Nitric Oxide Regulates Input Specificity of Long-Term Depression and Context Dependence of Cerebellar Learning

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Abstract
Recent studies have shown that multiple internal models are acquired in the cerebellum and that these can be switched under a given context of behavior. It has been proposed that long-term depression (LTD) of parallel fiber (PF)–Purkinje cell (PC) synapses forms the cellular basis of cerebellar learning, and that the presynaptically synthesized messenger nitric oxide (NO) is a crucial “gatekeeper” for LTD. Because NO diffuses freely to neighboring synapses, this volume learning is not input-specific and brings into question the biological significance of LTD as the basic mechanism for efficient supervised learning. To better characterize the role of NO in cerebellar learning, we simulated the sequence of electrophysiological and biochemical events in PF–PC LTD by combining established simulation models of the electrophysiology, calcium dynamics, and signaling pathways of the PC. The results demonstrate that the local NO concentration is critical for induction of LTD and for its input specificity. Pre- and postsynaptic coincident firing is not sufficient for a PF–PC synapse to undergo LTD, and LTD is induced only when a sufficient amount of NO is provided by activation of the surrounding PFs. On the other hand, above-adequate levels of activity in nearby PFs cause accumulation of NO, which also allows LTD in neighboring synapses that were not directly stimulated, ruining input specificity. These findings lead us to propose the hypothesis that NO represents the relevance of a given context and enables context-dependent selection of internal models to be updated. We also predict sparse PF activity in vivo because, otherwise, input specificity would be lost. The cerebellum is essential for coordinated movements. The skills for executing such movements are acquired in modules of the cerebellum, and the appropriate modules in which to store the skill for a certain movement are selected according to the environment, or the context, where the movement is made. We are interested in the molecular mechanisms that enable context-dependent cerebellar learning. In search of the key molecules, we combined established simulation models of Purkinje cells, the only output neurons in the cerebellar cortex, and constructed a new model. Using computer simulation, we found that nitric oxide is likely to have a pivotal role in context-dependent learning. Our simulation also provides insights into how sparse sensory information is coded in the cerebellar cortex. These findings have led us to propose the experimentally testable hypothesis that the relevance of a given context to learning modules is represented by the concentration of nitric oxide.