In recent years, event cameras, also known as silicon retinas, have emerged as a novel paradigm for capturing visual information in a sparse and asynchronous way, offering significant advantages in applications such as robotics and computer vision. However, to exploit the full potential of event cameras the development of innovative algorithms is required. The most effective learning algorithms developed for event cameras typically use Spiking Neural Networks (SNNs) for an event-by-event processing or start by transforming events into dense representations, which are subsequently processed using conventional Convolutional Neural Networks (CNNs). Nonetheless, the SNNs don't provide a back-propagation learning mechanism and the CNNs result in the loss of both the inherent sparsity and the fine-grained temporal resolution of events imposing a substantial computational load and latency introduction.