Bayesian optimization of spiking neural network parameters to solving the time series classification task

Abstract

This study contains the application of spiking neural networks to time series classification task. Because of the lack of mathematical framework for such biologically inspired neural networks, this study tries to solve hyperparameter optimization task with the help of surrogate models. To define classification task quality metric that measures separability index based on Fisher’s discriminant ratio is used.