Grid Search Optimization of Novel SNN-ESN Classifier on a Supercomputer Platform

Abstract

This work is demonstrating the use of a supercomputer platform to optimise hyper-parameters of a proposed by the team novel SNN-ESN computational model, that combines a brain template of spiking neurons in a spiking neural network (SNN) for feature extraction and an Echo State Network (ESN) for dynamic data series classification. A case study problem and data are used to illustrate the functionalities of the SNN-ESN. The overall SNN-ESN classifier has several hyper-parameters that are subject to refinement, such as: spiking threshold, duration of the refractory period and STDP learning rate for the SNN part; reservoir size, spectral radius of the connectivity matrix and leaking rate for the ESN part. In order to find the optimal hyper-parameter values exhaustive search over all possible combinations within reasonable intervals was performed using supercomputer Avitohol. The resulted optimal parameters led to improved classification accuracy. This work demonstrates the importance of model parameter optimisation using a supercomputer platform, which improves the usability of the proposed SNN-ESN for real-time applications on complex spatio-temporal data.