In this paper a chaotic spiking neuron is presented and its response characteristics to various periodic inputs are analyzed. A return map which can analytically describe the dynamics of the neuron is derived. Using the map, it is theoretically shown that a set of neurons can encode various periodic inputs into a set of spike-trains in such a way that a spike density of a summation of the spike-trains can approximate the waveform of the input. Based on the theoretical results, some potential applications of the presented neuron are also discussed. Using a prototype circuit, typical encoding functions of the neuron are confirmed by experimental measurements.