Correlation versus gradient type motion detectors: the pros and cons

Abstract

Visual motion contains a wealth of information about self-motion as well as the three-dimensional structure of the environment. Therefore, it is of utmost importance for any organism with eyes. However, visual motion information is not explicitly represented at the photoreceptor level, but rather has to be computed by the nervous system from the changing retinal images as one of the first processing steps. Two prominent models have been proposed to account for this neural computation: the Reichardt detector and the gradient detector. While the Reichardt detector correlates the luminance levels derived from two adjacent image points, the gradient detector provides an estimate of the local retinal image velocity by dividing the spatial and the temporal luminance gradient. As a consequence of their different internal processing structure, both the models differ in a number of functional aspects such as their dependence on the spatial-pattern structure as well as their sensitivity to photon noise. These different properties lead to the proposal that an ideal motion detector should be of Reichardt type at low luminance levels, but of gradient type at high luminance levels. However, experiments on the fly visual systems provided unambiguous evidence in favour of the Reichardt detector under all luminance conditions. Does this mean that the fly nervous system uses suboptimal computations, or is there a functional aspect missing in the optimality criterion? In the following, I will argue in favour of the latter, showing that Reichardt detectors have an automatic gain control allowing them to dynamically adjust their input-output relationships to the statistical range of velocities presented, while gradient detectors do not have this property. As a consequence, Reichardt detectors, but not gradient detectors, always provide a maximum amount of information about stimulus velocity over a large range of velocities. This important property might explain why Reichardt type of computations have been demonstrated to underlie the extraction of motion information in the fly visual system under all luminance levels.

Coding movement direction by burst firing in electrosensory neurons

“…Directional selectivity, in which neurons respond selectively to a given direction of movement over the opposite direction, is perhaps the most widely known computation performed by sensory neurons and is a correlate of movement perception (Hubel and Wiesel 1962). Several models have been proposed to explain how directional selectivity arises (Borst 2007;Derrington et al 2004;Hock et al 2009;Maex and Orban 1991). In particular, the Reichardt model for generating directionally selective responses to moving objects requires at least two fundamental operations (Reichardt 1987;Reichardt and Wenking 1969): asymmetric filtering of information from at least two spatial locations within the receptive field and nonlinear integration of these inputs.…”

“…In particular, the Reichardt model for generating directionally selective responses to moving objects requires at least two fundamental operations (Reichardt 1987;Reichardt and Wenking 1969): asymmetric filtering of information from at least two spatial locations within the receptive field and nonlinear integration of these inputs. These so-called "Reichardt detectors" have received considerable attention and have been described in neural circuits across animal species (Borst andEgelhaaf 1989, 1990; Chacron and Fortune 2010;Chacron et al 2009;Euler et al 2002;Haag et al 2004;Hubel and Wiesel 1962;Jagadeesh et al 1997;Priebe and Ferster 2008;Priebe et al 2004;Srinivasan et al 1999).…”

The case for emulating insect brains using anatomical “wiring diagrams” equipped with biophysical models of neuronal activity

“…Likewise, there are wellestablished models for insect vision. The Reichardt detector describes motion detection in the context of the ommatidia, the optical units found on insect eyes (Borst, 2007;Reichardt, 1987). It compares luminance values at two locations and uses a temporal delay at one of the locations to facilitate motion detection.…”

The Rotating Snakes Illusion Is a Straightforward Consequence of Nonlinearity in Arrays of Standard Motion Detectors

“…We here present a fifth, simpler and parameter-free model, based on nothing but an array of standard Reichard-Hassenstein correlation detectors (which are equivalent to the motion energy model; Adelson & Bergen, 1985 , but see Borst, 2007 ) with a subsequent nonlinear (e.g., saturating) transfer function before summing their outputs. This, together with saccades while viewing, or the appearance of the pattern out of a gray background, predicts the standard Rotating Snakes Illusion, including the parameter region leading to the opposite direction of rotation.…”