Oscillations of the membrane potential are a prominent feature of several neurons in the central and peripheral nervous system. Evidences exist that neurons combine intrinsic oscillations with stochastic influences to obtain sensitive encoding. Here we investigated the responses of oscillatory neurons with respect to different activity states and noise levels as well as different measures of the responses. For that we used a computational approach and studied systematically the responses of a physiologically motivated neuronal oscillator model. With subthreshold activation of the model, noise mediates oscillation-coupled spike generation. In this situation noise-tuning results in maximum curves for the coherence of oscillations and spikes (coherence resonance) whereas the mean spike frequencies increase monotonically. In contrast, with suprathreshold activation, noise suppresses oscillation-coupled spike generation. In this situation, noise tuning leads to a minimum curve for the mean spike frequency whereas the coherence measure decreases monotonically. In conclusion, our study shows interesting effects on neuronal responses depending on the level of stimulation and noise intensity. In addition, the study demonstrates how such dynamical behaviors might fulfill different purposes depending on the
actual encoding strategy used.