Improving Human Action Recognition with Neuronal Competition in Spiking Neural Networks

This study presents an end-to-end spiking pipeline trained with semi-supervised STDP for human action recognition, comprising an unsupervised feature extractor and a supervised classifier. It shows that incorporating neuronal competition in the classifier improves class separation by promoting the learning of various spatio-temporal patterns per class.