Consensus Paper: Cerebellum and Reward.

Cerebellum is a key-structure for the modulation of motor, cognitive, social and affective functions, contributing to automatic behaviours through interactions with the cerebral cortex, basal ganglia and spinal cord. The predictive mechanisms used by the cerebellum cover not only sensorimotor functions but also reward-related tasks. Cerebellar circuits appear to encode temporal difference error and reward prediction error. From a chemical standpoint, cerebellar catecholamines modulate the rate of cerebellar-based cognitive learning, and mediate cerebellar contributions during complex behaviours. Reward processing and its associated emotions are tuned by the cerebellum which operates as a controller of adaptive homeostatic processes based on interoceptive and exteroceptive inputs. Lobules VI-VII/areas of the vermis are candidate regions for the cortico-subcortical signaling pathways associated with loss aversion and reward sensitivity, together with other nodes of the limbic circuitry. There is growing evidence that the cerebellum works as a hub of regional dysconnectivity across all mood states and that mental disorders involve the cerebellar circuitry, including mood and addiction disorders, and impaired eating behaviors where the cerebellum might be involved in longer time scales of prediction as compared to motor operations. Cerebellar patients exhibit aberrant social behaviour, showing aberrant impulsivity/compulsivity. The cerebellum is a master-piece of reward mechanisms, together with the striatum, ventral tegmental area (VTA) and prefrontal cortex (PFC). Critically, studies on reward processing reinforce our view that a fundamental role of the cerebellum is to construct internal models, perform predictions on the impact of future behaviour and compare what is predicted and what actually occurs.

Attenuation of noise correlations in the transformation from the frontal eye field to movement.

Correlated activity between neurons can cause variability in behavior across trials, as trial-by-trial cofluctuations can propagate downstream through the motor system. The extent to which correlated activity affects behavior depends on the properties of the translation of the population activity into movement. A major hurdle in studying the effects of noise correlations on behavior is that in many cases this translation is unknown. Previous research has overcome this by using models that make strong assumptions about the coding of motor variables. We developed a novel method that estimates the contribution of correlations to behavior with minimal assumptions. Our method partitions noise correlations into correlations that are expressed in a specific behavior, termed behavior-related correlations, and correlations that are not. We applied this method to study the relationship between noise correlations in the frontal eye field (FEF) and pursuit eye movements. We defined a distance metric between the pursuit behavior on different trials. Based on this metric, we used a shuffling approach to estimate pursuit-related correlations. Although the correlations were partially linked to variability in the eye movements, even the most constrained shuffle strongly attenuated the correlations. Thus, only a small fraction of FEF correlations is expressed in behavior. We used simulations to validate our approach, show that it captures behavior-related correlations, and demonstrate its generalizability in different models. We show that the attenuation of correlated activity through the motor pathway could stem from the interplay between the structure of the correlations and the decoder of FEF activity.NEW & NOTEWORTHY The effect of noise correlations on neural computations has been studied extensively. However, the degree to which correlations affect downstream areas remains unknown. Here, we take advantage of precise measurement of eye movement behavior to estimate the degree to which correlated variability between neurons in the frontal eye field (FEF) affects subsequent behavior. To achieve this, we developed a novel shuffling-based method and verified it using different models of the FEF.

Organization of reward and movement signals in the basal ganglia and cerebellum.

The basal ganglia and the cerebellum are major subcortical structures in the motor system. The basal ganglia have been cast as the reward center of the motor system, whereas the cerebellum is thought to be involved in adjusting sensorimotor parameters. Recent findings of reward signals in the cerebellum have challenged this dichotomous view. To compare the basal ganglia and the cerebellum directly, we recorded from oculomotor regions in both structures from the same monkeys. We partitioned the trial-by-trial variability of the neurons into reward and eye-movement signals to compare the coding across structures. Reward expectation and movement signals were the most pronounced in the output structure of the basal ganglia, intermediate in the cerebellum, and the smallest in the input structure of the basal ganglia. These findings suggest that reward and movement information is sharpened through the basal ganglia, resulting in a higher signal-to-noise ratio than in the cerebellum.

Cerebellar climbing fibers encode expected reward size.

Climbing fiber inputs to the cerebellum encode error signals that instruct learning. Recently, evidence has accumulated to suggest that the cerebellum is also involved in the processing of reward. To study how rewarding events are encoded, we recorded the activity of climbing fibers when monkeys were engaged in an eye movement task. At the beginning of each trial, the monkeys were cued to the size of the reward that would be delivered upon successful completion of the trial. Climbing fiber activity increased when the monkeys were presented with a cue indicating a large reward, but not a small reward. Reward size did not modulate activity at reward delivery or during eye movements. Comparison between climbing fiber and simple spike activity indicated different interactions for coding of movement and reward. These results indicate that climbing fibers encode the expected reward size and suggest a general role of the cerebellum in associative learning beyond error correction.