Biomimetic visual detection based on insect neurobiology

Abstract

With a visual system that accounts for as much as 30% of the lifted mass, flying insects such as dragonflies and hoverflies invest more in vision than any other animal. Impressive visual performance is subserved by a surprisingly simple visual system. In a typical insect eye, between 2,000 and 30,000 pixels in the image are analyzed by fewer than 200,000 neurons in underlying neural circuits. The combination of sophisticated visual processing with an approachable level of complexity has made the insect visual system a leading model for biomimetic approaches to computer vision. Much neurobiological research has focused on neural circuits used for detection of moving patterns (e.g. optical flow during flight) and moving targets (e.g. prey). Research from several labs has led to great advances in our understanding of the neural mechanisms involved, and has spawned neuromorphic hardware based on key processes identified in neurobiological experiments. Despite its attractions, the highly non-linear nature of several key stages in insect visual processing presents a challenge to understanding. I will describe examples of adaptive elements of neural circuits in the fly visual system which analyze the direction and velocity of wide-field optical flow patterns and the result of experiments that suggest that these non-linearities may contribute to robust responses to `natural<right single quotation mark> image motion.