Neuronal representation and extraction of spatial information are essential for behavioral

control. For flying insects, a plausible way to gain spatial information is to exploit distancedependent

optic flow that is generated during translational self-motion. Optic flow is computed

by arrays of local motion detectors retinotopically arranged in the second neuropile

layer of the insect visual system. These motion detectors have adaptive response characteristics,

i.e. their responses to motion with a constant or only slowly changing velocity

decrease, while their sensitivity to rapid velocity changes is maintained or even increases.

We analyzed by a modeling approach how motion adaptation affects signal representation

at the output of arrays of motion detectors during simulated flight in artificial and natural 3D

environments. We focused on translational flight, because spatial information is only contained

in the optic flow induced by translational locomotion. Indeed, flies, bees and other

insects segregate their flight into relatively long intersaccadic translational flight sections

interspersed with brief and rapid saccadic turns, presumably to maximize periods of translation

(80% of the flight). With a novel adaptive model of the insect visual motion pathway we

could show that the motion detector responses to background structures of cluttered environments

are largely attenuated as a consequence of motion adaptation, while responses to

foreground objects stay constant or even increase. This conclusion even holds under the

dynamic flight conditions of insects.