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The nervous system of seeing animals derives information about optic flow in two subsequent steps. First, local motion vectors are calculated from moving retinal images, and second, the spatial distribution of these vectors is analyzed on the dendrites of large downstream neurons. In dipteran flies, this second step relies on a set of motion-sensitive lobula plate tangential cells (LPTCs), which have been studied in great detail in large fly species. Yet, studies on neurons that convey information to LPTCs and neuroanatomical investigations that enable a mechanistic understanding of the underlying dendritic computations in LPTCs are rare. We investigated the subcellular distribution of nicotinic acetylcholine receptors (nAChRs) on two sets of LPTCs: vertical system (VS) and horizontal system (HS) cells in Drosophila melanogaster. In this paper, we describe that both cell types express Dalpha7-type nAChR subunits specifically on higher order dendritic branches, similar to the expression of gamma aminobutyric acid (GABA) receptors. These findings support a model in which directional selectivity of LPTCs is achieved by the dendritic integration of excitatory, cholinergic, and inhibitory GABA-ergic input from local motion detectors with opposite preferred direction. Nonetheless, whole-cell recordings in mutant flies without Dalpha7 nAChRs revealed that direction selectivity of VS and HS cells is largely retained. In addition, mutant LPTCs were responsive to acetylcholine and remaining nAChR receptors were labeled by alpha-bungarotoxin. These results in LPTCs with genetically manipulated excitatory input synapses suggest a robust cellular implementation of dendritic processing that warrants direction selectivity. The underlying mechanism that ensures appropriate nAChR-mediated synaptic currents and the functional implications of separate sets or heteromultimeric nAChRs can now be addressed in this system.

SummaryWithin the last 400 million years, insects have radiated into at least a million species, accounting for more than half of all known living organisms: they are the most successful group in the animal kingdom, found in almost all environments of the planet, ranging in body size from a mere 0.1 mm up to half a meter. Their eyes, together with the respective parts of the nervous system dedicated to the processing of visual information, have long been the subject of intense investigation but, with the exception of some very basic reflexes, it is still not possible to link an insect's visual input to its behavioral output. Fortunately for the field, the fruit fly Drosophila is an insect, too. This genetic workhorse holds great promise for the insect vision field, offering the possibility of recording, suppressing or stimulating any single neuron in its nervous system. Here, I shall give a brief synopsis of what we currently know about insect vision, describe the genetic toolset available in Drosophila and give some recent examples of how the application of these tools have furthered our understanding of color and motion vision in Drosophila.

Many motion-sensitive tangential cells of the lobula plate in blowflies are well described with respect to their visual response properties and the connectivity among them. In addition to extensive connections between tangential cells within the lobula plate of one brain hemisphere, there exist many connections between the two hemispheres. Most of these connections have been found for neurons sensitive to horizontal motion. For neurons sensitive to vertical motion, however, only the connection of vertical sensitive cells (VS cells) and a cell (V1 cell) projecting to the other hemisphere has been demonstrated thus far. The ability to identify the presynaptic and postsynaptic cells as well as the good accessibility has made this specific synapse a model for graded transmission of synapses. However, the exact type of synapse, electrical or chemical, is not known. Investigating the connectivity between VS cells 1–3 and the V1 cell by means of dual recordings, we find that the VS cells are coupled via electrical synapses to the V1 cell. The results were confirmed by visualizing dye coupling between VS cells and V1.

SummaryThe crystalline-like structure of the optic lobes of the fruit fly Drosophila melanogaster has made them a model system for the study of neuronal cell-fate determination, axonal path finding, and target selection. For functional studies, however, the small size of the constituting visual interneurons has so far presented a formidable barrier. We have overcome this problem by establishing in vivo whole-cell recordings [1] from genetically targeted visual interneurons of Drosophila. Here, we describe the response properties of six motion-sensitive large-field neurons in the lobula plate that form a network consisting of individually identifiable, directionally selective cells most sensitive to vertical image motion (VS cells [2, 3]). Individual VS cell responses to visual motion stimuli exhibit all the characteristics that are indicative of presynaptic input from elementary motion detectors of the correlation type [4, 5]. Different VS cells possess distinct receptive fields that are arranged sequentially along the eye's azimuth, corresponding to their characteristic cellular morphology and position within the retinotopically organized lobula plate. In addition, lateral connections between individual VS cells cause strongly overlapping receptive fields that are wider than expected from their dendritic input. Our results suggest that motion vision in different dipteran fly species is accomplished in similar circuitries and according to common algorithmic rules. The underlying neural mechanisms of population coding within the VS cell network and of elementary motion detection, respectively, can now be analyzed by the combination of electrophysiology and genetic intervention in Drosophila.

Neurons in the nervous system can change their functional properties over time. At present, there are no techniques that allow reliable monitoring of changes within identified neurons over repeated experimental sessions. We increased the signal strength of troponin C-based calcium biosensors in the low-calcium regime by mutagenesis and domain rearrangement within the troponin C calcium binding moiety to generate the indicator TN-XXL. Using in vivo two-photon ratiometric imaging, we show that TN-XXL exhibits enhanced fluorescence changes in neurons of flies and mice. TN-XXL could be used to obtain tuning curves of orientation-selective neurons in mouse visual cortex measured repeatedly over days and weeks. Thus, the genetically encoded calcium indicator TN-XXL allows repeated imaging of response properties from individual, identified neurons in vivo, which will be crucial for gaining new insights into cellular mechanisms of plasticity, regeneration and disease.

Drosophila has emerged as an important model organism for the study of the neural basis of behavior. Its main asset is the experimental accessibility of identified neurons by genetic manipulation and physiological recordings. Drosophila therefore offers the opportunity to reach an integrative understanding of the development and neural underpinnings of behavior at all processing stages, from sensing to motor control, in a single species. Here, we will provide an account of the procedures involved in recording the electrical potential of individual neurons in the visual system of adult Drosophila using the whole-cell patch-clamp method. To this end, animals are fixed to a holder and mounted below a recording chamber. The head capsule is cut open and the glial sheath covering the brain is ruptured by a combination of shearing and enzymatic digest. Neuronal somata are thus exposed and targeted by low-resistance patch electrodes. After formation of a high resistance seal, electrical access to the cell is gained by small current pulses and suction. Stable recordings of large neurons are feasible for >1 h and can be combined with controlled visual stimulation as well as genetic and pharmacological manipulation of upstream circuit elements to infer circuit function in great detail.

In this work, we studied the adaptation of H1, a motion-sensitive neuron in the fly visual system, to the variance of randomly fluctuating velocity stimuli. We ask two questions. 1) Which components of the motion detection system undergo genuine adaptational changes in response to the variance of the fluctuating velocity signal? 2) What are the consequences of this adaptation for the information processing capabilities of the neuron? To address these questions, we characterized the adaptation of H1 by estimating the changes in the parameters of an associated Reichardt motion detection model under various stimulus conditions. The strongest stimulus dependence was exhibited by the temporal kernel of the motion detector and was parametrized by changes in the model's high-pass time constant (τH). This time constant shortened considerably with increasing velocity fluctuations. We showed that this adaptive process contributes significantly to the shortening of the velocity response time-course but not to velocity gain control. To assess the contribution of time-constant adaptation to information transmission, we compared the information rates generated by our adaptive model motion detector with model simulations in which τH was held fixed at its unadapted value for all stimulus conditions. We found that for intermediate stimulus conditions, fixing τH at its unadapted value led to higher information rates, suggesting that time-constant adaptation does not optimize total information rates about velocity trajectories. We also found that, over the wide range of stimulus conditions tested here, H1 information rates are dependent on the amplitude of velocity fluctuations.

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.

Neurons in many species have large receptive fields that are selective for specific optic flow fields. Here, we studied the neural mechanisms underlying flow field selectivity in lobula plate tangential cells (LPTCs) of the blowfly. Among these cells, the H2 cell responds preferentially to visual stimuli approximating rotational optic flow. Through double recordings from H2 and many other LPTCs, we characterized a bidirectional commissural pathway that allows visual information to be shared between the hemispheres. This pathway is mediated by axo-axonal electrical coupling of H2 and the horizontal system equatorial (HSE) cell located in the opposite hemisphere. Using single-cell ablations, we found that this pathway is sufficient to allow H2 to amplify and attenuate dendritic input during binocular visual stimuli. This is accomplished through a modulation of H2's membrane potential by input from the contralateral HSE cell, which scales the firing rate of H2 during visual stimulation but is not sufficient to induce action potentials.

Influx of Ca(2+) and Na(+) ions during an action potential can strongly affect the repolarization and the fast afterhyperpolarization (fAHP) if a neuron expresses Ca(2+)- and Na(+)-dependent K(+) currents (K(Ca) and K(Na)). This applies to cockroach abdominal dorsal unpaired median neurons (DUMs). Here the rapid activation of K(Ca) depends mainly on the P/Q-type Ca(2+) current. Adipokinetic hormones (AKHs)-insect counterparts to mammalian glucagon-mobilize energy reserves but also modulate neuronal activity and lead to enhanced locomotor activity. Cockroach AKH I accelerates spiking and enhances the fAHP of octopaminergic DUM neurons, and it is generally held that enhanced release of the biogenic amine from these and other neurons may lead to general arousal. AKH I modulates the voltage-gated Na(+) and P/Q-type Ca(2+) current and the background Ca(2+) current. Upregulation of P/Q-type Ca(2+) current increases the K(Ca) current, whereas enhanced inactivation of Na(+) current decreases the K(Na) current. We quantified the hormone-induced changes in ion currents in terms of Hodgkin-Huxley models and simulated the resulting activity of DUM neurons. Upregulation of P/Q-type Ca(2+) and K(Ca) current enhanced the hyperpolarization but had a weak effect on spiking. Downregulation of Na(+) and K(Na) current decreased hyperpolarization and slightly accelerated spiking. Superposition of these modulations produced an increase in fAHP while the spike frequency remained unchanged. Only when the upregulation of the pacemaking Ca(2+) background current was included in the simulated modulation the model reproduced the experimentally observed AKH-I-induced changes. The possible physiological relevance of this dual effect is discussed in respect to transmitter release and synaptic integration.

At the Drosophila melanogaster larval neuromuscular junction (NMJ), a motor neuron releases glutamate from 30–100 boutons onto the muscle it innervates. How transmission strength is distributed among the boutons of the NMJ is unknown. To address this, we created synapcam, a version of the Ca2+ reporter Cameleon. Synapcam localizes to the postsynaptic terminal and selectively reports Ca2+ influx through glutamate receptors (GluRs) with single-impulse and single-bouton resolution. GluR-based Ca2+ signals were uniform within a given connection (that is, a given bouton/postsynaptic terminal pair) but differed considerably among connections of an NMJ. A steep gradient of transmission strength was observed along axonal branches, from weak proximal connections to strong distal ones. Presynaptic imaging showed a matching axonal gradient, with higher Ca2+ influx and exocytosis at distal boutons. The results suggest that transmission strength is mainly determined presynaptically at the level of individual boutons, possibly by one or more factors existing in a gradient. *Note: In the version of this article initially published online, the second author’s name was misspelled. The correct spelling should be Dierk F Reiff. The error has been corrected in the HTML version of the article. This correction has been appended to the PDF and print versions.

Genetically encoded fluorescent probes of neural activity represent new promising tools for systems neuroscience. Here, we present a comparativein vivoanalysis of 10 different genetically encoded calcium indicators, as well as the pH-sensitive synapto-pHluorin. We analyzed their fluorescence changes in presynaptic boutons of theDrosophilalarval neuromuscular junction. Robust neural activity did not result in any or noteworthy fluorescence changes when Flash-Pericam, Camgaroo-1, and Camgaroo-2 were expressed. However, calculated on the raw data, fractional fluorescence changes up to 18% were reported by synapto-pHluorin, Yellow Cameleon 2.0, 2.3, and 3.3, Inverse-Pericam, GCaMP1.3, GCaMP1.6, and the troponin C-based calcium sensor TN-L15. The response characteristics of all of these indicators differed considerably from each other, with GCaMP1.6 reporting high rates of neural activity with the largest and fastest fluorescence changes. However, GCaMP1.6 suffered from photobleaching, whereas the fluorescence signals of the double-chromophore indicators were in general smaller but more photostable and reproducible, with TN-L15 showing the fastest rise of the signals at lower activity rates. We show for GCaMP1.3 and YC3.3 that an expanded range of neural activity evoked fairly linear fluorescence changes and a corresponding linear increase in the signal-to-noise ratio (SNR). The expression level of the indicator biased the signal kinetics and SNR, whereas the signal amplitude was independent. The presented data will be useful forin vivoexperiments with respect to the selection of an appropriate indicator, as well as for the correct interpretation of the optical signals.

Many sensory systems adapt their input-output relationship to changes in the statistics of the ambient stimulus. Such adaptive behavior has been measured in a motion detection sensitive neuron of the fly visual system, H1. The rapid adaptation of the velocity response gain has been interpreted as evidence of optimal matching of the H1 response to the dynamic range of the stimulus, thereby maximizing its information transmission. Here, we show that correlation-type motion detectors, which are commonly thought to underlie fly motion vision, intrinsically possess adaptive properties. Increasing the amplitude of the velocity fluctuations leads to a decrease of the effective gain and the time constant of the velocity response without any change in the parameters of these detectors. The seemingly complex property of this adaptation turns out to be a straightforward consequence of the multidimensionality of the stimulus and the nonlinear nature of the system.

Summary The recent Cell Symposium ‘Genes, Circuits and Behavior' brought together researchers working on neural circuits in vertebrate and invertebrate species. In the interest of fostering communication across the ‘backbone-divide', we asked a number of neuroscientists from both camps for their views on the extent to which insights obtained from studies on neural circuits in one type of species are transferable to the other.

The computational structure of an optimal motion detector was proposed to depend on the signal-to-noise ratio (SNR) of the stimulus: At low SNR, the optimal motion detector should be a correlation or “Reichardt” type, whereas at high SNR, the detector would employ a gradient scheme [Potters, M. & Bialek, W. (1994) J. Physiol. (Paris) 4, 1755-1775]. Although a large body of experiments supports the Reichardt detector as the processing scheme leading to direction selectivity in fly motion vision, in most of these studies the SNR was rather low. We therefore reinvestigated the question over a much larger SNR range. Using 2-photon microscopy, we found that local dendritic [Ca 2+ ] modulations, which are characteristic of Reichardt detectors, occur in response to drifting gratings over a wide range of luminance levels and contrasts. We also explored, as another fingerprint of Reichardt detectors, the dependence of the velocity optimum on the pattern wavelength. Again, we found Reichardt-typical behavior throughout the whole luminance and contrast range tested. Our results, therefore, provide strong evidence that only a single elementary processing scheme is used in fly motion vision.

Two types of transient responses have been investigated in fly motion-sensitive neurons in the past: the impulse and the step response. In response to a brief motion pulse, cells show a sudden rise in activity followed by an exponential decay (‘impulse response’). In response to the onset of a constant velocity stimulus, cells exhibit transient oscillations before settling to a steady-state value (‘step response’). Since the impulse response has been shown to shorten when tested after presentation of an adapting motion stimulus, we investigated whether adaptation also occurs during the step response. We tested this hypothesis by recording extracellularly the response of the H1-cell in the lobula plate of the blowfly Calliphora vicina to gratings of varying pattern contrasts and drift velocity.We found that the transient oscillations of the step response strongly depend on the pattern contrast: at low contrasts, oscillations lasted for several seconds, whereas at high contrasts, they settled within fractions of a second. This suggests that motion adaptation occurs during the initial period of the stimulus presentation and is dependent on the contrast of the motion stimulus. Using identical stimulus parameters (contrast and temporal frequency) for the adapting stimulus and testing the impulse response afterwards, we found that the impulse response and the transient period in the step response shortened in a similar way.We then analyzed the dynamic of the transients oscillations produced by ongoing motion of a square wave pattern in the anti-preferred direction (null direction) of H1. As observed for preferred direction motion, we found that the duration and amplitude of those transients shortened as the contrast and the velocity of the pattern increased, and that the oscillations disappeared when a blank screen instead of a pattern was presented before the onset of motion. Under both stimulus conditions, i.e. grating and blank screen before motion onset, the steady-state response level showed the same dependence on the contrast and temporal frequency of the pattern.When we analyzed the responses of the cell to pattern of various sizes and contrasts moving in the preferred direction of the cell, we found that increments in the size affected the overall amplitude of both the transient oscillations and the steady-state response level, whereas the duration of the oscillations only depended on the local pattern contrast. We also tested the impulse response before and after the presentation of an adapting stimulus presented in either the same or a different location of the visual field. The response shortened only when both the adapting and the test stimuli were presented at the same location. These last experiments demonstrate a strictly local mechanism of adaptation affecting the response transients of both the impulse and the step response.

The so-called ‘Reichardt detector’ can successfully account for many properties of fly motion vision. In its simplest form, the signals derived from neighboring image locations become multiplied after a low-pass filter has delayed one of them. This operation is done twice in a mirror-symmetrical form and the resulting output signals become finally subtracted. As predicted by this model, fly neurons respond to a brief motion pulse with a sudden rise in activity followed by an exponential decay. The time constant of this decay has been shown to shorten when tested after presentation of an adapting motion stimulus. In terms of the detector model this inevitably implies that the time constant of the low-pass filter is adapting. Given that, one would expect a concomitant shift of the steady-state response towards higher velocities, which, however, could not be experimentally verified. Here, we show that given a model with an additional temporal high-pass filter in the cross-arms of the detector, only the high-pass filter determines the time course of the impulse response. Assuming consequently that the time constant of the high-pass filter is the locus of adaptation resolves the conflicts mentioned above. Moreover, such an elaborated model with an adaptive time-constant faithfully mimics a particular contrast-dependency of transient response oscillations observed in fly motion sensitive neurons.

In the quest for deciphering the neural code, theoretical advances were made which allow for the determination of the information rate inherent in the spike trains of nerve cells. However, up to now, the dependence of the information rate on stimulus parameters has not been studied in any neuron in a systematic way. Here, I investigate the information carried by the spike trains of H1, a motion-sensitive visual interneuron of the blowfly (Calliphora vicina) using a moving grating as a stimulus. Stimulus parameters fall in two classes: those that have only a minor effect on the information rate like increasing the frequency bandwidth or the maximum amplitude of the stimulus velocity, and those which dramatically affect the neural information rate, like varying the spatial size or the contrast of the visual pattern being moved. It appears that, for a broad range of complex stimuli, the neuron covers the stimulus with its whole response repertoire regardless of the stimulus entropy, with the information rate being limited by the noise of the stimulus and the neural hardware.

Motion-sensitive neurons have long been studied in both the mammalian retina and the insect optic lobe, yet striking similarities have become obvious only recently. Detailed studies at the circuit level revealed that, in both systems, (i) motion information is extracted from primary visual information in parallel ON and OFF pathways; (ii) in each pathway, the process of elementary motion detection involves the correlation of signals with different temporal dynamics; and (iii) primary motion information from both pathways converges at the next synapse, resulting in four groups of ON-OFF neurons, selective for the four cardinal directions. Given that the last common ancestor of insects and mammals lived about 550 million years ago, this general strategy seems to be a robust solution for how to compute the direction of visual motion with neural hardware.

For visual course control, flies rely on a set of motion-sensitive neurons called lobula plate tangential cells (LPTCs). Among these cells, the so-called CH (centrifugal horizontal) cells shape by their inhibitory action the receptive field properties of other LPTCs called FD (figure detection) cells specialized for figure-ground discrimination based on relative motion. Studying the ipsilateral input circuitry of CH cells by means of dual-electrode and combined electrical-optical recordings, we find that CH cells receive graded input from HS (large-field horizontal system) cells via dendro-dendritic electrical synapses. This particular wiring scheme leads to a spatial blur of the motion image on the CH cell dendrite, and, after inhibiting FD cells, to an enhancement of motion contrast. This could be crucial for enabling FD cells to discriminate object from self motion.

Flies actively turn their head during flight to stabilize their gaze and reduce motion blur. This optomotor response is triggered by wide-field motion indicating a deviation from a desired flight path. We focus on the neuronal circuit that underlies this behavior in the blowfly Calliphora, studying the integration of optic flow in neck motor neurons that innervate muscles controlling head rotations. Frontal nerve motor neurons (FNMNs) have been described anatomically and recorded from extracellularly before. Here, we assign for the first time to five anatomical classes of FNMNs their visual motion tuning. We measured their responses to optic flow, as produced by rotations around particular body axes, recording intracellularly from single axons. Simultaneous injection of Neurobiotin allowed for the anatomical characterization of the recorded cells and revealed coupling patterns with neighboring neurons. The five FNMN classes can be divided into two groups that complement each other, regarding their preferred axes of rotation. The tuning matches the pulling planes of their innervated neck muscles, serving to rotate the head around its longitudinal axis. Anatomical and physiological findings demonstrate a synaptic connection between one FNMN and a well-described descending neuron, elucidating one important step from visual motion integration to neck motor output.

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).

Motion-sensitive large-field neurons found at higher processing stages in many species often exhibit a remarkable selectivity for particular flow fields. However, the underlying neural mechanisms are not yet understood. We studied this problem in the so-called lobula plate tangential cells (LPTCs) of the fly. Investigating the connectivity between LPTCs by means of dual recordings, we find two types of connections: (1) heterolateral connections between LPTCs of both hemispheres and (2) ipsilateral connections between LPTCs within one lobula plate. The circuit is suitable to amplify incoming, dendritic signals in the case of rotatory flow fields and to reduce them in the case of other flow-field structures. In addition to feedforward connectivity, thus, the flow-field selectivity of LPTCs may be significantly attributable to recurrent excitation involving the network of large-field neurons in both brain hemispheres.

We investigated the effect of mean firing on the information rate of a spiking motion-sensitive neuron in the fly (H1-cell). In the control condition, the cell was stimulated repeatedly by identical zero-symmetrical white-noise motion. The mean firing rate was manipulated by adding a constant velocity offset either in the same area of the receptive field where the dynamic stimulus was displayed or in a separate one. We determined the information rate in the resulting spike trains in the time domain as the difference between the total and the noise entropy rate and found that the information rate increases with increasing mean firing under both stimulus conditions.

This study describes the spatial response properties of a particular group of motion-sensitive and directionally selective neurons located in the lobula plate of the fly visual system. Their preferred motion direction is front-to-back (depolarization), and their null direction is back-to-front (hyperpolarization). They receive inhibitory input from the contralateral eye during pattern motion from back to front (regressive). In this study, we call these neurons regressive contralateral inhibited lobula plate tangential cells (rCI-LPTCs). Three physiologic groups of rCI-neurons (rCI-I, rCI-IIa, and rCI-IIb) can be distinguished on the basis of their ipsilateral pattern size dependence and their inhibitory contralateral input. rCI-I neurons depolarize during the motion of small ipsilateral patterns from front to back, but they become hyperpolarized by large ipsilateral patterns moving in the same direction. rCI-IIa and rCI-IIb neurons receive bidirectional inhibitory input from the contralateral eye. rCI-IIa neurons respond best to small ipsilateral pattern sizes, but unlike rCI-I neurons, their net response to large patterns is positive. rCI-IIb neurons respond best to large ipsilateral patterns. The anatomical and physiologic variability of the rCI-neurons suggests that more than three types of rCI-neurons exist. The suggested physiologic groups might be preliminary. We recorded one neuron that could mediate the bidirectional inhibitory input that rCI-IIa and rCI-IIb neurons receive from the contralateral eye. In the case of the rCI-IIa neurons at least one further contralateral inhibitory element has to be assumed. The tuning of rCI-IIa neurons to small ipsilateral pattern sizes is likely to be based on an on-center/off-surround organization of their synaptic input.

In this last paper in a series (Borst and Haag, 1996; Haag et al., 1997) about the lobula plate tangential cells of the fly visual system (CH, HS, and VS cells), the visual response properties were examined using intracellular recordings and computer simulations. In response to visual motion stimuli, all cells responded mainly by a graded shift of their axonal membrane potential. While ipsilateral motion resulted in a graded membrane potential shift, contralateral motion led to distinct EPSPs. For HS cells, simultaneous extracellular recorded action potentials of a spiking interneuron, presumably the H2 cell, corresponded to the EPSPs in the HS cell in a one-to-one fashion. When HS cells were hyperpolarized during ipsilateral motion, they mainly produced action potentials, but when they were hyperpolarized during contralateral motion only a slight increase of EPSP amplitude, could be observed. Intracellular application of the sodium channel blocker QX 314 abolished action potentials of HS cells while having little effect on the graded membrane response to ipsilateral motion. HS and CH cells were also studied with respect to their spatial integration properties. For both cell types, their graded membrane response was found to increase less than linearly with the size of the ipsilateral motion pattern. However, while for HS cells various amounts of hyperpolarizing current injected during motion stimulation led to different saturation levels, this was not the case for CH cells. In response to a sinusoidal velocity modulation, CH cells followed pattern motion only up to 10 Hz modulation frequency, but HS cells still revealed significant membrane depolarizations up to about 40 Hz. In the computer simulations, the compartmental models of tangential cells, as derived in the previous papers, were linked to an array of local motion detectors. The model cells revealed the same basic response features as their natural counterparts. They showed a response saturation as a function of stimulus size. In CH-models, however, the saturation was less pronounced than in real CH-cells, indicating spatially nonuniform membrane resistances with higher values in the dendrite. As in the experiments, HS models responded to high-frequency velocity modulation with a higher amplitude than did CH models.

Antibodies against the Drosophila gamma-aminobutyric acid (GABA) receptor subunit RDL were used to investigate the significance of inhibitory inputs to the mushroom bodies in the blowfly (Calliphora erythrocephala) brain. The pedunculus and the lobes of the mushroom body, which mainly consist of Kenyon cell fibers, revealed strong immunoreactivity against RDL. Pedunculi, alpha- and beta-lobe show characteristic unstained core structures with concentric labeling along the neuropile axis. The gamma-lobes in contrast exhibit a compartmentalized RDL-immunoreactive pattern. These data suggest an important role of GABAergic inhibition in the pedunculus and the lobes of insect mushroom bodies. It is most likely that the RDL-immunoreactivity in the mushroom bodies is closely related to Kenyon cell fibers suggesting that Kenyon cells are an inhomogeneous class of neurons, only part of which receive inhibitory GABAergic input from extrinsic elements. GABAergic inhibition, therefore, may play a substantial role in the process of learning and memory formation in the insect mushroom bodies.

We investigated the information about stimulus velocity inherent in the membrane signals of two types of directionally selective, motion-sensitive interneurons in the fly visual system. One of the cells, the H1-cell, is a spiking neuron, whereas the other, the HS-cell, encodes sensory information mainly by a graded shift of its membrane potential. Using a pseudo-random velocity waveform by which a visual grating is moving along the horizontal axis of the eye, both cell types follow the stimulus velocity at higher precision than in response to a step-like velocity function. To measure how much information about the stimulus velocity is preserved in the cellular responses, we calculated the coherence between the stimulus and the neural signals as a function of stimulus frequency. At frequencies up to approximately 10 Hz motion information is well contained in the electrical signals of HS- and H1-cells: For HS-cells the coherence value amounts to approximately 70%, and for H1-cells this value is approximately 60%. Comparing these values with the coherence expected from a linear encoding reveals that the fidelity of the original stimulus is deteriorated in the neural signal partly by neural noise and partly by the nonlinearity inherent in the process of visual motion detection. The degree to which this nonlinearity contributes to the decrease in coherence depends on the maximum velocity used in the experiments; the smaller the stimulus amplitude, the higher the coherence and, thus, the smaller the nonlinearity in encoding of stimulus motion. All these results are in agreement with model simulations in which visual motion is processed by an array of local motion detectors, the spatially integrated output of which is considered the equivalent of the neural signals of HS- and H1-cells.

In this study, the distribution of synapses in the ventral centrifugal horizontal (VCH)-a nonspiking, inhibitory, motion-sensitive interneuron in the third visual ganglion (lobula plate) of the blowfly Calliphora erythrocephala-was examined by electron microscopy and electrophysiology. The frequency histograms of excitatory and inhibitory postsynaptic potential amplitudes recorded from the VCH during contralateral stimulus presentation suggest the existence of three neurons, two excitatory and one inhibitory, mediating contralateral input. To localize input and output regions of the VCH, we investigated the ultrastructure of its two arborisation areas after intracellular iontophoretic injection of horseradish peroxidase. The VCH has input synapses in its arborisation in the protocerebrum and in its arborisation in the lobula plate. Output synapses were found exclusively in the lobula plate. Thus, the large dendritic arbor of the VCH in the lobula plate serves simultaneously as an input and an output region. There, we found input and output synapses in close vicinity (0.5-1.5 microm) to each other. Taking into account that the VCH receives retinotopicly arranged input from the ipsilateral eye in the lobula plate, the close location of input and output synapses in the VCH suggests that the spatial organization of its retinotopic synaptic input is more or less conserved in its inhibitory output pattern. The VCH has been proposed to inhibit the figure detection 1 (FD1), another neuron of the lobula plate, that responds preferentially to small moving objects. These results suggest that the FD1 may receive inhibitory inputs from the VCH in the lobula plate, where the dendritic arbors of both neurons overlap.

Summary Background Detecting the direction of visual motion is an essential task of the early visual system. The Reichardt detector has been proven to be a faithful description of the underlying computation in insects. A series of recent studies addressed the neural implementation of the Reichardt detector in Drosophila revealing the overall layout in parallel ON and OFF channels, its input neurons from the lamina (L1→ON, and L2→OFF), and the respective output neurons to the lobula plate (ON→T4, and OFF→T5). While anatomical studies showed that T4 cells receive input from L1 via Mi1 and Tm3 cells, the neurons connecting L2 to T5 cells have not been identified so far. It is, however, known that L2 contacts, among others, two neurons, called Tm2 and L4, which show a pronounced directionality in their wiring. Results We characterized the visual response properties of both Tm2 and L4 neurons via Ca 2+ imaging. We found that Tm2 and L4 cells respond with an increase in activity to moving OFF edges in a direction-unselective manner. To investigate their participation in motion vision, we blocked their output while recording from downstream tangential cells in the lobula plate. Silencing of Tm2 and L4 completely abolishes the response to moving OFF edges. Conclusions Our results demonstrate that both cell types are essential components of the Drosophila OFF motion vision pathway, prior to the computation of directionality in the dendrites of T5 cells.

Understanding the computational power of individual neurons is one of the major tasks in neuroscience. Complex calculations in dendrites and axons were identified in recent years in a great number of different systems. The small set of around 60 lobula plate tangential cells (LPTCs) in the fly visual system is a prime example where such computations and their underlying mechanisms are well understood. In this chapter we review recent findings resulting from detailed modelling studies based on experimental data from LPTCs. These studies have shown that the network connectivity is sufficient to explain the morphology of LPTCs to a large extent. Furthermore, an extensive network ubiquitously but highly selectively connects LPTC dendrites and axons with each other and is responsible for sophisticated optic flow calculations. The electrotonic features of LPTCs are affected by this network. We describe how a selective dendritic network between Horizontal System (HS) and Centrifugal Horizontal (CH) cells implements a specialised spatial filter operating on the moving images encoded in the dendrites. We also show how an axonal network renders the axon terminals of Vertical System (VS) cells in the fly selective to rotational optic flow. In summary, fly LPTCs are a prime example to show that the embedding and wiring within the network is crucial to understand morphology and computation.

The extraction of optic flow fields by visual systems is crucial for course stabilization during locomotion, and relies on feedforward and lateral integration of visual inputs. Here we report a novel form of systemic, motion-sensitive lateral suppression in the dendrites of large, flow-field-selective neurons in the fly's visual lobes. Usingin vivoCalcium-imaging and intracellular recordings, we demonstrate that responses in dendrites, but not axon terminals, are end inhibited by flanking gratings both in the vertical and horizontal systems. We show evidence for a mechanism involving wide-field dendritic inhibition that exceeds the retinotopic spatial extent of the dendrites. Using compartmental modeling, we point out a possible function in enhancing selectivity for optic flow fields. Our results suggest that lateral suppression is a common element serving similar functional requirements in different visual systems.

Different visual features of an object, such as its position and direction of motion, are important elements for animal orientation, but the neural circuits extracting them are generally not well understood. We analyzed this problem in Drosophila, focusing on two well-studied behaviors known as optomotor response and fixation response. In the neural circuit controlling the optomotor response, columnar T4 and T5 cells are thought to be crucial. We found that blocking T4 and T5 cells resulted in a complete loss of the optomotor response. Nevertheless, these flies were still able to fixate a black bar, although at a reduced performance level. Further analysis revealed that flies in which T4 and T5 cells were blocked possess an intact position circuit that is implemented in parallel to the motion circuit; the optomotor response is exclusively controlled by the motion circuit, whereas the fixation response is supported by both the position and the motion circuit.

The passive membrane properties of the tangential cells in the fly lobula plate (CH, HS, and VS cells, Fig. 1) were determined by combining compartmental modeling and current injection experiments. As a prerequisite, we built a digital base of the cells by 3D-reconstructing individual tangential cells from cobalt-stained material including both CH cells (VCH and DCH cells), all three HS cells (HSN, HSE, and HSS cells) and most members of the VS cell family (Figs. 2, 3). In a first series of experiments, hyperpolarizing and depolarizing currents were injected to determine steady-state I-V curves (Fig. 4). At potentials more negative than resting, a linear relationship holds, whereas at potentials more positive than resting, an outward rectification is observed. Therefore, in all subsequent experiments, when a sinusoidal current of variable frequency was injected, a negative DC current was superimposed to keep the neurons in a hyperpolarized state. The resulting amplitude and phase spectra revealed an average steady-state input resistance of 4 to 5 M omega and a cut-off frequency between 40 and 80 Hz (Fig. 5). To determine the passive membrane parameters Rm (specific membrane resistance), Ri (specific internal resistivity), and Cm (specific membrane capacitance), the experiments were repeated in computer simulations on compartmental models of the cells (Fig. 6). Good fits between experimental and simulation data were obtained for the following values: Rm = 2.5 k omega cm2, Ri = 60 omega cm, and Cm = 1.5 microF/cm2 for CH cells; Rm = 2.0 k omega cm2, Ri = 40 omega cm, and Cm = 0.9 microF/cm2 for HS cells; Rm = 2.0 k omega cm2, Ri = 40 omega cm, and Cm = 0.8 microF/cm2 for VS cells. An error analysis of the fitting procedure revealed an area of confidence in the Rm-Ri plane within which the Rm-Ri value pairs are still compatible with the experimental data given the statistical fluctuations inherent in the experiments (Figs. 7, 8). We also investigated whether there exist characteristic differences between different members of the same cell class and how much the exact placement of the electrode (within +/-100 microns along the axon) influences the result of the simulation (Fig. 9). The membrane parameters were further examined by injection of a hyperpolarizing current pulse (Fig. 10). The resulting compartmental models (Fig. 11) based on the passive membrane parameters determined in this way form the basis of forthcoming studies on dendritic integration and signal propagation in the fly tangential cells (Haag et al., 1997; Haag and Borst, 1997).

In the flyDrosophila melanogaster, photoreceptor input to motion vision is split into two parallel pathways as represented by first-order interneurons L1 and L2 (Rister et al., 2007; Joesch et al., 2010). However, how these pathways are functionally specialized remains controversial. One study (Eichner et al., 2011) proposed that the L1-pathway evaluates only sequences of brightness increments (ON-ON), while the L2-pathway processes exclusively brightness decrements (OFF-OFF). Another study (Clark et al., 2011) proposed that each of the two pathways evaluates both ON-ON and OFF-OFF sequences. To decide between these alternatives, we recorded from motion-sensitive neurons in flies in which the output from either L1 or L2 was genetically blocked. We found that blocking L1 abolishes ON-ON responses but leaves OFF-OFF responses intact. The opposite was true, when the output from L2 was blocked. We conclude that the L1 and L2 pathways are functionally specialized to detect ON-ON and OFF-OFF sequences, respectively.

A new method is described which allows to image the steady-state distribution of m em ­ brane potential of single neurons in situ. The method consists of staining the tissue with an extracellular voltage-sensitive dye (Di-4-ANEPPS) and impaling a single neuron with a microelectrode. After focusing the imaging system onto the cell a large series of images are taken with a CCD camera at the appropriate excitation wavelength of the voltage-sensitive dye while the neuron’s membrane potential is shifted by a periodic current injection (PCI). Afterwards two groups of images are averaged separately: those images while the cell was at rest and those images while the cell was hyperpolarized. After subtraction of these averaged images, the resulting difference image shows only the membrane potential of the cell which was altered periodically. The success of the method is demonstrated on leech cells in intact ganglia. If applied to cells with a basically two-dimensional arborization pattern, the decrease of activity in the difference image in areas further away from the injection site should relate to a decrease in membrane potential according to the passive electrotonic properties of the cell under study.

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.

One of the most distinguishing features of nerve cells is the vast morphological diversity of their input regions, that is, their dendrites. These range from bulbous structures, with only small protrusions, to large tree-like arborizations. The diversity of nerve cells is further augmented by a continuously increasing number of types of voltage-dependent conductances in dendrites that might alter the postsynaptic signals in a pronounced way. Moreover, intracellular factors such as Ca2+ link electrical activity with biochemical processes, and can induce short and long-term changes in responsiveness. This complexity of neurons in general, and the uniqueness of each cell type, sharply contrasts with the comparatively simple and uniform design principle of the integrate-and-fire units of so-called neuronal net models. This raises the question of which particular structural and physiological details of nerve cells really matter for the performance of neuronal circuits. An answer to this basic problem of computational neurobiology might be given only if the task of the neurons and circuits is known. This review illustrates how the problem can be approached particularly well in sensory interneurons. The functional significance of sensory interneurons can often be assessed more easily than that of central nerve cells because of their vicinity to the sensory surface.

Detecting motion is a feature of all advanced visual systems [1], nowhere more so than in flying animals, such as insects [2,3]. In flies, an influential auto-correlation model for motion detection, the elementary motion detector circuit (EMD; [4,5]), compares visual signals from neighboring photoreceptors to derive information on motion direction and velocity. This information is fed by two types of interneuron, L1 and L2, in the first optic neuropile, or lamina, to downstream local motion detectors in columns of the second neuropile, the medulla. Despite receiving carefully matched photoreceptor inputs, L1 and L2 drive distinct, separable pathways responding preferentially to moving ‘on’- and ‘off’-edges, respectively [6,7]. Our serial EM identifies two types of transmedulla (Tm) target neurons, Tm1 and Tm2, that receive apparently matched synaptic inputs from L2. Tm2 neurons also receive inputs from two retinotopically posterior neighboring columns via L4, a third type of lamina neuron. Light microscopy reveals that the connections in these L2/L4/Tm2 circuits are highly determinate. Single-cell transcript profiling suggests that nicotinic acetylcholine receptors mediate transmission within the L2/L4/Tm2 circuits while L1 is apparently glutamatergic. We propose that Tm2 integrates sign-conserving inputs from neighboring columns to mediate the detection of front-to-back motion generated during forward motion.

Sensory neurons are mostly studied in fixed animals, but their response properties might change when the animal is free to move. Indeed, recent studies found differences between responses of sensory neurons in resting versus moving insects. Since the dynamic range of visual motion stimuli strongly depends on the speed at which an animal is moving, we investigated whether the visual system adapts to such changes in stimulus dynamics as induced by self-motion. Lobula plate tangential cells of flies lend themselves well to study this question because they are known to code for ego-motion based on optic-flow. We recorded the responses of the lobula plate tangential cell H1 to a visual pattern moving at different velocities under three different conditions: fixed flies before and after application of the octopamine agonist chlordimeform (CDM) and tethered flying flies. CDM has been previously shown to induce arousal in flies. We found that flying as well as the application of CDM significantly broadens the velocity tuning of H1 toward higher velocities.

The mechanism for motion detection in a fly's vision system, known as the Reichardt correlator, suffers from a main shortcoming as a velocity estimator: low accuracy. To enable accurate velocity estimation, responses of the Reichardt correlator to image sequences are analyzed in this paper. An elaborated model with additional preprocessing modules is proposed. The relative error of velocity estimation is significantly reduced by establishing a real-time response-velocity lookup table based on the power spectrum analysis of the input signal. By exploiting the improved velocity estimation accuracy and the simple structure of the Reichardt correlator, a high-speed vision system of 1 kHz is designed and applied for robot yaw-angle control in real-time experiments. The experimental results demonstrate the potential and feasibility of applying insect-inspired motion detection to robot control.

It has now been 100 years since Ramon y Cajal described the remarkable diversity of neuronal branching. Only recently, however, have a number of rigorous formalisms emerged providing an accurate quantitative description of axonal and dendritic morphologies. We have launched a freely distributed open-source software package, the TREES toolbox, written in Matlab (Mathworks, Natick, MA), in order to help to pool together the resources offered by a wide variety of novel approaches to studying dendritic and axonal branching that have recently become available. This package introduces a simple general description of neuronal morphology as a graph and provides the basic tools to edit, visualize and analyze neuronal trees in the basis of this description. We then implement our own approach, assuming that neuronal branching can largely be expressed by local optimization of total wiring and conduction distances. We provide the corresponding modular extendable tools to automatically reconstruct neuronal branching from microscopy image stacks and to generate synthetic branched structures. The package is complemented by an extensive user interface to facilitate the generation, visualization and editing of neuronal tree structures. The TREES toolbox is structured to make it easy for other groups to integrate their own code in order to implement their own specific applications. Accurate predictions of computation in single neurons are nowadays well known to require detailed morphological representations. Tools for compartmental modelling such as NEURON, Genesis and neuroConstruct have recently facilitated the modelling of small and large neural circuits involving detailed compartmental models of the neurons. Also, a new trend highlighting the importance of morphology for better understanding of network connectivity adds to the appeal of acquiring morphologies in their full level of detail. However, obtaining the morphologies of all neurons present in one network currently remains an insurmountable hurdle. On the other hand, a number of computational methods have

1. Visual interneurons tuned to the motion of small objects are found in many animal species and are assumed to be the neuronal basis of figure-ground discrimination by relative motion. A well-examined example is the FD1-cell in the third visual neuropil of blowflies. This cell type responds best to motion of small objects. Motion of extended patterns elicits only small responses. As a neuronal mechanism that leads to such a response characteristic, it was proposed that the FD1-cell is inhibited by the two presumably GABAergic and, thus, inhibitory CH-cells, the VCH- and the DCH-cell. The CH-cells respond best to exactly that type of motion by which the activity of the FD1-cell is reduced. The hypothesis that the CH-cells inhibit the FD1-cell and, thus, mediate its selectivity to small moving objects was tested by ablating the CH-cells either pharmacologically or by photoinactivation. 2. After application of the gamma-aminobutyric acid (GABA) antagonist picrotoxinin, the FD1-cell responds more strongly to large-field than to small-field motion, i.e., it has lost its small-field selectivity. This suggests that the tuning of the FD1-cell to small moving objects relies on a GABAergic mechanism and, thus, most likely on the CH-cells. 3. The role of each CH-cell for small-field tuning was determined by inactivating them individually. They were injected with a fluorescent dye and then ablated by laser illumination. Only photoinactivation of the VCH-cell eliminated the specific selectivity of the FD1-cell for small-field motion. Ablation of the DCH-cell did not significantly change the response characteristic of the FD1-cell. This reveals the important role of the VCH-cells in mediating the characteristic sensitivity of the FD1-cell to motion of small objects. 4. The FD1-cell is most sensitive to motion of small objects in the ventral part of the ipsilateral visual field, whereas motion in the dorsal part influences the cell only weakly. This specific feature fits well to the sensitivity of the VCH-cell to ipsilateral motion that is most pronounced in the ventral part of the visual field. The spatial sensitivity distribution of the FD1-cell matches also the characteristics of figure-ground discrimination and fixation behavior.

The optic lobe of Drosophila houses about 60,000 neurons that are organized in parallel, retinotopically arranged columns. Based on the Golgi-staining method, Fischbach and Dittrich ([1989] Cell Tissue Res 258:441-475) determined that each column contains about 90 identified cells. Each of these cells is supposed to release one or two different neurotransmitters. However, for most cells the released neurotransmitter is not known. Here we characterize the vast majority of the neurons in the Drosophila optic lobe that release acetylcholine (Ach), the major excitatory neurotransmitter of the insect central nervous system. We employed a promoter specific for cholinergic neurons and restricted its activity to single or a few cells using the MARCM technique. This approach allowed us to establish an anatomical map of neurons with a cholinergic phenotype based on their branching pattern. We identified 43 different types of neurons with a cholinergic phenotype. Thirty-one of them match previously described members of nine different subgroups: Transmedullary (Tm), Transmedullary Y (TmY), Medulla intrinsic (Mi, Mt, and Pm), Bushy T (T), Translobula Plate (Tlp), and Lobula intrinsic (Lcn and Lt) neurons (Fischbach and Dittrich [1989]). Intriguingly, 12 newly identified cell types suggest that previous Golgi studies were not saturating and that the actual number of different neurons per column is higher than previously thought. This study and similar ones on other neurotransmitter systems will contribute towards a columnar wiring diagram and foster the functional dissection of the visual circuitry in Drosophila.

We study the integration of multisensory and central input at the level of an identified fly motoneuron, the ventral cervical nerve motoneuron (VCNM) cell, which controls head movements of the animal. We show that this neuron receives input from a central neuron signaling flight activity, from two identified wide-field motion-sensitive neurons, from the wind-sensitive Johnston organ on the antennae, and from the campaniform sensillae of the halteres. We find that visual motion alone leads to only subthreshold responses. Only when it is combined with flight activity or wind stimuli does the VCNM respond to visual motion by modulating its spike activity in a directionally selective way. This nonlinear enhancement of visual responsiveness in the VCNM by central activity is reflected at the behavioral level, when compensatory head movements are measured in response to visual motion. While head movements of flies have only a small amplitude when flies are at rest, the response amplitude is increased by a factor of 30–40 during flight.

Motion vision is essential for navigating through the environment. Due to its genetic amenability, the fruit fly Drosophila has been serving for a lengthy period as a model organism for studying optomotor behavior as elicited by large-field horizontal motion. However, the neurons underlying the control of this behavior have not been studied in Drosophila so far. Here we report the first whole cell recordings from three cells of the horizontal system (HSN, HSE, and HSS) in the lobula plate of Drosophila. All three HS cells are tuned to large-field horizontal motion in a direction-selective way; they become excited by front-to-back motion and inhibited by back-to-front motion in the ipsilateral field of view. The response properties of HS cells such as contrast and velocity dependence are in accordance with the correlation-type model of motion detection. Neurobiotin injection suggests extensive coupling among ipsilateral HS cells and additional coupling to tangential cells that have their dendrites in the contralateral hemisphere of the brain. This connectivity scheme accounts for the complex layout of their receptive fields and explains their sensitivity both to ipsilateral and to contralateral motion. Thus the main response properties of Drosophila HS cells are strikingly similar to the responses of their counterparts in the blowfly Calliphora, although we found substantial differences with respect to their dendritic structure and connectivity. This long-awaited functional characterization of HS cells in Drosophila provides the basis for the future dissection of optomotor behavior and the underlying neural circuitry by combining genetics, physiology, and behavior.