Quantized Spiking Neuron With A/D Conversion Functions

Abstract

This brief presents a simple artificial spiking neuron and proposes its application to an A/D converter. Depending on the initial state, which is an analog input, the neuron can generate spike trains having various spike position patterns. Based on spike position modulation, the spike train can be symbolized by a digital output. As a result, the analog input can be encoded into the digital output. Adjusting a reconfigurable parameter, the neuron can realize various encodings such as binary and Gray encodings. This brief also proposes a simple reconfigurable implementation circuit and experimentally confirms typical A/D conversion functions