Fly motion vision is based on Reichardt detectors regardless of the signal-to-noise ratio

Abstract

The computational structure of an optimal motion detector was proposed to depend on the signal-to-noise ratio (SNR) of the stimulus: At low SNR, the optimal motion detector should be a correlation or ''Reichardt'' type, whereas at high SNR, the detector would employ a gradient scheme [ Although a large body of experiments supports the Reichardt detector as the processing scheme leading to direction selectivity in fly motion vision, in most of these studies the SNR was rather low. We therefore reinvestigated the question over a much larger SNR range. Using 2-photon microscopy, we found that local dendritic [Ca 2؉ ] modulations, which are characteristic of Reichardt detectors, occur in response to drifting gratings over a wide range of luminance levels and contrasts. We also explored, as another fingerprint of Reichardt detectors, the dependence of the velocity optimum on the pattern wavelength. Again, we found Reichardt-typical behavior throughout the whole luminance and contrast range tested. Our results, therefore, provide strong evidence that only a single elementary processing scheme is used in fly motion vision.calcium imaging ͉ computational model ͉ motion detection I n motion vision, two distinct models have been proposed to account for direction selectivity: the Reichardt detector and the gradient detector (Fig. 1). In the Reichardt detector (also called Hassenstein-Reichardt detector or correlation-type motion detector), the luminance levels of two neighboring image locations are multiplied after being filtered asymmetrically (Fig. 1a). This operation is performed twice in a mirror-symmetrical fashion before the outputs of both multipliers are subtracted from one another (1-4). The spatial or temporal average of such local motion detector signals is proportional to the image velocity within a range set by the detector time constant (5). It is one of the hallmarks of this model, however, that the output of the individual velocity detectors depends on the spatial structure of the moving pattern (in addition to the stimulus velocity) in a characteristic way. In response to drifting gratings, for example, the local Reichardt detector output consists of two components: a sustained [direct current (DC)] component, which indicates by its sign the direction of the moving stimulus, and an alternating current (AC) component, which follows the local intensity modulation and, thus, carries no directional information at all. Because the local intensity modulations are phase-shifted with respect to each other, the AC components in the local signals are cancelled by the spatial integration of many adjacent detectors. Unlike the AC component, the DC component survives spatial or temporal averaging (integration). The global output signal, therefore, is purely directional. It is predicted to exhibit a distinct optimum as a function of stimulus velocity for each pattern wavelength. The ratio of velocity and spatial wavelength at this optimum corresponds to a certain temporal frequency, which is the number of spatial patter...

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“…Nonetheless, neurobiological observations of the fly's nervous system support this computation (Single and Borst, 1998). This is most probably due to the fact that individual EMD outputs contain not only a direct current (DC) component corresponding to the stimulus motion direction, but also an alternating current (AC) component following the local intensity modulations, which carries no directional information and is phaseshifted with respect to neighboring EMDs (Haag et al, 2004). Thus, spatial integration of many adjacent EMDs is beneficial in that it preserves only the directional DC component.…”

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