Directionally selective cells in the locust medulla

Summary Intracellular recordings of a distinctive class of directionally selective cell from the medulla of the locust,Locusta migratoria, optic lobe are described. Dye marking shows that these cells arborize in the distal part of the medulla, and project through the lobula complex. The cells are excited by upward movement and have receptive fields of about 20° in diameter. They are sensitive to a wide range of angular velocities from 0.02°/s to over 200°/s. The cells are sensitive to stationary flicker and have different latencies to dimming and brightening. Evidence is presented which suggests that directional computation depends, at least in part, on an inhibitory interaction between flicker sensitive channels.     

Processing of figure and background motion in the visual system of the fly

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.     

Fly-like visuomotor responses of a robot using aVLSI motion-sensitive chips

 We explore the use of continuous-time analog very-large-scale-integrated (aVLSI) neuromorphic visual preprocessors together with a robotic platform in generating bio-inspired behaviors. Both the aVLSI motion sensors and the robot behaviors described in this work are inspired by the motion computation in the fly visual system and two different fly behaviors. In most robotic systems, the visual information comes from serially scanned imagers. This restricts the form of computation of the visual image and slows down the input rate to the controller system of the robot, hence increasing the reaction time of the robot. These aVLSI neuromorphic sensors reduce the computational load and power consumption of the robot, thus making it possible to explore continuous-time visuomotor control systems that react in real-time to the environment. The motion sensor provides two outputs: one for the preferred direction and the other for the null direction. These motion outputs are created from the aggregation of six elementary motion detectors that implement a variant of Reichardt's correlation algorithm. The four analog continuous-time outputs from the motion chips go to the control system on the robot which generates a mixture of two behaviors – course stabilization and fixation – from the outputs of these sensors. Since there are only four outputs, the amount of information transmitted to the controller is reduced (as compared to using a CCD sensor), and the reaction time of the robot is greatly decreased. In this work, the robot samples the motion sensors every 3.3 ms during the behavioral experiments. Received: 4 October 1999 / Accepted in revised form: 26 April 2001     

Temporal modulation of luminance adapts time constant of fly movement detectors

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.     

Perception of heterochromatic flicker by honeybees: a behavioural study

Freely flying honeybees were trained to discriminate a stimulus consisting of two alternating chromatic lights (heterochromatic flicker) from a steady mixture of the same two lights, using 3 different pairs of lights: blue-UV, UV-green, and green-UV. With each light pair, training to the heterochromatic flicker was conducted at several flicker frequencies, using experimentally naive bees in each training. In subsequent tests, the trained bees were given a choice between the two lights that constituted the flicker, presented steady, as well as between either of them and the steady mixture. We find that bees trained to particular frequencies of heterochromatic flicker prefer one of the component lights over the other as well as over the steady mixture, suggesting that the colour they perceive in the heterochromatic flicker to which they have been trained is shifted in the direction of one of the lights contained in the flicker. The colour shift occurs at flicker frequencies that depend on the pair of lights used. We propose that the shift is generated by an effect similar to the Brücke-Bartley phenomenon known from human vision. This effect is based on the enhancement of the photoreceptors' response upon onset of stimulation, causing an intermittent light to appear brighter than a steady light of identical physical intensity. We propose that the degree of enhancement might differ among the 3 spectral classes of photoreceptor, causing the colour perceived in a heterochromatic flicker to differ from that perceived in a steady mixture of its two light components.     

Figure-ground discrimination in the visual system ofDrosophila melanogaster

Drosophila melanogaster is able to detect a small visual object hidden in a background of identical texture, as long as there is relative motion between their retinal images. The properties of figure-ground discrimination in the walking fly are studied under experimental conditions where the positions of figure and ground oscillate sinusoidally with similar frequency and similar amplitude but with different phase. The following points have been established. (a) The average turning reaction of the stationarily walkingDrosophila depends on phase; contrary to results obtained with the flyingMusca (Reichardt and Poggio, 1979), antiphasic oscillation of figure and ground does not suppress the attrativeness of the figure. (b) A translatory response has been found which also depends on the phase difference of the oscillatory movements of figure and ground. (c) The time course of the responses and its intra- and inter-individual variability do not seem to fit into a rigid model of figure-ground discrimination.     

Columnar cells necessary for motion responses of wide-field visual interneurons in <Emphasis Type="Italic">Drosophila</Emphasis>

Wide-field motion-sensitive neurons in the lobula plate (lobula plate tangential cells, LPTCs) of the fly have been studied for decades. However, it has never been conclusively shown which cells constitute their major presynaptic elements. LPTCs are supposed to be rendered directionally selective by integrating excitatory as well as inhibitory input from many local motion detectors. Based on their stratification in the different layers of the lobula plate, the columnar cells T4 and T5 are likely candidates to provide some of this input. To study their role in motion detection, we performed whole-cell recordings from LPTCs in Drosophila with T4 and T5 cells blocked using two different genetically encoded tools. In these flies, motion responses were abolished, while flicker responses largely remained. We thus demonstrate that T4 and T5 cells indeed represent those columnar cells that provide directionally selective motion information to LPTCs. Contrary to previous assumptions, flicker responses seem to be largely mediated by a third, independent pathway. This work thus represents a further step towards elucidating the complete motion detection circuitry of the fly.     

Elementary detectors for vertical movement in the visual system of Drosophila

Optomotor thrust responses of the fruitfly Drosophila melanogaster to moving gratings have been analysed in order to determine the arrangement of elementary movement detectors in the hexagonal array of the compound eye. These detectors enable the fly to perceive vertical movement. The results indicate that, under photopic stimulation of a lateral equatorial eye region, the movement specific response originates predominantly from two types of elementary movement detectors which connect neighbouring visual elements in the compound eye. One of the detectors is oriented vertically, the other detector deviates 60° towards the anterior-superior direction (Fig. 5b). The maximum of the thrust differences to antagonistic movement is obtained if the pattern is moving vertically or along a superior/anterior — inferior/posterior direction 30° displaced from the vertical (Fig. 3d,e, Fig. 6). Only one of the detectors coincides with one of the two detectors responsible for horizontal movement detection. This indicates that a third movement specific interaction in the compound eye of Drosophila has to be postulated. — The contrast dependence of the thrust response (Fig. 2) yields the acceptance angle of the receptors mediating the response. The result coincides with the acceptance angle found by analysis of the turning response of Drosophila (Heisenberg and Buchner, 1977). This value corresponds to the acceptance angle expected, on the basis of optical considerations, for the receptor system R 1–6. — The movement-specific neuronal network responsible for thrust control is not homogeneous throughout the visual field of Drosophila. Magnitude and preferred direction of the thrust response in the upper frontal part of the visual field seem to vary considerably in different flies (Fig. 6).     

蝇视系统的图形—背景分辨和初级模式分辨

本文通过行为实验及计算机模拟进一步证明,蝇视系统的自发模式辨别可以看作是图形—背景分辨的特殊情况.关键在于蝇的模式分辨是由运动检测器实现的.运动检测器不仅对模式速度反应,也对模式的结构特性反应.本文提出,人视系统的模式分辨也可能部分地由运动检测器来实现.     

Visual sensitivity of ground squirrels to spatial and temporal luminance variations

Summary We have investigated the visual sensitivity of the California ground squirrel (Speromphilus beecheyi) to spatial and temporal luminance patterns. Spatial contrast sensitivity functions were determined in behavioral discrimination experiments in which the stimuli were sinusoidally-modulated luminance gratings. These squirrels were found to be maximally sensitive to spatial frequencies of about 0.7 cycles/ degree (c/d), and they are unable to discriminate gratings whose frequencies exceed 4 c/d. Similar results were obtained in electrophysiological experiments when the visually evoked cortical potential (VECP) was recorded from anesthetized squirrels. A third experiment involved tests of the ability of ground squirrels to discriminate square-wave gratings of much higher luminance (340 cd/m²). The finest gratings which were discriminable at this luminance level did not exceed 3.9–4.3 c/d and, thus, we conclude that the maximal spatial resolution of the California ground squirrel is about 4 c/d (corresponding to a bar separation of 7.5). In another behavioral experiment the abilities of ground squirrels to discriminate sinusoidally flickering lights (mean luminance = 3.4 cd/m²) was measured. The results show that ground squirrels are maximally sensitive to lights flickering at a rate of about 18 Hz, and that the highest rates that are still discriminable are slightly above 60 Hz.Abbreviations c/d cycles/degree - CFF critical flicker frequency - VECP visually evoked cortical potential This research was supported by Grant EY 00105 from the National Eye Institute. We thank David Birch who participated in some preliminary behavioral experiments and Kenneth Long who provided the histological material from which measurements of receptor spacing were made.     

Orientation flights of solitary wasps (Cerceris; Sphecidae; Hymenoptera)

Bees and wasps are known to use a visual representation of the nest environment to guide the final approach to their nest. It is also known that they acquire this representation during an orientation flight performed on departure.A detailed film analysis shows that orientation flights in solitary wasps of the genus Cerceris consist of a systematic behavioural sequence: after lift-off from the nest entrance, wasps fly in ever increasing arcs around the nest. They fly along these arcs obliquely to their long axis and turn so that the nest entrance is held in the left or right visual field at retinal positions between 30° and 70° from the midline. Horizontal distance from the nest and height above ground increase throughout an orientation flight so that the nest is kept at retinal elevations between 45° and 60° below the horizon. The wasps' rate of turning is constant at between 100°/s and 200°/s independent of their distance from the nest and their ground velocity increases with distance. The consequence of this is that throughout the flight wasps circle at a constant angular velocity around the nest.Orientation flights are strongly influenced by landmark lay-out. Wasps adjust their flight-path and their orientation in a way that allows them to fixate the nest entrance and to hold the closest landmark in their frontal visual field.The orientation flight generates a specific topography of motion parallax across the visual field. This could be used by wasps to acquire a series of snapshots that all contain the nest position, to acquire snapshots of close landmarks only (distance filtering), to exclude shadow contours from their visual representation (figure-ground discrimination) or to gain information on the distance of landmarks relative to the nest.     

Figure tracking by flies is supported by parallel visual streams

Visual figures may be distinguished based on elementary motion or higher-order non-Fourier features, and flies track both. The canonical elementary motion detector, a compact computation for Fourier motion direction and amplitude, can also encode higher-order signals provided elaborate preprocessing. However, the way in which a fly tracks a moving figure containing both elementary and higher-order signals has not been investigated. Using a novel white noise approach, we demonstrate that (1) the composite response to an object containing both elementary motion (EM) and uncorrelated higher-order figure motion (FM) reflects the linear superposition of each component; (2) the EM-driven component is velocity-dependent, whereas the FM component is driven by retinal position; (3) retinotopic variation in EM and FM responses are different from one another; (4) the FM subsystem superimposes saccadic turns upon smooth pursuit; and (5) the two systems in combination are necessary and sufficient to predict the full range of figure tracking behaviors, including those that generate no EM cues at all. This analysis requires an extension of the model that fly motion vision is based on simple elementary motion detectors and provides a novel method to characterize the subsystems responsible for the pursuit of visual figures.     

CRITICAL FREQUENCY OF FLICKER AS A FUNCTION OF INTENSITY OF ILLUMINATION FOR THE EYE OF THE BEE

The bee''s characteristic response to a movement of its visual field is used for the study of the relation between critical frequency of flicker and illumination. The critical flicker frequency varies with illumination in such a way that with increasing flicker frequency the intensity of illumination must be increased to produce a threshold response in the bee. The illuminations required to give a response in a bee at different flicker frequencies closely correspond to the intensities for threshold response in visual acuity tests. This is due to the different thresholds of excitability of the elements of the ommatidial mosaic. An analysis of the variation of the values for threshold intensities at the several flicker frequencies shows that the variation depends upon flicker frequency and upon the number of elements functioning at different intensities.     

The halteres of the blowfly Calliphora

The movement of the halteres during fixed flight was video recorded under stroboscopic illumination phase coupled to the wing beat. The halteres swing in a rounded triangular manner through an angle of almost 80° in vertical planes tilted backwards from the transverse plane by ca. 30° (Figs. 1, 2).The physics of the halteres are described in terms of a general formula for the force acting onto the endknob of the moving haltere during rotations and linear accelerations of the fly (Eq. 1). On the basis of the experimentally determined kinematics of the haltere, the primary forces and the forces dependent on angular velocity and on angular acceleration are calculated (Figs. 3, 4).Three distinct types of angular velocity dependent (Coriolis) forces are generated by rotations about 3 orthogonal axes. Thus, in principle one haltere could detect all rotations in space (Fig. 6).The angular acceleration dependent forces have the same direction and frequency as the Coriolis forces, but they are shifted in phase by 90°. Thus, they could be evaluated in parallel and independently from the Coriolis forces. They are, however, much smaller than the Coriolis forces for oscillation frequencies of the fly up to 20 Hz (Fig. 5). From these considerations it is concluded that Coriolis forces play the major role in detecting body rotations.     

Correlation versus gradient type motion detectors: the pros and cons

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.     

Dynamic response properties of movement detectors: Theoretical analysis and electrophysiological investigation in the visual system of the fly

Dynamic aspects of the computation of visual motion information are analysed both theoretically and experimentally. The theoretical analysis is based on the type of movement detector which has been proposed to be realized in the visual system of insects (e.g. Hassenstein and Reichardt 1956; Reichardt 1957, 1961; Buchner 1984), but also of man (e.g. van Doorn and Koenderink 1982a, b; van Santen and Sperling 1984; Wilson 1985). The output of both a single movement detector and a one-dimensional array of detectors is formulated mathematically as a function of time. The resulting movement detector theory can be applied to a much wider range of moving stimuli than has been possible on the basis of previous formulations of the detector output. These stimuli comprise one-dimensional smooth detector input functions, i.e. functions which can be expanded into a time-dependent convergent Taylor series for any value of the spatial coordinate.The movement detector response can be represented by a power series. Each term of this series consists of one exclusively time-dependent component and of another component that depends, in addition, on the properties of the pattern. Even the exclusively time-dependent components of the movement detector output are not solely determined by the stimulus velocity. They rather depend in a non-linear way on the weighted sum of the instantaneous velocity and all its higher order time derivatives. The latter point represents another reason — not discussed so far in the literature — that movement detectors of the type analysed here do not represent pure velocity sensors.The significance of this movement detector theory is established for the visual system of the fly. This is done by comparing the spatially integrated movement detector response with the functional properties of the directionally-selective motion-sensitive. Horizontal Cells of the third visual ganglion of the fly's brain.These integrate local motion information over large parts of the visual field. The time course of the spatially integrated movement detector response is about proportional to the velocity of the stimulus pattern only as long as the pattern velocity and its time derivatives are sufficiently small. For large velocities and velocity changes of the stimulus pattern characteristic deviations of the response profiles from being proportional to pattern velocity are predicted on the basis of the detector theory developed here. These deviations are clearly reflected in the response of the wide-field Horizontal Cells, thus, providing very specific evidence that the movement detector theory developed here can be applied to motion detection in the fly. The characteristic dynamic features of the theoretically predicted and the experimentally determined cellular responses are exploited to estimate the time constant of the movement detector filter.     

Comparison between the movement detection systems underlying the optomotor and the landing response in the housefly

Flies evaluate movement within their visual field in order to control the course of flight and to elicit landing manoeuvres. Although the motor output of the two types of responses is quite different, both systems can be compared with respect to the underlying movement detection systems. For a quantitative comparison, both responses were measured during tethered flight under identical conditions. The stimulus was a sinusoidal periodic pattern of vertical stripes presented bilaterally in the fronto-lateral eye region of the fly. To release the landing response, the pattern was moved on either side from front to back. The latency of the response depends on the stimulus conditions and was measured by means of an infrared light-beam that was interrupted whenever the fly lifted its forelegs to assume a preprogrammed landing posture (Borst and Bahde 1986). As an optomotor stimulus the pattern moved on one side from front to back and on the other side in the opposite direction. The induced turning tendency was measured by a torque meter (Götz 1964). The response values which will be compared are the inverse latencies of the landing response and the amplitude of the yaw torque.

1. Optomotor course-control is more sensitive to pattern movement at small spatial wavelengths (10° and 20°) than the landing response (Fig. 1a and b). This suggests that elementary movement detectors (EMDs, Buchner 1976) with large detection base (the distance between interacting visual elements) contribute more strongly to the landing than to the optomotor system.
2. The optimum contrast frequencies of the different responses obtained at a comparatively high pattern contrast of about 0.6 was found to be between 1 and 10 Hz for the optomotor response, and around 20 Hz for the landing response (Fig. 2a and b). This discrepancy can be explained by the fact that the optomotor response was tested under stationary conditions (several seconds of stimulation) while for the landing response transient response characteristics of the movement detectors have to be taken into account (landing occurs under these conditions within less than 100 ms after onset of the movement stimulus). To test the landing system under more stationary conditions, the pattern contrast had to be reduced to low values. This led to latencies of several seconds. Then the optimum of the landing response is around 4 Hz. This is in the optimum range of the optomotor course-control response. The result suggests the same filter time constants for the movement detectors of both systems.
3. The dependence of both responses on the position and the size of the pattern was examined. The landing response has its optimum sensitivity more ventrally than the optomotor response (Fig. 3a and b). Both response amplitudes increase with the size of the pattern in a similar progression (Fig. 3c and d).

In first approximation, the present results are compatible with the assumption of a common set of movement detectors for both the optomotor course-control and the landing system. Movement detectors with different sampling bases and at different positions in the visual field seem to contribute with different gain to both responses. Accordingly, the control systems underlying both behaviors are likely to be independent already at the level of spatial integration of the detector output.     

What kind of movement detector is triggering the landing response of the housefly?

As shown before, the latency of the housefly's landing response depends on the conditions of the visual stimulus (Borst 1986). Accordingly, the latency can be used to characterize the movement detection system which is triggering the landing response.The stimulus was a sinusoidal periodic pattern of vertical stripes presented bilaterally in the frontolateral eye region of the fly. It started to move, simultaneously on either side, from front to back at a given time. The latency of the response was measured by means of an infrared light-beam that was interrupted whenever the fly lifted its forelegs to assume a preprogrammed landing posture (Fig. 1). The latency was found to vary in a range from 60 ms up to several seconds depending on the pattern's spatial wavelength , contrast frequency cf and contrast C.For sufficiently high pattern contrast the optimum of the reaction (minimum latency) is found at spatial wavelengths of 30–40° and contrast frequencies of 8–17 periods/s (Fig. 3a). This is about 2–10 times more than is anticipated from the optomotor response under similar conditions. Evaluation of the optimum contrast frequency cf _OPT at different wavelengths shows that cf _OPT is not independent of (Fig. 3b, solid line). The same is true for the contrast dependence of the reaction: reduction of the contrast leads not only to a general decrease in the response amplitudes (prolongation of the latency) (Fig. 4a), but also to a shift of cf _OPT towards lower contrast frequencies (Fig. 4b, solid line).In the theory of the correlation-type movement detector (Reichardt 1961) which underlies the optomotor response of flies the dependence of cf _OPT on pattern wavelength and/or pattern contrast is not expected under stationary conditions. However, as shown by computer simulation all experimental results can be explained by a homogeneous retinotopic array of correlation movement detectors (Fig. 2) if their response under non-stationary conditions is taken into account. We simply assume that the spatially and temporally integrated output of the movement detectors is evaluated by a threshold device (Fig.5). The correlation-type movement detection in combination with a temporal integrator system predicts the rather complex dependence of the optimum contrast frequency on pattern wavelength and pattern contrast (dashed lines in Fig. 3b and 4b) and provides the missing explanation of the variable latencies of the landing response.Comparing the parameters of the correlation-type movement detector derived in the present study with those of the optomotor response, the landing response seems to use the same type of movement detection system. To account for the high wavelength optimum, however, the input elements of the movement detection system of the landing response might have an increased visual field (e.g. by pooling neighbouring visual elements) and, accordingly, a reduced visual acuity as compared with the input elements of the optomotor system.Abbreviations (°) spatial pattern wavelength - w(°/s) angular velocity of the pattern - cf (Hz) contrast frequency=w/ - cf _OPT(Hz) cf leading to the shortest latency - (Hz) angular frequency=2cf - I mean luminance of the pattern - I modulation amplitude of the pattern - C pattern contrast=I/ - (ms) time constant of a filter - (°) angle between the optical axis of neighbouring visual elements - (°) acceptance angle of visual elements     

Blue flicker modifies the subfoveal choroidal blood flow in the human eye

The objective of the present study was to reveal an interaction between choroidal blood flow (ChBF) and light-induced photoreceptor activity, a physiological coupling that has been already demonstrated for retinal blood flow but rejected for ChBF. Ten healthy adults volunteered for this study. A real-time recording near-infrared laser-Doppler flowmeter was used to quantify the subfoveal ChBF while the luminance of blue flicker between 1 and 64 Hz was first increased and then decreased by 4.0 log units in 1.0-log unit steps between 0.0375 and 375 cd/m2. In separate testing, flash electroretinograms (ERGs) provided electrophysiological indexes of the relative response of short-wave cones (s-cones) and rods to blue light stimulation. Group-averaged, normalized ChBF measurements revealed that it was modulated by approximately 9% by flicker frequency. Increasing the blue flicker luminance from low to high attenuated the subfoveal ChBF, volume, and velocity by approximately 32%, approximately 30%, and approximately 5%, respectively. Decreasing the luminance from high to low over the same range had no effect on the subfoveal choroidal hemodynamics. The markedly different effects of reversed directions of change in blue flicker luminance on the subfoveal ChBF were linked to transitions between rod-dominated and s-cone-dominated retinal responses. Collectively, these findings indicate that the blue light-induced photoreceptor response is associated with a differential distribution of the ChBF across the ocular fundus according to the degree and type of retinal photoreceptor stimulated.