Rhythmic activities were widely observed in many brain regions. Human EEG recording revealed several frequency modulation of the oscillation reflecting internal brain states such as attentional modulation in visual systems. On the other hand, in vivo intracellular recordings suggested that individual neurons showed persistent membrane fluctuations and global oscillation originated from the activity of the neuronal fluctuations. Furthermore, it was found that some types of neuron showed membrane resonance in their subthreshold level. However, functional roles of the subthreshold resonance in a recurrent neural network are still unknown. Here, we computationally examined the behavior of resonator network driven by external inputs and organized through the spike-timing-dependent plasticity (STDP) under oscillatory background and noise. As a result, it was shown how the resonator network modified its responsiveness depending on frequency modulation and its connectivity through the STDP. Introduction Kang et al. Synaptic Learning of the Resonator Network Interacting with Noise 126 SNE 28(3) – 9/2018 SN Figure 1. Neuronal response to oscillatory inputs. 1 Method 1.1 Neuron model 1.2 Network organization 1.3 Input currents Iapp Iwave Inoise D Istim Iwave Figure 2. Synaptic weights after the STDP learning. 2 Result 2.1 Responsiveness of single neuron to oscillatory inputs 2.2 Synaptic learning through the STDP