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Electrical synapses are present in almost all organisms that have a nervous system. However, their brain-wide expression patterns and the full range of contributions to neural function are unknown in most species. Here, we first provide a light-microscopic, immunohistochemistry-based anatomical map of all innexin gap junction proteins-the building blocks of electrical synapses-in the central nervous system of Drosophila melanogaster. Of those innexin types that are expressed in the nervous system, some localize to glial cells, whereas others are predominantly expressed in neurons, with shakB being the most widely expressed neuronal innexin. We then focus on the function of shakB in VS/HS cells-a class of visual projection neurons-thereby uncovering an unexpected role for electrical synapses. Removing shakB from these neurons leads to spontaneous, cell-autonomous voltage and calcium oscillations, demonstrating that electrical synapses are required for these cells' intrinsic stability. Furthermore, we investigate the role of shakB-type electrical synapses in early visual processing. We find that the loss of shakB from the visual circuits upstream of VS/HS cells differentially impairs ON and OFF visual motion processing pathways but is not required for the computation of direction selectivity per se. Taken together, our study demonstrates that electrical synapses are widespread across the Drosophila nervous system and that they play essential roles in neuronal function and visual information processing.

Summary: For a proper understanding of neural circuit function, it is important to know which signals neurons relay to their downstream partners. Calcium imaging with genetically encoded calcium sensors like GCaMP has become the default approach for mapping these responses. How well such measurements represent the true neurotransmitter output of any given cell, however, remains unclear. Here, we demonstrate the viability of the glutamate sensor iGluSnFR for 2-photon in vivo imaging in Drosophila melanogaster and prove its usefulness for estimating spatiotemporal receptive fields in the visual system. We compare the results obtained with iGluSnFR with the ones obtained with GCaMP6f and find that the spatial aspects of the receptive fields are preserved between indicators. In the temporal domain, however, measurements obtained with iGluSnFR reveal the underlying response properties to be much faster than those acquired with GCaMP6f. Our approach thus offers a more accurate description of glutamatergic neurons in the fruit fly. : Optical Imaging; Sensory Neuroscience; Techniques in Neuroscience Subject Areas: Optical Imaging, Sensory Neuroscience, Techniques in Neuroscience

Estimating motion is a fundamental task for the visual system of sighted animals. In Drosophila, direction-selective T4 and T5 cells respond to moving brightness increments (ON) and decrements (OFF), respectively. Current algorithmic models of the circuit are based on the interaction of two differentially filtered signals. However, electron microscopy studies have shown that T5 cells receive their major input from four classes of neurons: Tm1, Tm2, Tm4, and Tm9. Using two-photon calcium imaging, we demonstrate that T5 is the first direction-selective stage within the OFF pathway. The four cells provide an array of spatiotemporal filters to T5. Silencing their synaptic output in various combinations, we find that all input elements are involved in OFF motion detection to varying degrees. Our comprehensive survey challenges the simplified view of how neural systems compute the direction of motion and suggests that an intricate interplay of many signals results in direction selectivity.

SummaryWhen navigating in their environment, animals use visual motion cues as feedback signals that are elicited by their own motion. Such signals are provided by wide-field neurons sampling motion directions at multiple image points as the animal maneuvers. Each one of these neurons responds selectively to a specific optic flow-field representing the spatial distribution of motion vectors on the retina. Here, we describe the discovery of a group of local, inhibitory interneurons in the fruit fly Drosophila key for filtering these cues. Using anatomy, molecular characterization, activity manipulation, and physiological recordings, we demonstrate that these interneurons convey direction-selective inhibition to wide-field neurons with opposite preferred direction and provide evidence for how their connectivity enables the computation required for integrating opposing motions. Our results indicate that, rather than sharpening directional selectivity per se, these circuit elements reduce noise by eliminating non-specific responses to complex visual information.

Motion vision provides essential cues for navigation and course control as well as for mate, prey, or predator detection. Consequently, neurons responding to visual motion in a direction-selective way are found in almost all species that see. However, directional information is not explicitly encoded at the level of a single photoreceptor. Rather, it has to be computed from the spatio-temporal excitation level of at least two photoreceptors. How this computation is done and how this computation is implemented in terms of neural circuitry and membrane biophysics have remained the focus of intense research over many decades. Here, we review recent progress made in this area with an emphasis on insects and the vertebrate retina.

SummaryRecent experiments have shown that motion detection in Drosophila starts with splitting the visual input into two parallel channels encoding brightness increments (ON) or decrements (OFF). This suggests the existence of either two (ON-ON, OFF-OFF) or four (for all pairwise interactions) separate motion detectors. To decide between these possibilities, we stimulated flies using sequences of ON and OFF brightness pulses while recording from motion-sensitive tangential cells. We found direction-selective responses to sequences of same sign (ON-ON, OFF-OFF), but not of opposite sign (ON-OFF, OFF-ON), refuting the existence of four separate detectors. Based on further measurements, we propose a model that reproduces a variety of additional experimental data sets, including ones that were previously interpreted as support for four separate detectors. Our experiments and the derived model mark an important step in guiding further dissection of the fly motion detection circuit.

Summary Moving animals experience constant sensory feedback, such as panoramic image shifts on the retina, termed optic flow. Underlying neuronal signals are thought to be important for exploratory behavior by signaling unintended course deviations and by providing spatial information about the environment [ 1 , 2 ]. Particularly in insects, the encoding of self-motion-related optic flow is well understood [ 1 , 2 , 3 , 4 , 5 ]. However, a gap remains in understanding how the associated neuronal activity controls locomotor trajectories. In flies, visual projection neurons belonging to two groups encode panoramic horizontal motion: horizontal system (HS) cells respond with depolarization to front-to-back motion and hyperpolarization to the opposite direction [ 6 , 7 ], and other neurons have the mirror-symmetrical response profile [ 6 , 8 , 9 ]. With primarily monocular sensitivity, the neurons’ responses are ambiguous for different rotational and translational self-movement components. Such ambiguities can be greatly reduced by combining signals from both eyes [ 10 , 11 , 12 ] to determine turning and movement speed [ 13 , 14 , 15 , 16 ]. Here, we explore the underlying functional logic by optogenetic HS cell manipulation in tethered walking Drosophila. We show that de- and hyperpolarization evoke opposite turning behavior, indicating that both direction-selective signals are transmitted to descending pathways for course control. Further experiments reveal a negative effect of bilaterally symmetric de- and hyperpolarization on walking velocity. Our results are therefore consistent with a functional architecture in which the HS cells’ membrane potential influences walking behavior bi-directionally via two decelerating pathways.

SUMMARY A central goal in sensory neuroscience is to fully characterize a neuron’s input-output relation. However, strong nonlinearities in the responses of sensory neurons have made it difficult to develop models that generalize to arbitrary stimuli. Typically, the standard linear-nonlinear models break down when neurons exhibit stimulus-dependent modulations of their gain or selectivity. We studied these issues in optic-flow processing neurons in the fly. We found that the neurons’ receptive fields are fully described by a time-varying vector field that is space-time separable. Increasing the stimulus strength, however, strongly reduces the neurons’ gain and selectivity. To capture these changes in response behavior, we extended the linear-nonlinear model by a biophysically motivated gain and selectivity mechanism. We fit all model parameters directly to the data and show that the model now characterizes the neurons’ input-output relation well over the full range of motion stimuli.

Many animals rely on vision to detect, locate, and track moving objects. In Drosophila courtship, males primarily use visual cues to orient toward and follow females and to select the ipsilateral wing for courtship song. Here, we show that the LC10 visual projection neurons convey essential visual information during courtship. Males with LC10 neurons silenced are unable to orient toward or maintain proximity to the female and do not predominantly use the ipsilateral wing when singing. LC10 neurons preferentially respond to small moving objects using an antagonistic motion-based center-surround mechanism. Unilateral activation of LC10 neurons recapitulates the orienting and ipsilateral wing extension normally elicited by females, and the potency with which LC10 induces wing extension is enhanced in a state of courtship arousal controlled by male-specific P1 neurons. These data suggest that LC10 is a major pathway relaying visual input to the courtship circuits in the male brain.

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.

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.

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.

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.

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.

Dendritic integration plays a key role in the way information is processed by nerve cells. The large motion-sensitive interneurons of the fly appear to be most appropriate for an investigation of this process. These cells are known to receive input from numerous local motion-sensitive elements and to control visually-guided optomotor responses (e.g., Trends Neurosci., 11 (1988) 351–358; Stavenga and Hardie, Facets of Vision, Springer, 1989). The retinotopic input organization of these cells allows for in vivo stimulation of selected parts of their dendritic tree with their natural excitatory and inhibitory synaptic input signals. By displaying motion in either the cells' preferred or null direction in different regions of the receptive field we found: (i) Responses to combinations of excitatory and inhibitory motion stimuli can be described as the sum of the two response components. (ii) Responses to combination of excitatory stimuli show saturation effects. The deviation from linear superposition depends on the distance and relative position of the activated synaptic sites on the dendrite and makes the responses almost insensitive to the number of activated input channels. (iii) The saturation level depends on different stimulus parameters, e.g. the velocity of the moving pattern. The cell still encodes velocity under conditions of spatial saturation. The results can be understood on the basis of passive dendritic integration of the signals of retinotopically organized local motion-detecting elements with opposite polarity.

SummarySensory neurons have mostly been studied in fixed animals, but how do they behave when the animal is free to move? A recent study shows that, during locomotor activity, besides there being a general enhancement in responsiveness, the tuning curves of neurons can also change, altering their optimal stimuli.

Many animals use relative motion to segregate objects from their background [21, 26, 28, 31, 33]. Nerve cells tuned to this visual cue have been found in various animal groups, such as insects [3, 4, 6, 24, 25], amphibians [32], birds [12, 13] and mammals [1, 14]. Well examined examples are the figure detection (FD) cells in the visual system of the blowfly [6, 11]. The mechanism that tunes a particular FD-cell, the FD1-cell, to small-field motion is analyzed by injecting individual visual interneurons with a fluorescent dye and ablating them by illumination with a laser beam. In this way, it is shown that the FD1-cell acquires its specific spatial tuning by inhibitory input from an identified GABAergic cell, the ventral centrifugal horizontal (VCH)-cell which is most sensitive to coherent large-field motion in front of both eyes. For the first time, the detection of small objects by evaluation of their motion parallax, thus, can be attributed to synaptic interactions between identified neurons.

Electroantennogram (EAG) responses to single chemicals as well as to binary mixtures were recorded from the funiculus of wild-type Drosophila . Responses to 4-methylcyclohexanol and to 3-octanol were additive when both chemcials were given in a binary mixture, while responses to 3-nonanol and 3-octanol were not. This is interpreted as the first odour pair acting on different receptor types (4-MCH receptor and 3-OCT receptor) and the second acting on the same receptor type (3-OCT receptor), but with different affinity.