Notes: Abstract Translatory movement of an animal in its environment induces optic flow that contains information about the three-dimensional layout of the surroundings: as a rule, images of objects that are closer to the animal move faster across the retina than those of more distant objects. Such relative motion cues are used by flies to detect objects in front of a structured background. We confronted flying flies, tethered to a torque meter, with front-to-back motion of patterns displayed on two CRT screens, thereby simulating translatory motion of the background as experienced by an animal during straight flight. The torque meter measured the instantaneous turning responses of the fly around its vertical body axis. During short time intervals, object motion was superimposed on background pattern motion. The average turning response towards such an object depends on both object and background velocity in a characteristic way: (1) in order to elicit significant responses object motion has to be faster than background motion; (2) background motion within a certain range of velocities improves object detection. These properties can be interpreted as adaptations to situations as they occur in natural free flight. We confirmed that the measured responses were mediated mainly by a control system specialized for the detection of objects rather than by the compensatory optomotor system responsible for course stabilization.

Notes: Abstract. A prominent model of visual motion detection is the so-called correlation or Reichardt detector. Whereas this model can account for many properties of motion vision, from humans to insects (review, Borst and Egelhaaf 1989), it has been commonly assumed that this scheme of motion detection is not well suited to the measurement of image velocity. This is because the commonly used version of the model, which incorporates two unidirectional motion detectors with opposite preferred directions, produces a response which varies not only with the velocity of the image, but also with its spatial structure and contrast. On the other hand, information on image velocity can be crucial in various contexts, and a number of recent behavioural experiments suggest that insects do extract velocity for navigational purposes (review, Srinivasan et al. 1996). Here we show that other versions of the correlation model, which consists of a single unidirectional motion detector or incorporates two oppositely directed detectors with unequal sensitivities, produce responses which vary with image speed and display tuning curves that are substantially independent of the spatial structure of the image. This surprising feature suggests simple strategies of reducing ambiguities in the estimation of speed by using components of neural hardware that are already known to exist in the visual system.

Notes: Abstract The time constant of movement detectors in the fly visual system has been proposed to adapt in response to moving stimuli (de Ruyter van Steveninck et al. 1986). The objective of the present study is to analyse, whether this adaptation can be induced as well, if the luminance of a stationary uniform field is modulated in time. The experiments were done on motion-sensitive wide-field neurones of the lobula plate, the posterior part of the third visual ganglion of the blowfly, calliphora erythrocephala. These cells are assumed to receive input from large retinotopic arrays of movement detectors. In order to demonstrate that our results concern the properties of the movement detectors rather than those of a particular wide-field cell we recorded from two different types of them, the H1- and the HSE-cell. Both cell types respond to a brief movement stimulus in their preferred direction with a transient excitation. This response decays about exponentially. The time constant of this decay reflects, in a first approximation, the time constant of the presynaptic movement detectors. It was determined after prestimulation of the cell by the following stimuli: (a) periodic stationary grating; (b) uniform field, the intensity of which was modulated sinusoidally in time (flicker stimulation); (c) periodic grating moving front-to-back; (d) periodic grating moving back-to-front. The decay of the response is significantly faster not only after movement but also after flicker stimulation as compared with pre-stimulation with a stationary stimulus. This is interpreted as an adaptation of the movement detector's time constant. The finding that flicker stimulation also leads to an adaptation shows that movement is not necessary for this process. Instead the adaptation of the time constant appears to be governed mainly by the temporal modulation (i.e., contrast frequency) of the signal in each visual channel.

Notes: Abstract The visual system of the fly is able to extract different types of global retinal motion patterns as may be induced on the eyes during different flight maneuvers and to use this information to control visual orientation. The mechanisms underlying these tasks were analyzed by a combination of quantitative behavioral experiments on tethered flying flies (Musca domestica) and model simulations using different conditions of oscillatory large-field motion and relative motion of different segments of the stimulus pattern. Only torque responses about the vertical axis of the animal were determined. The stimulus patterns consisted of random dot textures (“Julesz patterns”) which could be moved either horizontally or vertically. Horizontal rotatory large-field motion leads to compensatory optomotor turning responses, which under natural conditions would tend to stabilize the retinal image. The response amplitude depends on the oscillation frequency: It is much larger at low oscillation frequencies than at high ones. When an object and its background move relative to each other, the object may, in principle, be discriminated and then induce turning responses of the fly towards the object. However, whether the object is distinguished by the fly depends not only on the phase relationship between object and background motion but also on the oscillation frequency. At all phase relations tested, the object is detected only at high oscillation frequencies. For the patterns used here, the turning responses are only affected by motion along the horizontal axis of the eye. No influences caused by vertical motion could be detected. The experimental data can be explained best by assuming two parallel control systems with different temporal and spatial integration properties: TheLF-system which is most sensitive to coherent rotatory large-field motion and mediates compensatory optomotor responses mainly at low oscillation frequencies. In contrast, theSF-system is tuned to small-field and relative motion and thus specialized to discriminate a moving object from its background; it mediates turning responses towards objects mainly at high oscillation frequencies. The principal organization of the neural networks underlying these control systems could be derived from the characteristic features of the responses to the different stimulus conditions. The input to the model circuits responsible for the characteristic sensitivity of the SF-system to small-field and relative motion is provided by retinotopic arrays of local movement detectors. The movement detectors are integrated by a large-field element, the output cell of the network. The synapses between the detectors and the output cells have nonlinear transmission characteristics. Another type of large-field elements (“pool cells”) which respond to motion in front of both eyes and have characteristic direction selectivities are assumed to interact with the local movement detector channels by inhibitory synapses of the shunting type, before the movement detectors are integrated by the output cells. The properties of the LF-system can be accounted for by similar model circuits which, however, differ with respect to the transmission characteristic of the synapses between the movement detectors and the output cell; moreover, their pool cells are only monocular. This type of network, however, is not necessary to account for the functional properties of the LF-system. Instead, intrinsic properties of single neurons may be sufficient. Computer simulations of the postulated mechanisms of the SF-and LF-system reveal that these can account for the specific features of the behavioral responses under quite different conditions of coherent large-field motion and relative motion of different pattern segments.