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The Journal of Neurophysiology Vol. 81 No. 1 January 1999,
pp. 277-287
Copyright ©1999 The American Physiological Society
Institute of General Physiology and Istituto Nazionale Fisica della Materia, Pavia Unit, I-27100 Pavia, Italy
D'Angelo, Egidio, Paola Rossi, Simona Armano, and Vanni Taglietti. Evidence for NMDA and mGlu receptor-dependent long-term potentiation of mossy fiber-granule cell transmission in rat cerebellum. J. Neurophysiol. 81: 277-287, 1999. Long-term potentiation (LTP) is a form of synaptic plasticity that can be revealed at numerous hippocampal and neocortical synapses following high-frequency activation of N-methyl-D-aspartate (NMDA) receptors. However, it was not known whether LTP could be induced at the mossy fiber-granule cell relay of cerebellum. This is a particularly interesting issue because theories of the cerebellum do not consider or even explicitly negate the existence of mossy fiber-granule cell synaptic plasticity. Here we show that high-frequency mossy fiber stimulation paired with granule cell membrane depolarization (
40 mV) leads to LTP of granule cell excitatory postsynaptic currents (EPSCs). Pairing with a relatively hyperpolarized potential (60 mV) or in the presence of NMDA receptor blockers [5-amino-D-phosphonovaleric acid (APV) and 7-chloro-kynurenic acid (7-Cl-Kyn)] prevented LTP, suggesting that the induction process involves a voltage-dependent NMDA receptor activation. Metabotropic glutamate receptors were also involved because blocking them with (+)-
-methyl-4-carboxyphenyl-glycine (MCPG) prevented potentiation. At the cytoplasmic level, EPSC potentiation required a Ca2+ increase and protein kinase C (PKC) activation. Potentiation was expressed through an increase in both the NMDA and non-NMDA receptor-mediated current and by an NMDA current slowdown, suggesting that complex mechanisms control synaptic efficacy during LTP. LTP at the mossy fiber-granule cell synapse provides the cerebellar network with a large reservoir for memory storage, which may be needed to optimize pattern recognition and, ultimately, cerebellar learning and computation.
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