The project aims to implement neuromorphic multi-layer networks of leaky integrate-and-fire (LIF) neurons in cascade to a motorized event-based camera (DAVIS, DVS, https://inivation.com/technology/), to obtain artificial replicas of the early stages of an active vision system. Testing the models will involve the assessment of multiple and varying parameters captured under real-life and adaptive conditions. At functional level, the system will (1) consider the neural resources required to account for a range of linear/nonlinear early visual processes, and (2) provide the inference engines for relating the resulting visual representations to performance on psychophysical tasks. The visual performance of the resulting silicon model will be comparatively assessed with that of a typical human observer. The objective is twofold: on the one hand, we contribute a deeper understanding of visual processes, especially about predicting how early computation may reverberate through the sensory pathways eventually contributing to functional vision. On the other hand, we contribute to the definition of a new generation of perceptual machines to be used in robotics and in general in newly developed Artificial Intelligence systems.