RT conference output
T1 Event-Based Regression with Spiking Networks
A1 Guerrero Vázquez, Elisa
A1 Quintana Velázquez, Fernando Manuel
A1 Guerrero Lebrero, María de la Paz
A2 Ingeniería Informática
K1 Regression
K1 Spiking Neural Networks
K1 Neuromorphic Software
K1 DVS
AB 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.
PB Ignacio Rojas, Gonzalo Joya, Andreu Catalá
SN 978-3-031-43077-0
YR 2023
FD 2023
LK http://hdl.handle.net/10498/32858
UL http://hdl.handle.net/10498/32858
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 10-may-2026