A cerebellar adaptation to uncertain inputs

Discrimination training affects stimulus generalization in mice during Pavlovian eyeblink conditioning

The delicate balance between discrimination and generalization of responses is crucial for survival in our ever-changing environment. In particular, it is important to understand how stimulus discrimination affects the level of stimulus generalization. For example, when we use non-differential training for Pavlovian eyeblink conditioning to investigate generalization of cerebellar-related eyelid motor responses, we find generalization effects on amount, amplitude and timing of the conditioned responses. However, it is unknown what the generalization effects are following differential training. We trained mice to close their eyelids to a 10 kHz tone with an air-puff as the reinforcing stimulus (CS+), while alternatingly exposing them to a tone frequency of either 4 kHz, 9 kHz or 9.5 kHz without the air-puff (CS-) during the training blocks. We tested the generalization effects during the expression of the responses after the training period with tones ranging from 2 kHz to 20 kHz. Our results show that the level of generalization tended to positively correlate with the difference between the CS+ and the CS- training stimuli. These effects of generalization were found for the probability, amplitude but not for the timing of the conditioned eyelid responses. These data indicate the specificity of the generalization effects following differential versus non-differential training, highlighting the relevance of discrimination learning for stimulus generalization.

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.

Emergence of syntax and word prediction in an artificial neural circuit of the cerebellum

The cerebellum, interconnected with the cerebral neocortex, plays a vital role in human-characteristic cognition such as language processing, however, knowledge about the underlying circuit computation of the cerebellum remains very limited. To gain a better understanding of the computation underlying cerebellar language processing, we developed a biologically constrained cerebellar artificial neural network (cANN) model, which implements the recently identified cerebello-cerebellar recurrent pathway. We found that while cANN acquires prediction of future words, another function of syntactic recognition emerges in the middle layer of the prediction circuit. The recurrent pathway of the cANN was essential for the two language functions, whereas cANN variants with further biological constraints preserved these functions. Considering the uniform structure of cerebellar circuitry across all functional domains, the single-circuit computation, which is the common basis of the two language functions, can be generalized to fundamental cerebellar functions of prediction and grammar-like rule extraction from sequences, that underpin a wide range of cerebellar motor and cognitive functions. This is a pioneering study to understand the circuit computation of human-characteristic cognition using biologically-constrained ANNs.

Accessible and reliable neurometric testing in humans using a smartphone platform

Tests of human brain circuit function typically require fixed equipment in lab environments. We have developed a smartphone-based platform for neurometric testing. This platform, which uses AI models like computer vision, is optimized for at-home use and produces reproducible, robust results on a battery of tests, including eyeblink conditioning, prepulse inhibition of acoustic startle response, and startle habituation. This approach provides a scalable, universal resource for quantitative assays of central nervous system function.

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.

Stimulus Generalization in Mice during Pavlovian Eyeblink Conditioning

Here, we investigate stimulus generalization in a cerebellar learning paradigm, called eyeblink conditioning. Mice were conditioned to close their eyes in response to a 10-kHz tone by repeatedly pairing this tone with an air puff to the eye 250 ms after tone onset. After 10 consecutive days of training, when mice showed reliable conditioned eyelid responses to the 10-kHz tone, we started to expose them to tones with other frequencies, ranging from 2 to 20 kHz. We found that mice had a strong generalization gradient, whereby the probability and amplitude of conditioned eyelid responses gradually decreases depending on the dissimilarity with the 10-kHz tone. Tones with frequencies closest to 10 kHz evoked the most and largest conditioned eyelid responses and each step away from the 10-kHz tone resulted in fewer and smaller conditioned responses (CRs). In addition, we found that tones with lower frequencies resulted in CRs that peaked earlier after tone onset compared with those to tones with higher frequencies. Together, our data show prominent generalization patterns in cerebellar learning. Since the known function of cerebellum is rapidly expanding from pure motor control to domains that include cognition, reward-learning, fear-learning, social function, and even addiction, our data imply generalization controlled by cerebellum in all these domains.

Feedback inhibition underlies new computational functions of cerebellar interneurons

The function of a feedback inhibitory circuit between cerebellar Purkinje cells and molecular layer interneurons (MLIs) was defined by combining optogenetics, neuronal activity recordings both in cerebellar slices and in vivo, and computational modeling. Purkinje cells inhibit a subset of MLIs in the inner third of the molecular layer. This inhibition is non-reciprocal, short-range (less than 200 μm) and is based on convergence of one to two Purkinje cells onto MLIs. During learning-related eyelid movements in vivo, the activity of a subset of MLIs progressively increases as Purkinje cell activity decreases, with Purkinje cells usually leading the MLIs. Computer simulations indicate that these relationships are best explained by the feedback circuit from Purkinje cells to MLIs and that this feedback circuit plays a central role in making cerebellar learning efficient.

50 Years Since the Marr, Ito, and Albus Models of the Cerebellum

Fifty years have passed since David Marr, Masao Ito, and James Albus proposed seminal models of cerebellar functions. These models share the essential concept that parallel-fiber-Purkinje-cell synapses undergo plastic changes, guided by climbing-fiber activities during sensorimotor learning. However, they differ in several important respects, including holistic versus complementary roles of the cerebellum, pattern recognition versus control as computational objectives, potentiation versus depression of synaptic plasticity, teaching signals versus error signals transmitted by climbing-fibers, sparse expansion coding by granule cells, and cerebellar internal models. In this review, we evaluate different features of the three models based on recent computational and experimental studies. While acknowledging that the three models have greatly advanced our understanding of cerebellar control mechanisms in eye movements and classical conditioning, we propose a new direction for computational frameworks of the cerebellum, that is, hierarchical reinforcement learning with multiple internal models.

Resistance, vulnerability and resilience: A review of the cognitive cerebellum in aging and neurodegenerative diseases

In the context of neurodegeneration and aging, the cerebellum is an enigma. Genetic markers of cellular aging in cerebellum accumulate more slowly than in the rest of the brain, and it generates unknown factors that may slow or even reverse neurodegenerative pathology in animal models of Alzheimer's Disease (AD). Cerebellum shows increased activity in early AD and Parkinson's disease (PD), suggesting a compensatory function that may mitigate early symptoms of neurodegenerative pathophysiology. Perhaps most notably, different parts of the brain accumulate neuropathological markers of AD in a recognized progression and generally, cerebellum is the last brain region to do so. Taken together, these data suggest that cerebellum may be resistant to certain neurodegenerative mechanisms. On the other hand, in some contexts of accelerated neurodegeneration, such as that seen in chronic traumatic encephalopathy (CTE) following repeated traumatic brain injury (TBI), the cerebellum appears to be one of the most susceptible brain regions to injury and one of the first to exhibit signs of pathology. Cerebellar pathology in neurodegenerative disorders is strongly associated with cognitive dysfunction. In neurodegenerative or neurological disorders associated with cerebellar pathology, such as spinocerebellar ataxia, cerebellar cortical atrophy, and essential tremor, rates of cognitive dysfunction, dementia and neuropsychiatric symptoms increase. When the cerebellum shows AD pathology, such as in familial AD, it is associated with earlier onset and greater severity of disease. These data suggest that when neurodegenerative processes are active in the cerebellum, it may contribute to pathological behavioral outcomes. The cerebellum is well known for comparing internal representations of information with observed outcomes and providing real-time feedback to cortical regions, a critical function that is disturbed in neuropsychiatric disorders such as intellectual disability, schizophrenia, dementia, and autism, and required for cognitive domains such as working memory. While cerebellum has reciprocal connections with non-motor brain regions and likely plays a role in complex, goal-directed behaviors, it has proven difficult to establish what it does mechanistically to modulate these behaviors. Due to this lack of understanding, it's not surprising to see the cerebellum reflexively dismissed or even ignored in basic and translational neuropsychiatric literature. The overarching goals of this review are to answer the following questions from primary literature: When the cerebellum is affected by pathology, is it associated with decreased cognitive function? When it is intact, does it play a compensatory or protective role in maintaining cognitive function? Are there theoretical frameworks for understanding the role of cerebellum in cognition, and perhaps, illnesses characterized by cognitive dysfunction? Understanding the role of the cognitive cerebellum in neurodegenerative diseases has the potential to offer insight into origins of cognitive deficits in other neuropsychiatric disorders, which are often underappreciated, poorly understood, and not often treated.

Cerebellar Contribution to Preparatory Activity in Motor Neocortex

In motor neocortex, preparatory activity predictive of specific movements is maintained by a positive feedback loop with the thalamus. Motor thalamus receives excitatory input from the cerebellum, which learns to generate predictive signals for motor control. The contribution of this pathway to neocortical preparatory signals remains poorly understood. Here, we show that, in a virtual reality conditioning task, cerebellar output neurons in the dentate nucleus exhibit preparatory activity similar to that in anterolateral motor cortex prior to reward acquisition. Silencing activity in dentate nucleus by photoactivating inhibitory Purkinje cells in the cerebellar cortex caused robust, short-latency suppression of preparatory activity in anterolateral motor cortex. Our results suggest that preparatory activity is controlled by a learned decrease of Purkinje cell firing in advance of reward under supervision of climbing fiber inputs signaling reward delivery. Thus, cerebellar computations exert a powerful influence on preparatory activity in motor neocortex.

•

Similar activity in dentate nucleus (DN) and ALM cortex prior to reward acquisition

•

Silencing DN activity selectively suppresses preparatory activity in ALM

•

Preparatory activity likely controlled by learned decrease in Purkinje cell firing

•

Dynamics of preparatory activity imply reward time prediction from external cues