Abstract. In the compensatory optomotor response of the fly the interesting phenomenon of gain control has been observed by Reichardt and colleagues (Reichardt et al., 1983): The amplitude of the response tends to saturate with increasing stimulus size, but different saturation plateaus are assumed with different velocities at which the stimulus is moving. This characteristic can already be found in the motion-sensitive large field neurons of the fly optic lobes that play a role in mediating this behavioral response (Hausen, 1982;Reichardt et al., 1983;Egelhaaf, 1985;Haag et al., 1992). To account for gain control a model was proposed involving shunting inhibition of these cells by another cell, the so-called pool cell (Reichardt et al., 1983), both cells sharing common input from an array of local motion detectors. This article describes an alternative model which only requires dendritic integration of the output signals of two types of local motion detectors with opposite polarity. The explanation of gain control relies on recent findings that these input elements are not perfectly directionally selective and that their direction selectivity is a function of pattern velocity. As a consequence, the resulting postsynaptic potential in the dendrite of the integrating cell saturates with increasing pattern size at a level between the excitatory and inhibitory reversal potentials. The exact value of saturation is then set by the activation ratio of excitatory and inhibitory input elements which in turn is a function of other stimulus parameters such as pattern velocity. Thus, the apparently complex phenomenon of gain control can be simply explained by the biophysics of dendritic integration in conjunction with the properties of the motion-sensitive input elements.

Detecting the direction of image motion is a fundamental component of visual computation, essential for survival of the animal. However, at the level of individual photoreceptors, the direction in which the image is shifting is not explicitly represented. Rather, directional motion information needs to be extracted from the photoreceptor array by comparing the signals of neighboring units over time. The exact nature of this process as implemented in the visual system of the fruit fly Drosophila melanogaster has been studied in great detail, and much progress has recently been made in determining the neural circuits giving rise to directional motion information. The results reveal the following: (1) motion information is computed in parallel ON and OFF pathways. (2) Within each pathway, T4 (ON) and T5 (OFF) cells are the first neurons to represent the direction of motion. Four subtypes of T4 and T5 cells exist, each sensitive to one of the four cardinal directions. (3) The core process of direction selectivity as implemented on the dendrites of T4 and T5 cells comprises both an enhancement of signals for motion along their preferred direction as well as a suppression of signals for motion along the opposite direction. This combined strategy ensures a high degree of direction selectivity right at the first stage where the direction of motion is computed. (4) At the subsequent processing stage, tangential cells spatially integrate direct excitation from ON and OFF-selective T4 and T5 cells and indirect inhibition from bi-stratified LPi cells activated by neighboring T4/T5 terminals, thus generating flow-field-selective responses.

Abstract. Reengineering a legacy product line has been addressed very little by current product line research activities. This paper introduces a method to investigate feature dependencies and interactions, which restricts the variants that can be derived from the legacy product line assets. Reorganizing the product line assets with respect to new requirements requires more knowledge than what is easily provided by the classical feature-modeling approaches. Hence, adding all the feature dependencies and interactions into the feature tree results in unreadable and unmanageable feature models that fail to achieve their original goals. We therefore propose two complementary views to represent the feature model. One view shows the hierarchical refinement of features similar to common featuremodeling approaches in a feature tree. The second view describes what kind of dependencies and interactions there are between various features. We show two examples of feature dependencies and interactions in the context of an engine-control software product line, and we demonstrate how our approach helps to define correct product configurations from product line variants.

Global visual motion elicits an optomotor response of the eye that stabilizes the visual input on the retina. Here, we analyzed the neck motor system of the blowfly to understand binocular integration of visual motion information underlying a head optomotor response. We identified and characterized two cervical nerve motor neurons (called CNMN6 and CNMN7) tuned precisely to an optic flow corresponding to pitch movements of the head. By means of double recordings and dye coupling, we determined that these neurons are connected ipsilaterally to two vertical system cells (VS2 and VS3), and contralaterally to one horizontal system cell (HSS). In addition, CNMN7 turned out to be connected to the ipsilateral CNMN6 and to its contralateral counterpart. To analyze a potential function of this circuit, we performed behavioral experiments and found that the optomotor pitch response of the fly head was only observable when both eyes were intact. Thus, this neural circuit performs two visuomotor transformations: first, by integrating binocular visual information it enhances the tuning to the optic flow resulting from pitch movements of the head, and second it could assure an even head declination by coordinating the activity of the CNMN7 neurons on both sides.

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