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In the central nervous systems of animals like pigeons and locusts, neuronswere identified which signal objects approaching the animal on a directcollision course. Unraveling the neural circuitry for collision avoidance, andidentifying the underlying computational principles, is promising for buildingvision-based neuromorphic architectures, which in the near future could findapplications in cars or planes. At the present there is no published modelavailable for robust detection of approaching objects under real-worldconditions. Here we present a computational architecture for signallingimpending collisions, based on known anatomical data of the locust \emph{lobulagiant movement detector} (LGMD) neuron. Our model shows robust performance evenin adverse situations, such as with approaching low-contrast objects, or withhighly textured and moving backgrounds. We furthermore discuss which componentsneed to be added to our model to convert it into a full-fledgedreal-world-environment collision detector. KEYWORDS: Locust, LGMD, collisiondetection, lateral inhibition, diffusion, ON-OFF-pathways, neuronal dynamics,computer vision, image processing

A neural model of the locust visual system for detection of object approaches with real-world scenes