Event-Based Regression with Spiking Networks

Abstract

Spiking Neuron Networks (SNNs), also known as the third generation of neural networks, are inspired from natural computing in the brain and recent advances in neuroscience. SNNs can overcome the computational power of neural networks made of threshold or sigmoidal units. Recent advances on event-based devices along with their great power, considering the time factor, make SNNs a cutting-edge priority research objective. SNNs have been used mainly for classification problems, but their application to regression tasks remains challenging due to the complexity of training with continuous output data. In the literature we can find some first approximations in regression, specifically, for problems of a single variable of continuous values. This work deals with the analysis of the behavior of SNNs as predictors of multivariable continuous values. For this, a data set based on events has been generated from a bouncing ball and an event-based camera. The goal is to predict the next position of the ball over time.