title: Nitric oxide regulates input specificity of long-term depression and context dependence of cerebellar learning.
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.
authors.affiliation: ATR Computational Neuroscience Laboratories, Seika, Kyoto, Japan. ogahide@atr.jp
authors.forename: Hideaki
authors.initials: H
authors.lastname: Ogasawara
authors.forename: Tomokazu
authors.initials: T
authors.lastname: Doi
authors.forename: Kenji
authors.initials: K
authors.lastname: Doya
authors.forename: Mitsuo
authors.initials: M
authors.lastname: Kawato
mesh_terms: D000200:Action Potentials / Q000502:physiology; D001245:Association Learning; D002531:Cerebellum / Q000502:physiology*; D003198:Computer Simulation; D005246:Feedback / Q000502:physiology; D036881:Long-Term Synaptic Depression; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009569:Nitric Oxide / Q000378:metabolism*; D011689:Purkinje Cells / Q000502:physiology*; D012680:Sensitivity and Specificity; D015398:Signal Transduction / Q000502:physiology
chemical_list: D009569:Nitric Oxide