to learned stimuli that either do or do not predict reward. We find that CFs can develop generalized responses to similar cues of the same modality, regardless of whether they are reward predictive. However, this generalization depends on temporal context, and does not extend across sensory modalities. Further, learned CF responses are flexible, and can be rapidly updated according to new reward contingencies. Together these results suggest that CFs can generate learned, reward-predictive responses that flexibly adapt to the current environment in a context-sensitive manner.

Summary: The cerebellum plays a key role in motor coordination and learning. In contrast with classical supervised learning models, recent work has revealed that CFs can signal reward-predictive information in some behaviors. This raises the question of whether CFs may also operate according to the principles of reinforcement learning. To test how CFs operate during reward-guided behavior, and evaluate the role of reward-related CF activity in learning, we have measured CF responses in Purkinje cells of the lateral cerebellum during a Pavlovian task using 2-photon calcium imaging. Specifically, we have performed multi-stimulus experiments to determine whether CF activity meets the requirements of a reward prediction error (rPE) signal for transfer from an unexpected reward to a reward-predictive cue. We find that once CF activity is transferred to a conditioned stimulus, and there is no longer a response to reward, CFs cannot generate learned responses to a second conditioned stimulus that carries the same reward prediction. In addition, by expressing the inhibitory opsin GtACR2 in neurons of the inferior olive, and optically inhibiting these neurons across behavioral training at the time of unexpected reward, we find that the transfer of CF signals to the conditioned stimulus is impaired. Moreover, this optogenetic inhibition also impairs learning, resulting in a deficit in anticipatory lick timing. Together, these results indicate that CF signals can exhibit several characteristics in common with the rPEs that have been observed during reinforcement learning, and that the cerebellum can harness these reward-related learning signals to generate accurately timed motor behavior.

Astrotactin 2 (ASTN2) is a transmembrane neuronal protein highly expressed in the cerebellum that functions in receptor trafficking and modulates cerebellar Purkinje cell (PC) synaptic activity. Individuals with ASTN2 mutations exhibit neurodevelopmental disorders, including autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), learning difficulties, and language delay. To provide a genetic model for the role of the cerebellum in ASD-related behaviors and study the role of ASTN2 in cerebellar circuit function, we generated global and PC-specific conditional Astn2 knockout (KO and cKO, respectively) mouse lines. Astn2 KO mice exhibit strong ASD-related behavioral phenotypes, including a marked decrease in separation-induced pup ultrasonic vocalization calls, hyperactivity, repetitive behaviors, altered behavior in the three-chamber test, and impaired cerebellar-dependent eyeblink conditioning. Hyperactivity and repetitive behaviors are also prominent in Astn2 cKO animals, but they do not show altered behavior in the three-chamber test. By Golgi staining, Astn2 KO PCs have region-specific changes in dendritic spine density and filopodia numbers. Proteomic analysis of Astn2 KO cerebellum reveals a marked upregulation of ASTN2 family member, ASTN1, a neuron–glial adhesion protein. Immunohistochemistry and electron microscopy demonstrate a significant increase in Bergmann glia volume in the molecular layer of Astn2 KO animals. Electrophysiological experiments indicate a reduced frequency of spontaneous excitatory postsynaptic currents (EPSCs), as well as increased amplitudes of both spontaneous EPSCs and inhibitory postsynaptic currents in the Astn2 KO animals, suggesting that pre- and postsynaptic components of synaptic transmission are altered. Thus, ASTN2 regulates ASD-like behaviors and cerebellar circuit properties.

Astrotactin 2 (ASTN2) is a transmembrane neuronal protein highly expressed in the cerebellum that functions in receptor trafficking and modulates cerebellar Purkinje cell (PC) synaptic activity. We recently reported a family with a paternally inherited intragenic ASTN2 duplication with a range of neurodevelopmental disorders, including autism spectrum disorder (ASD), learning difficulties, and speech and language delay. To provide a genetic model for the role of the cerebellum in ASD-related behaviors and study the role of ASTN2 in cerebellar circuit function, we generated global and PC-specific conditional Astn2 knockout (KO and cKO, respectively) mouse lines. Astn2 KO mice exhibit strong ASD-related behavioral phenotypes, including a marked decrease in separation-induced pup ultrasonic vocalization calls, hyperactivity and repetitive behaviors, altered social behaviors, and impaired cerebellar-dependent eyeblink conditioning. Hyperactivity and repetitive behaviors were also prominent in Astn2 cKO animals. By Golgi staining, Astn2 KO PCs have region-specific changes in dendritic spine density and filopodia numbers. Proteomic analysis of Astn2 KO cerebellum reveals a marked upregulation of ASTN2 family member, ASTN1, a neuron-glial adhesion protein. Immunohistochemistry and electron microscopy demonstrate a large increase in Bergmann glia volume in the molecular layer of Astn2 KO animals. Electrophysiological experiments indicate a reduced frequency of spontaneous excitatory postsynaptic currents (EPSCs), as well as increased amplitudes of both spontaneous EPSCs and inhibitory postsynaptic currents (IPSCs) in the Astn2 KO animals, suggesting that pre- and postsynaptic components of synaptic transmission are altered. Thus, ASTN2 regulates ASD-like behaviors and cerebellar circuit properties.

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.

High-density probes allow electrophysiological recordings from many neurons simultaneously across entire brain circuits but don’t reveal cell type. Here, we develop a strategy to identify cell types from extracellular recordings in awake animals, revealing the computational roles of neurons with distinct functional, molecular, and anatomical properties. We combine optogenetic activation and pharmacology using the cerebellum as a testbed to generate a curated ground-truth library of electrophysiological properties for Purkinje cells, molecular layer interneurons, Golgi cells, and mossy fibers. We train a semi-supervised deep-learning classifier that predicts cell types with greater than 95% accuracy based on waveform, discharge statistics, and layer of the recorded neuron. The classifier’s predictions agree with expert classification on recordings using different probes, in different laboratories, from functionally distinct cerebellar regions, and across animal species. Our classifier extends the power of modern dynamical systems analyses by revealing the unique contributions of simultaneously-recorded cell types during behavior.