  In the central nervous systems of animals like pigeons and locusts, neurons
were identified which signal objects approaching the animal on a direct
collision course. Unraveling the neural circuitry for collision avoidance, and
identifying the underlying computational principles, is promising for building
vision-based neuromorphic architectures, which in the near future could find
applications in cars or planes. At the present there is no published model
available for robust detection of approaching objects under real-world
conditions. Here we present a computational architecture for signalling
impending collisions, based on known anatomical data of the locust \emph{lobula
giant movement detector} (LGMD) neuron. Our model shows robust performance even
in adverse situations, such as with approaching low-contrast objects, or with
highly textured and moving backgrounds. We furthermore discuss which components
need to be added to our model to convert it into a full-fledged
real-world-environment collision detector. KEYWORDS: Locust, LGMD, collision
detection, lateral inhibition, diffusion, ON-OFF-pathways, neuronal dynamics,
computer vision, image processing
