We used large-scale computer simulations of eyelid conditioning to investigate how the cerebellum generates and makes use of temporal information. In the simulations the adaptive timing displayed by conditioned responses is mediated by two factors: (1) different sets of granule cells are active at different times during the conditioned stimulus (CS), and (2) responding is not only amplified at reinforced times but also suppressed at unreinforced times during the CS. These factors predict an unusual pattern of responding after partial removal of the cerebellar cortex that was confirmed using small, electrolytic lesions of cerebellar cortex. These results are consistent with timing mechanisms in the cerebellum that are similar to Pavlov's "inhibition of delay" hypothesis.

The hypothesis of cerebellar learning proposes that complex spikes in Purkinje cells engage mechanisms of plasticity in the cerebellar cortex; in turn, changes in the cerebellum depress the simple spike response of Purkinje cells to a given stimulus and cause the adaptive modification of a motor behavior. Although many elements of this hypothesis have been supported by prior experiments, the links between complex spikes, simple spike plasticity, and behavior have not yet been examined simultaneously during the learning process. We now pioneer a trial-by-trial analysis of Purkinje cell responses in awake-behaving monkeys, with results that strongly favor a causal role for complex spikes in the induction of cerebellar plasticity during a simple motor learning task. We show that the presence of a complex spike on one learning trial is linked to a substantial depression of simple spike responses on the subsequent trial, at a time when behavioral learning is expressed.

Climbing fiber inputs to Purkinje cells are thought to play a teaching role by generating the instructive signals that drive cerebellar learning. To investigate how these instructive signals are encoded, we recorded the activity of individual climbing fibers during cerebellar-dependent eyeblink conditioning in mice. Our findings show that climbing fibers signal both the unexpected delivery and the unexpected omission of the periocular airpuff that serves as the instructive signal for eyeblink conditioning. In addition, we report the surprising discovery that climbing fibers activated by periocular airpuffs also respond to stimuli from other sensory modalities, if those stimuli are novel or if they predict that the periocular airpuff is about to be presented. This pattern of climbing fiber activity is strikingly similar to the responses of dopamine neurons during reinforcement learning, which have been shown to encode a particular type of instructive signal known as a temporal difference prediction error.

Purkinje cells (PCs) of the cerebellar cortex are necessary for controlling movement with precision, but a mechanistic explanation of how the activity of these inhibitory neurons regulates motor output is still lacking. We used an optogenetic approach in awake mice to show for the first time that transiently suppressing spontaneous activity in a population of PCs is sufficient to cause discrete movements that can be systematically modulated in size, speed, and timing depending on how much and how long PC firing is suppressed. We further demonstrate that this fine control of movement kinematics is mediated by a graded disinhibition of target neurons in the deep cerebellar nuclei. Our results prove a long-standing model of cerebellar function and provide the first demonstration that suppression of inhibitory signals can act as a powerful mechanism for the precise control of behavior.

Although many functions have been ascribed to the cerebellum, the uniformity of its synaptic organization suggests that a single, characteristic computation may be common to all. Computer simulations are useful in examining this cerebellar computation, as they inherently address function at the level of information processing. Progress is facilitated by factors that make the cerebellum particularly amenable to such analysis. We review progress from two contrasting approaches. Top-down simulations begin with hypotheses about computational mechanisms and then ask how such mechanisms might operate within the cerebellum. Bottom-up simulations attempt to build a representation of the cerebellum that reflects known cellular and synaptic components as accurately as possible. We describe recent advances from these two approaches that are leading to an understanding of what information the cerebellum processes and how its neurons and synapses accomplish this task.