Flying insects, such as flies or bees, rely on consistent information regarding the

depth structure of the environment when performing their flight maneuvers in cluttered natural

environments. These behaviors include avoiding collisions, approaching targets or spatial

navigation. Insects are thought to obtain depth information visually from the retinal image

displacements (“optic flow”) during translational ego-motion. Optic flow in the insect visual

system is processed by a mechanism that can be modeled by correlation-type elementary motion

detectors (EMDs). However, it is still an open question how spatial information can be extracted

reliably from the responses of the highly contrast- and pattern-dependent EMD responses, especially

if the vast range of light intensities encountered in natural environments is taken into account.

This question will be addressed here by systematically modeling the peripheral visual system of

flies, including various adaptive mechanisms. Different model variants of the peripheral visual

system were stimulated with image sequences that mimic the panoramic visual input during

translational ego-motion in various natural environments, and the resulting peripheral signals were

fed into an array of EMDs. We characterized the influence of each peripheral computational unit on

the representation of spatial information in the EMD responses. Our model simulations reveal that

information about the overall light level needs to be eliminated from the EMD input as is

accomplished under light-adapted conditions in the insect peripheral visual system. The response

characteristics of large monopolar cells (LMCs) resemble that of a band-pass filter, which reduces

the contrast dependency of EMDs strongly, effectively enhancing the representation of the nearness

of objects and, especially, of their contours. We furthermore show that local brightness adaptation

of photoreceptors allows for spatial vision under a wide range of dynamic light

conditions.