An Experimental Analysis of Reservoir Parameters of the Echo State Queueing Network Model

Abstract

During the last years, there has been a growing interest in the Reservoir Computing (RC) paradigm. Recently, a new RC model was presented under the name of Echo State Queueing Networks (ESQN). This model merges ideas from Queueing Theory and one of the two pioneering RC techniques, Echo State Networks. In a RC model there is a dynamical system called reservoir which serves to expand the input data into a larger space. This expansion can enhance the linear separability of the data. In the case of ESQN, the reservoir is a Recurrent Neural Network composed of spiking neurons which fire positive and negative signals. Unlike other RC models, an analysis of the dynamics behavior of the ESQN system is still to be done. In this work, we present an experimental analysis of these dynamics. In particular, we study the impact of the spectral radius of the reservoir in system stability. In our experiments, we use a range of benchmark time series data.