Contrast sensitivity of insect motion detectors to natural images

Abstract

How do animals regulate self-movement despite large variation in the luminance contrast of the environment? Insects are capable of regulating flight speed based on the velocity of image motion, but the mechanisms for this are unclear. The Hassenstein-Reichardt correlator model and elaborations can accurately predict responses of motion detecting neurons under many conditions but fail to explain the apparent lack of spatial pattern and contrast dependence observed in freely flying bees and flies. To investigate this apparent discrepancy, we recorded intracellularly from horizontal-sensitive (HS) motion detecting neurons in the hoverfly while displaying moving images of natural environments. Contrary to results obtained with grating patterns, we show these neurons encode the velocity of natural images largely independently of the particular image used despite a threefold range of contrast. This invariance in response to natural images is observed in both strongly and minimally motion-adapted neurons but is sensitive to artificial manipulations in contrast. Current models of these cells account for some, but not all, of the observed insensitivity to image contrast. We conclude that fly visual processing may be matched to commonalities between natural scenes, enabling accurate estimates of velocity largely independent of the particular scene.

Wide-field motion tuning in nocturnal hawkmoths

“…Our earlier work has verified that the velocity tuning of insect neurons to spectrally broadband stimuli is predicted well by their tuning to the spatial and temporal frequency of grating patterns (Dror et al 2001), so the measured differences in velocity optima are likely to reflect differences in velocity tuning for natural scenes. One caveat, however, is that the optima for scenes with 1/f statistics, such as natural images, while still predictable, will probably be two to three times higher than those for narrow-band sinusoids, as we have observed for the hoverfly (Straw et al 2008).…”

Honeybees as a Model for the Study of Visually Guided Flight, Navigation, and Biologically Inspired Robotics

“…The speed of the image can then be represented by the relative strengths of the responses across the array of interneurons, just as the wavelength of a light source is represented by the relative strengths of the responses that it elicits in an array of photoreceptors with different spectral sensitivities. Another possibility is that these interneurons, which have so far been probed mainly by using stimuli consisting of sinusoidal gratings moving at a constant velocity, exhibit velocity-sensitive properties when they are exposed to more realistic visual stimuli, such as natural scenes that move at changing velocities (113,256). A third possibility is that image speed is computed by an entirely different set of neurons, in a parallel movement-detecting pathway that mediates the behaviors that require robust measurement of image speed.…”

Gaze characteristics of freely walking blowflies in a goal-directed task

“…Haltere reafferences reflect the yaw rotation velocity of the body and, therefore, could be used to modulate visual responses to yaw rotations in conjunction with reafferences of the prosternal organ, which detects the head orientation in relation to the body (Preuss and Hengstenberg, 1992). Although an efference copy and reafferences from mechanosensors might well be utilized to reduce the impact of self-induced rotations on visual information processing, both mechanisms cannot exactly predict the visual responses of LPTCs to self-rotations: LPTC responses do not only depend on stride-coupled retinal velocities, but also on the spatial frequency content and local contrast of the stimulus Egelhaaf and Borst, 1989;Straw et al, 2008;Warzecha et al, 2000).…”