This paper studies encoding/decoding function of artificial spiking neurons. First, we investigate basic characteristics of spike-trains of the neurons and fix parameter value that can minimize variation of spike-train length for initial value. Second we consider analog-to-digital encoding based upon spike-interval modulation that is suitable for simple and stable signal detection. Third we present a digital-to-analog decoder in which digital input is applied to switch the base signal of the spiking neuron. The system dynamics can be simplified into simple switched dynamical systems and precise analysis is possible. A simple circuit model is also presented.

This letter considers relationship between cyclic digital-to-analog converters (DACs) and iterated function systems (IFSs). We introduce the cyclic DACs as inverse systems of analog-to-digital converters in terms of one-dimensional maps. We then compare the DACs with a typical example of existing applications of IFSs: chaos game representation for analysis of DNA structures. We also present a simple test circuit of a DAC for Gray decoding based on switched capacitors and confirm the basic operation experimentally.