Trends in Neurosciences
What the cerebellum computes
Section snippets
From a model system to modeling a system
Although initially viewed as a model of associative learning, it is increasingly clear that Pavlovian eyelid conditioning is an especially useful means of studying cerebellar computation and its underlying mechanisms. This transition has occurred with the steady accumulation of evidence to indicate that eyelid conditioning engages the cerebellum in a straightforward and conceptually useful manner. In a typical experiment, the paired presentation of a cue stimulus such as a tone and a
Feedforward control and learning
Making accurate movements requires sensory input. In principle, this input can be applied to motor commands in two general ways: feedback and feedforward. This distinction can be illustrated using control of room temperature. A standard thermostat controls room temperature via feedback. It activates the heating–cooling system when the thermometer (sensory input) signals that room temperature differs from the target setting. Thus, feedback about current performance is used to generate output
Temporally specific learning
The demands on this learning are made more complex by the likelihood that not all sensory signals will predict errors with the same time delay. The time-dependent properties of eyelid conditioning show that cerebellar learning is well suited to solve this problem.
In the thermostat analogy, some sensors might be activated when a small window is opened, and others activated by opening a larger window. On a cold day, these signals would predict the same error (room too cold), but with different
Mechanisms of temporally specific learning
Analysis of cerebellum-dependent behaviors such as eyelid conditioning and adaptation of the vestibulo–ocular reflex (VOR) has revealed a great deal about how cerebellar neurons and synapses accomplish this computation 8, 14. Evidence from such studies indicates that plasticity in both the cerebellar cortex and cerebellar nuclei is involved 15, 16. For learning that increases cerebellar output, climbing fiber inputs drive the induction of plasticity in the cortex. Recent evidence indicates that
Concluding remarks
Pavlovian eyelid conditioning has emerged as a valuable tool for studying cerebellar computation. Eyelid conditioning engages the cerebellum in a relatively direct way: mossy and climbing fibers, respectively, convey information about the tone and the puff to the cerebellum, and cerebellar output drives the expression of the conditioned response. Because of this straightforward mapping, the behavioral properties of eyelid conditioning are a reasonable first approximation of the input–output
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