A connectionist model for local speed estimation

Models for motion detection with artificial neural networks are inspired in physiological data of simple visual systems. Local speed estimation is a problem that involves more complicated architecture than a single lateral connection. We proposed an extension of these pioneer models. It considers bigger receptive fields that provide a larger zone of influence from receptors, allowing speed estimation on a wide range of velocities. The essence of this neural model is to estimate local speed from comparison between edge detection at the current position and delayed activities of neighboring input receptive fields