Proceedings of the Annual Meeting of the Cognitive Science Society

The cerebellum is classically described in terms of its role inmotor control. Recent evidence suggests that the cerebellumsupports a wide variety of functions, including timing-relatedcognitive tasks and perceptual prediction. Correspondingly,deciphering cerebellar function may be important to advanceour understanding of cognitive processes. In this paper, webuild a model of eyeblink conditioning, an extensively studiedlow-level function of the cerebellum. Building such a modelis of particular interest, since, as of now, it remains unclearhow exactly the cerebellum manages to learn and reproducethe precise timings observed in eyeblink conditioning that arepotentially exploited by cognitive processes as well. We em-ploy recent advances in large-scale neural network modelingto build a biologically plausible spiking neural network basedon the cerebellar microcircuitry. We compare our simulationresults to neurophysiological data and demonstrate how therecurrent Granule-Golgi subnetwork could generate the dynam-ics representations required for triggering motor trajectoriesin the Purkinje cell layer. Our model is capable of reproduc-ing key properties of eyeblink conditioning, while generatingneurophysiological data that could be experimentally verified.