We study information processing and learning in the cerebellum. Our main experimental approach involves the use of eyelid conditioning as a way to control cerebellar inputs and monitor cerebellar output in vivo. Through behavioral analysis, in vivo recordings and other manipulations such as stimulation and inactivation we try to understand what the cerebellum computes and the mechanisms that implement these computations. We augment these studies with computational approaches that include large-scale computer simulations and mathematical models. The large-scale simulations have been under development for over 25 years. They involve building conductance-based spiking representations of each cerebellar cell type, developing algorithms to interconnect these neurons in ways that represent cerebellar synaptic organization, and testing them using inputs derived from our empirical studies. Current versions involve over one million neurons implemented on GPU-based workstations. These simulations, along with simpler mathematical models when useful, allow us to generate new, empirically testable predictions, to understand our data better and to determine the computational principles that make up cerebellar function. Big questions include how inputs are transformed to improve learning and to implement stimulus-temporal coding required for the well-timed learning the cerebellum mediates. We are also interested in the role of feedback in neural system function and in neural/system adaptations that make learning more efficient and that improve performance in the face of noisy inputs.