Augmented and Virtual Reality (AR/VR) technologies are gaining popularity to improve healthcare professionals training, with precise eye tracking playing a crucial role in enhancing performance. However, these systems need to be both low-latency and low-power to operate in real-time scenarios on resource-constrained devices. Event-based cameras can be employed to address these requirements, as they offer energy-efficient, high temporal resolution data with minimal battery drain. However, their sparse data format necessitates specialized processing algorithms. In this work, we propose a data preprocessing technique that improves the performance of nonrecurrent Deep Neural Networks (DNNs) for pupil position estimation. With this approach, we integrate over time - with a leakage factor - multiple time surfaces of events, so that the input data is enriched with information from past events. Additionally, in order to better distinguish between recent and old information, we generate multiple memory channels characterized by different leakage/forgetting rates. These memory channels are fed to well-known non-recurrent neural estimators to predict the position of the pupil. As an example, by using time surfaces only and feeding them to a MobileNet-V3L model to track the pupil in DVS recordings, we achieve a P10 accuracy (Euclidean error lower than ten pixels) of 85.40%, whether by using memory channels we achieve a P10 accuracy of 94.37% with a negligible time overhead.