New journal publication: “Exploiting the stimuli encoding scheme of evolving Spiking Neural Networks for stream learning”

It is a great pleasure to announce that our last research on Spiking Neural Networks and Stream Learning. Jesús López Lobo, Izaskun Oregi and Javier Del Ser, in collaboration with Albert Bifet, have recently published this research in the high impact Q1 JCR scientific journal: Neural Networks.

Summary: Stream data processing has lately gained momentum with the arrival of new Big Data scenarios and applications dealing with continuously produced information flows. Unfortunately, traditional machine learning algorithms are not prepared to tackle the specific challenges imposed by data stream processing, such as the need for learning incrementally, limited memory and processing time requirements, and adaptation to non-stationary data, among others. To face these paradigms, Spiking Neural Networks have emerged as one of the most promising stream learning techniques, capable of efficiently addressing many of these challenges. Interestingly, these networks resort to a particular population encoding scheme – Gaussian Receptive Fields – to transform the incoming stimuli into temporal spikes. The study presented in this work sheds lights on the predictive potential of this encoding scheme.

You can enjoy an open access to the online publication during a few days: https://lnkd.in/dm9yrxs

DOI: https://doi.org/10.1016/j.neunet.2019.11.021