SUMMARY Visual identification of targets is an important task for many animals searching for prey or conspecifics. Dragonflies utilize specialized optics in the dorsal acute zone, accompanied by higher-order visual neurons in the lobula complex, and descending neural pathways tuned to the motion of small targets. While recent studies describe the physiology of insect small target motion detector (STMD) neurons, little is known about the mechanisms that underlie their exquisite sensitivity to target motion. Lobula plate tangential cells (LPTCs), a group of neurons in dipteran flies selective for wide-field motion, have been shown to take input from local motion detectors consistent with the classic correlation model developed by Hassenstein and Reichardt in the 1950s. We have tested the hypothesis that similar mechanisms underlie the response of dragonfly STMDs. We show that an anatomically characterized centrifugal STMD neuron (CSTMD1) gives responses that depend strongly on target contrast, a clear prediction of the correlation model. Target stimuli are more complex in spatiotemporal terms than the sinusoidal grating patterns used to study LPTCs, so we used a correlation-based computer model to predict response tuning to velocity and width of moving targets. We show that increasing target width in the direction of travel causes a shift in response tuning to higher velocities, consistent with our model. Finally, we show how the morphology of CSTMD1 allows for impressive spatial interactions when more than one target is present in the visual field.

Quantitative analysis of animal behaviour is a requirement to understand the task solving strategies of animals and the underlying control mechanisms. The identification of repeatedly occurring behavioural components is thereby a key element of a structured quantitative description. However, the complexity of most behaviours makes the identification of such behavioural components a challenging problem. We propose an automatic and objective approach for determining and evaluating prototypical behavioural components. Behavioural prototypes are identified using clustering algorithms and finally evaluated with respect to their ability to represent the whole behavioural data set. The prototypes allow for a meaningful segmentation of behavioural sequences. We applied our clustering approach to identify prototypical movements of the head of blowflies during cruising flight. The results confirm the previously established saccadic gaze strategy by the set of prototypes being divided into either predominantly translational or rotational movements, respectively. The prototypes reveal additional details about the saccadic and intersaccadic flight sections that could not be unravelled so far. Successful application of the proposed approach to behavioural data shows its ability to automatically identify prototypical behavioural components within a large and noisy database and to evaluate these with respect to their quality and stability. Hence, this approach might be applied to a broad range of behavioural and neural data obtained from different animals and in different contexts.

SUMMARYHoverflies such as Eristalis tenax Linnaeus are known for their distinctive flight style. They can hover on the same spot for several seconds and then burst into movement in apparently any possible direction. In order to determine a quantitative and structured description of complex flight manoeuvres, we searched for a set of repeatedly occurring prototypical movements (PMs) and a set of rules for their ordering. PMs were identified by applying clustering algorithms to the translational and rotational velocities of the body of Eristalis during free-flight sequences. This approach led to nine stable and reliable PMs, and thus provided a tremendous reduction in the complexity of behavioural description. This set of PMs together with the probabilities of transition between them constitute a syntactical description of flight behaviour. The PMs themselves can be roughly segregated into fast rotational turns (saccades) and a variety of distinct translational movements (intersaccadic intervals). We interpret this segregation as reflecting an active sensing strategy which facilitates the extraction of spatial information from retinal image displacements. Detailed analysis of saccades shows that they are performed around all rotational axes individually and in all possible combinations. We found the probability of occurrence of a given saccade type to depend on parameters such as the angle between the long body axis and the direction of flight.

Drosophila melanogaster structures its optic flow during flight by interspersing translational movements with abrupt body rotations. Whether these “body saccades” are accompanied by steering movements of the head is a matter of debate. By tracking single flies moving freely in an arena, we now discovered that walking Drosophila also perform saccades. Movement analysis revealed that the flies separate rotational from translational movements by quickly turning their bodies by 15 degrees within a tenth of a second. Although walking flies moved their heads by up to 20 degrees about their bodies, their heads moved with the bodies during saccadic turns. This saccadic strategy contrasts with the head saccades reported for e.g., blowflies and honeybees, presumably reflecting optical constraints: modeling revealed that head saccades as described for these latter insects would hardly affect the retinal input in Drosophila because of the lower acuity of its compound eye. The absence of head saccades in Drosophila was associated with the absence of haltere oscillations, which seem to guide head movements in other flies. In addition to adding new twists to Drosophila walking behavior, our analysis shows that Drosophila does not turn its head relative to its body when turning during walking.

Fast moving animals depend on cues derived from the optic flow on their retina. Optic flow from translational locomotion includes information about the three-dimensional composition of the environment, while optic flow experienced during a rotational self motion does not. Thus, a saccadic gaze strategy that segregates rotations from translational movements during locomotion will facilitate extraction of spatial information from the visual input. We analysed whether birds use such a strategy by highspeed video recording zebra finches from two directions during an obstacle avoidance task. Each frame of the recording was examined to derive position and orientation of the beak in three-dimensional space. The data show that in all flights the head orientation was shifted in a saccadic fashion and was kept straight between saccades. Therefore, birds use a gaze strategy that actively stabilizes their gaze during translation to simplify optic flow based navigation. This is the first evidence of birds actively optimizing optic flow during flight.