Analyzing the dynamics of the simultaneous feature and parameter optimization of an evolving Spiking Neural Network

This study investigates the characteristics of the Quantum-inspired Spiking Neural Network (QiSNN) feature selection and classification framework. The self-adapting nature of QiSNN due to the simultaneous optimization of network parameters and feature subsets represents a highly desirable characteristic in the context of machine learning and knowledge discovery. In this paper, the evolution of the parameters and feature subsets is studied in detail. The goal of this analysis is a comprehensive understanding of all parameters involved in QiSNN and some practical guidelines for using the method in future research and applications. We also highlight the role of the employed neural encoding technique along with its impact on the classification abilities of QiSNN.