Properties of elementary movement detectors in the flyCalliphora erythrocephala

How fly neurons compute the direction of visual motion

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.

A common directional tuning mechanism of Drosophila motion-sensing neurons in the ON and in the OFF pathway

In the fruit fly optic lobe, T4 and T5 cells represent the first direction-selective neurons, with T4 cells responding selectively to moving brightness increments (ON) and T5 cells to brightness decrements (OFF). Both T4 and T5 cells comprise four subtypes with directional tuning to one of the four cardinal directions. We had previously found that upward-sensitive T4 cells implement both preferred direction enhancement and null direction suppression (Haag et al., 2016). Here, we asked whether this mechanism generalizes to OFF-selective T5 cells and to all four subtypes of both cell classes. We found that all four subtypes of both T4 and T5 cells implement both mechanisms, that is preferred direction enhancement and null direction inhibition, on opposing sides of their receptive fields. This gives rise to the high degree of direction selectivity observed in both T4 and T5 cells within each subpopulation.

Complementary mechanisms create direction selectivity in the fly

How neurons become sensitive to the direction of visual motion represents a classic example of neural computation. Two alternative mechanisms have been discussed in the literature so far: preferred direction enhancement, by which responses are amplified when stimuli move along the preferred direction of the cell, and null direction suppression, where one signal inhibits the response to the subsequent one when stimuli move along the opposite, i.e. null direction. Along the processing chain in the Drosophila optic lobe, directional responses first appear in T4 and T5 cells. Visually stimulating sequences of individual columns in the optic lobe with a telescope while recording from single T4 neurons, we find both mechanisms at work implemented in different sub-regions of the receptive field. This finding explains the high degree of directional selectivity found already in the fly's primary motion-sensing neurons and marks an important step in our understanding of elementary motion detection.

In search of the Holy Grail of fly motion vision

Detecting the direction of image motion is important for visual navigation as well as predator, prey and mate detection and, thus, essential for the survival of all animals that have eyes. However, the direction of motion is not explicitly represented at the level of the photoreceptors: it rather needs to be computed by subsequent neural circuits, involving a comparison of the signals from neighbouring photoreceptors over time. The exact nature of this process as implemented at the neuronal level has been a long-standing question in the field. Only recently, much progress has been made in Drosophila by genetically targeting individual neuron types to block, activate or record from them. The results obtained this way indicate that: (i) luminance information from fly photoreceptors R1-6 is split into two parallel motion circuits, specialized to detect the motion of luminance increments (ON-Channel) and decrements (OFF-Channel) separately; (ii) lamina neurons L1 and L2 are the primary input neurons to these circuits (L1 → ON-channel, L2 → OFF-channel); and (iii) T4 and T5 cells carry their output signals (ON → T4, OFF → T5).

A visual motion detection circuit suggested by Drosophila connectomics

Animal behaviour arises from computations in neuronal circuits, but our understanding of these computations has been frustrated by the lack of detailed synaptic connection maps, or connectomes. For example, despite intensive investigations over half a century, the neuronal implementation of local motion detection in the insect visual system remains elusive. Here we develop a semi-automated pipeline using electron microscopy to reconstruct a connectome, containing 379 neurons and 8,637 chemical synaptic contacts, within the Drosophila optic medulla. By matching reconstructed neurons to examples from light microscopy, we assigned neurons to cell types and assembled a connectome of the repeating module of the medulla. Within this module, we identified cell types constituting a motion detection circuit, and showed that the connections onto individual motion-sensitive neurons in this circuit were consistent with their direction selectivity. Our results identify cellular targets for future functional investigations, and demonstrate that connectomes can provide key insights into neuronal computations.

A `bright zone' in male hoverfly (Eristalis tenax) eyes and associated faster motion detection and increased contrast sensitivity

Eyes of the hoverfly Eristalis tenax are sexually dimorphic such that males have a fronto-dorsal region of large facets. In contrast to other large flies in which large facets are associated with a decreased interommatidial angle to form a dorsal `acute zone' of increased spatial resolution, we show that a dorsal region of large facets in males appears to form a `bright zone' of increased light capture without substantially increased spatial resolution. Theoretically, more light allows for increased performance in tasks such as motion detection. To determine the effect of the bright zone on motion detection, local properties of wide field motion detecting neurons were investigated using localized sinusoidal gratings. The pattern of local preferred directions of one class of these cells, the HS cells, in Eristalis is similar to that reported for the blowfly Calliphora. The bright zone seems to contribute to local contrast sensitivity; high contrast sensitivity exists in portions of the receptive field served by large diameter facet lenses of males and is not observed in females. Finally, temporal frequency tuning is also significantly faster in this frontal portion of the world, particularly in males, where it overcompensates for the higher spatial-frequency tuning and shifts the predicted local velocity optimum to higher speeds. These results indicate that increased retinal illuminance due to the bright zone of males is used to enhance contrast sensitivity and speed motion detector responses. Additionally, local neural properties vary across the visual world in a way not expected if HS cells serve purely as matched filters to measure yaw-induced visual motion.

Implications of functionally different synaptic inputs for neuronal gain and computational properties of fly visual interneurons

Neurons embedded in networks are thought to receive synaptic inputs that do not drive them on their own, but modulate the responsiveness to driving input. Although studies on brain slices have led to detailed knowledge of how nondriving input affects dendritic integration, its origin and functional implications remain unclear. We tackle this issue using an ensemble of fly wide-field visual interneurons. These neurons offer the opportunity not only to combine in vivo recording techniques and natural sensory stimulation but also to interpret electrophysiological results in a behavioral context. By targeted manipulation of the animal's visual input we find a pronounced modulating impact of nondriving input, whereas functionally important cellular properties like direction tuning and the coding of pattern velocity are left almost unaffected. We propose that the integration of functionally different synaptic inputs is a mechanism that immanently equalizes the ensemble's sensitivity irrespective of the specific stimulus conditions.

Anatomical organization of retinotopic motion-sensitive pathways in the optic lobes of flies

Anatomical methods have identified conserved neuronal morphologies and synaptic relationships among small-field retinotopic neurons in insect optic lobes. These conserved cell shapes occur across many species of dipteran insects and are also shared by Lepidoptera and Hymenoptera. The suggestion that such conserved neurons should participate in motion computing circuits finds support from intracellular recordings as well as older studies that used radioactive deoxyglucose labeling to reveal strata with motion-specific activity in an achromatic neuropil called the lobula plate. While intracellular recordings provide detailed information about the motion-sensitive or motion-selective responses of identified neurons, a full understanding of how arrangements of identified neurons compute and integrate information about visual motion will come from a multidisciplinary approach that includes morphological circuit analysis, the use of genetic mutants that exhibit specific deficits in motion processing, and biomimetic models. The latter must be based on the organization and connections of real neurons, yet provide output properties similar to those of more traditional theoretical models based on behavioral observations that date from the 1950s. Microsc. Res. Tech. 62:132-150, 2003.

Adaptation and the temporal delay filter of fly motion detectors

Recent accounts attribute motion adaptation to a shortening of the delay filter in elementary motion detectors (EMDs). Using computer modelling and recordings from HS neurons in the drone-fly Eristalis tenax, we present evidence that challenges this theory. (i) Previous evidence for a change in the delay filter comes from 'image step' (or 'velocity impulse') experiments. We note a large discrepancy between the temporal frequency tuning predicted from these experiments and the observed tuning of motion sensitive cells. (ii) The results of image step experiments are highly sensitive to the experimental method used. (iii) An apparent motion stimulus reveals a much shorter EMD delay than suggested by previous 'image step' experiments. This short delay agrees with the observed temporal frequency sensitivity of the unadapted cell. (iv) A key prediction of a shortening delay filter is that the temporal frequency optimum of the cell should show a large shift to higher temporal frequencies after motion adaptation. We show little change in the temporal or spatial frequency (and hence velocity) optima following adaptation.

Motion detection in insect orientation and navigation

The visual systems of insects are exquisitely sensitive to motion. Over the past 40 years or so, motion processing in insects has been studied and characterised primarily through the optomotor response. This response, which is a turning response evoked by the apparent movement of the visual environment, serves to stabilise the insect's orientation with respect to the environment. Research over the past decade, however, is beginning to reveal the existence of a variety of other behavioural responses in insects, that use motion information in different ways. Here we review some of the recently characterised behaviours, describe the inferred properties of the underlying movement-detecting processes, and propose modified or new models to account for them.

Seeing better at night: life style, eye design and the optimum strategy of spatial and temporal summation

Animals which need to see well at night generally have eyes with wide pupils. This optical strategy to improve photon capture may be improved neurally by summing the outputs of neighbouring visual channels (spatial summation) or by increasing the length of time a sample of photons is counted by the eye (temporal summation). These summation strategies only come at the cost of spatial and temporal resolution. A simple analytical model is developed to investigate whether the improved photon catch afforded by summation really improves vision in dim light, or whether the losses in resolution actually make vision worse. The model, developed for both vertebrate camera eyes and arthropod compound eyes, calculates the finest spatial detail perceivable by a given eye design at a specified light intensity and image velocity. Visual performance is calculated for the apposition compound eye of the locust, the superposition compound eye of the dung beetle and the camera eye of the nocturnal toad. The results reveal that spatial and temporal summation is extremely beneficial to vision in dim light, especially in small eyes (e.g. compound eyes), which have a restricted ability to collect photons optically. The model predicts that using optimum spatiotemporal summation the locust can extend its vision to light intensities more than 100,000 times dimmer than if it relied on its optics alone. The relative amounts of spatial and temporal summation predicted to be optimal in dim light depend on the image velocity. Animals which are sedentary and rely on seeing small, slow images (such as the toad) are predicted to rely more on temporal summation and less on spatial summation. The opposite strategy is predicted for animals which need to see large, fast images. The predictions of the model agree very well with the known visual behaviours of nocturnal animals.

Visual motion: dendritic integration makes sense of the world

Flies use a system of specialised neurons to read the patterns of visual motion - optic flow - induced by the their movements. Recent experiments illustrate how the dendrites of these neurons reach out to assemble patterns of optic flow and encode them reliably.

Dendritic structure and receptive-field organization of optic flow processing interneurons in the fly

The third visual neuropil (lobula plate) of the blowfly Calliphora erythrocephala is a center for processing motion information. It contains, among others, 10 individually identifiable "vertical system" (VS) neurons responding to visual wide-field motions of arbitrary patterns. We demonstrate that each VS neuron is tuned to sense a particular aspect of optic flow that is generated during self-motion. Thus the VS neurons in the fly supply visual information for the control of head orientation, body posture, and flight steering. To reveal the functional organization of the receptive fields of the 10 VS neurons, we determined with a new method the distributions of local motion sensitivities and local preferred directions at 52 positions in the fly's visual field. Each neuron was identified by intracellular staining with Lucifer yellow and three-dimensional reconstructions from 10-micron serial sections. Thereby the receptive-field organization of each recorded neuron could be correlated with the location and extent of its dendritic arborization in the retinotopically organized neuropil of the lobula plate. The response fields of the VS neurons, i.e., the distributions of local preferred directions and local motion sensitivities, are not uniform but resemble rotatory optic flow fields that would be induced by the fly during rotations around various horizontal axes. Theoretical considerations and quantitative analyses of the data, which will be presented in a subsequent paper, show that VS neurons are highly specialized neural filters for optic flow processing and thus for the visual sensation of self-motions in the fly.

Appetitive responses to computer-generated visual stimuli by the praying mantis Sphodromantis lineola (Burr.)

Tethered adult female praying mantises, Sphodromantis lineola (Burr.), were presented with various computer-generated visual stimuli that moved against patterned or homogeneous white backgrounds in predetermined patterns and at predetermined speeds. The degrees to which the stimulus configurations elicited appetitive behaviors (attempting to approach and/or striking) indicated the relative degrees to which the stimuli were classified as prey. Mantises readily struck at cartoon "crickets" that subtended visual angles as great as 24.5 deg x 62.5 deg, but response rate was suppressed if the stimuli were superimposed on horizontally moving patterned backgrounds. Mantises also displayed appetitive behaviors to moving black squares (edge lengths = 10-47 deg) that moved in predetermined "erratic" paths; however, their response rates were affected by several factors: (1) response rate declined as edge length increased over 10 deg; (2) striking was emitted to stimuli viewed from 23 mm (but not farther) away; and (3) both stimulus displacement rate (distance moved between video frames) and apparent speed (video frame rate) dramatically affected the releasing strength of the stimuli. Finally, mantises responded appetitively to random dot patterns moving synchronously against identically patterned backgrounds and to pairs of black squares moving synchronously against a white background. However, in the latter case, response rate declined as the squares were moved farther apart horizontally or vertically. These and previous results from our laboratory on mantises are congruent with behavioral results obtained from other insects such as flies (Diptera) and dragon flies (Odonata) and suggest that there are neuroanatomical similarities between these groups.

Are there separate ON and OFF channels in fly motion vision?

Visual information is processed in a series of subsequent steps. The performance of each of these steps depends not only on the computations it performs itself but also on the representation of the visual surround on which it operates. Here we investigate the consequences of signal preprocessing for the performance of the motion-detection system of the fly. In particular, we analyze whether the retinal input signals are rectified and segregate into separate ON and OFF channels, which then feed independent parallel motion-detection pathways. We recorded the activity of an identified directionally selective interneuron (H1-cell) in response to apparent motion stimuli, i.e. sequential brightness changes at two neighboring locations in the visual field, as well as to brightness changes at only a single location. For apparent motion stimuli, the motion-dependent response component was determined by subtracting from the overall response to the individual stimulus components when presented alone. The following conclusions could be derived: (1) Apparent motion consisting of a sequence of increased or decreased brightness at two locations in the visual field have the same optimum interstimulus time interval (Fig. 3). (2) Sequences of brightness steps of like polarity (either increments or decrements) elicit positive and negative motion-dependent response components when mimicking motion in the cell's preferred and null direction, respectively. The motion-dependent response components are inverted in sign when the brightness steps of a stimulus sequence have a different polarity (Fig. 7). (3) The responses to the beginning and the end of a brightness pulse depend on the pulse duration. For pulse durations of less than 2 s, both events interact with each other (Fig. 9). All of these results do not provide any indication that the fly processes motion information in independent ON and OFF motion detectors. Brightness changes of both signs are rather represented at the input of the same movement detectors, and interactions between signals resulting from both brightness increments and decrements take their sign into account. This type of preprocessing of the retinal input is argued to render a motion-detection system particularly robust against noise.

Circadian inputs influence the performance of a spiking, movement-sensitive neuron in the visual system of the blowfly

Long-term extracellular recordings from a spiking, movement-sensitive giant neuron (H1) in the third optic ganglion of the blowfly Calliphora vicina (L.) revealed periodic endogenous sensitivity fluctuations. The sensitivity changes showed properties typical of an endogenous circadian rhythm. This was true for the responses in reaction to intensity changes of visual patterns as well as for the responses elicited by pattern movement. For these two types of stimuli, the circadian fluctuations were comparable, but the envelope in the case of responses to movement was more robust. A circadian fluctuation in responses to movement is, therefore, present at the level of single elementary movement detectors. The tonic activity of the neuron was also shown to be under circadian control. In constant darkness (DD) the fluctuation was circadian, whereas in constant light it was not. The subjective light-dark (LD) transitions in the tonic activity in DD closely followed the LD transitions in the holding cages initially; that is, there was low activity at night and high activity during the daytime. The sensitivity fluctuations in response to visual stimuli led the tonic spike activity fluctuations by several hours.

Directional tuning curves, elementary movement detectors, and the estimation of the direction of visual movement

Both the insect brain and the vertebrate retina detect visual movement with neurons having broad, cosine-shaped directional tuning curves oriented in either of two perpendicular directions. This article shows that this arrangement can lead to isotropic estimates of the direction of movement: for any direction the estimate is unbiased (no systematic errors) and equally accurate (constant random errors). A simple and robust computational scheme is presented that accounts for the directional tuning curves as measured in movement sensitive neurons in the blowfly. The scheme includes movement detectors of various spans, and predicts several phenomena of movement perception in man.