Rate-Encoding A/D Converter Based on Spiking Neuron Model with Rectangular Wave Threshold Signal

Abstract

This paper proposes and characterizes an A/D converter (ADC) based on a spiking neuron model with a rectangular threshold signal. The neuron repeats an integrate-and-fire process and outputs a superstable spike sequence. The dynamics of this system are closely related to those of rate-encoding ADCs. We propose an ADC system based on the spiking neuron model. We derive a theoretical parameter region in a limited time interval of the digital output sequence. We analyze the conversion characteristics in this region and verify that they retain the monotonic increase and rate encoding of an ADC.