Bio-inspired visual attention process using spiking neural networks controlling a camera

Abstract

This study introduces virtual and physical implementations of a bottom-up visual attention mechanism using a spiking neural network (SNN) controlling a camera. The SNN is able to focus simple stimuli of various lengths that appear randomly in the camera's view. This is accomplished with an overt process based on a competitive choice according to a stimulus quadrant location. After focusing a selected stimulus toward the centre of its view, the SNN scans it from one edge to the other. Since the spike train of dedicated neurons reflects the duration of each scan, it allows the extraction of the stimulus length. Upon the completion of a scan, the SNN has the ability to switch to another stimulus. This preliminary work on spatial visual attention intends to be a step toward the study of the concept size learning process in a robotic context.