The olivocerebellar system plays a central role in motor learning, crucially contributing to the coordination, precision and accurate timing of movements. The system is formed by Purkinje cells (PC), the Deep Cerebellar Nucleus (DCN) and Inferior Olive (IO). Its input activate the PC which produces simple (SS) and complex spikes (CS). The latter induced by the IO. The oscillatory nature of the production of CS seems to play a role in motor control and motor timing. In addition, CS modulate the parallel fiber-Purkinje cell (PF-PC) synaptic plasticity. IO synchrony can help the system learn timing with the PF input as the timing context. Furthermore, the level of synchronization of the coupled IO cells could determine the function of complex spikes, implying that the olivocerebellum is capable of switching between modes of learning by changing the level of synchronization. In this paper we introduced a novel computational model to analyze the role of coupling of the IO and long-term plasticity at the PF-PC synapse in the response of the olivocerebellar loop. It is a resonant system formed by a detailed IO model and Integrate-and-Fire PC and DCN models, with physiologically observed firing frequencies. The IO cells are modeled as coupled oscillators and plasticity is incorporated through a timing dependent specialization of Hebbian learning (Spike-Timing Dependent Plasticity or STDP). Two different simulations are performed both for the coupled and uncoupled scenarios. The STDP is used only in the second type of simulation. Both types of simulations use the same noisy input, which is applied twice during the second type of simulation. The second half of the latter is interpreted as the response of a trained loop. Results show that in the presence of coupling the correlation of the firing rates distribution decreases. This indicates that for the coupled scenario PCs are separating the patterns, while for the uncoupled scenarios the noise is encoded more robustly. Furthermore, a drop in the noise current (inhibiting the PC) leads to an IO spike at about 100 milliseconds later. After training, however, the loop recognizes a drop in the noise and depresses the synapses avoiding an increase in the firing rate of the PC. This effect is more noticeable in the coupled scenario. In conclusion, the model shows that plasticity can lead to learning, and that this process is more efficient for a coupled system.