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Dragonflies are highly skilled and successful aerial predators that are even capable of selectively attending to one target within a swarm. Detection and tracking of prey is likely to be driven by small target motion detector (STMD) neurons identified from several insect groups. Prior work has shown that dragonfly STMD responses are facilitated by targets moving on a continuous path, enhancing the response gain at the present and predicted future location of targets. In this study, we combined detailed morphological data with computational modeling to test whether a combination of dendritic morphology and nonlinear properties of NMDA receptors could explain these observations. We developed a hybrid computational model of neurons within the dragonfly optic lobe, which integrates numerical and morphological components. The model was able to generate potent facilitation for targets moving on continuous trajectories, including a localized spotlight of maximal sensitivity close to the last seen target location, as also measured during in vivo recordings. The model did not, however, include a mechanism capable of producing a traveling or spreading wave of facilitation. Our data support a strong role for the high dendritic density seen in the dragonfly neuron in enhancing non-linear facilitation. An alternative model based on the morphology of an unrelated type of motion processing neuron from a dipteran fly required more than three times higher synaptic gain in order to elicit similar levels of facilitation, despite having only 20% fewer synapses. Our data support a potential role for NMDA receptors in target tracking and also demonstrate the feasibility of combining biologically plausible dendritic computations with more abstract computational models for basic processing as used in earlier studies.

Visual pathways from the compound eye of an insect relay to four neuropils, successively the lamina, medulla, lobula, and lobula plate in the underlying optic lobe. Among these neuropils, the medulla, lobula, and lobula plate are interconnected by the complex second optic chiasm, through which the anteroposterior axis undergoes an inversion between the medulla and lobula. Given their complex structure, the projection patterns through the second optic chiasm have so far lacked critical analysis. By densely reconstructing axon trajectories using a volumetric scanning electron microscopy (SEM) technique, we reveal the three-dimensional structure of the second optic chiasm of Drosophila melanogaster, which comprises interleaving bundles and sheets of axons insulated from each other by glial sheaths. These axon bundles invert their horizontal sequence in passing between the medulla and lobula. Axons connecting the medulla and lobula plate are also bundled together with them but do not decussate the sequence of their horizontal positions. They interleave with sheets of projection neuron axons between the lobula and lobula plate, which also lack decussations. We estimate that approximately 19,500 cells per hemisphere, about two thirds of the optic lobe neurons, contribute to the second chiasm, most being Tm cells, with an estimated additional 2,780 T4 and T5 cells each. The chiasm mostly comprises axons and cell body fibers, but also a few synaptic elements. Based on our anatomical findings, we propose that a chiasmal structure between the neuropils is potentially advantageous for processing complex visual information in parallel. The EM reconstruction shows not only the structure of the chiasm in the adult brain, the previously unreported main topic of our study, but also suggest that the projection patterns of the neurons comprising the chiasm may be determined by the proliferation centers from which the neurons develop. Such a complex wiring pattern could, we suggest, only have arisen in several evolutionary steps.

Visual projection neurons (VPNs) provide an anatomical connection between early visual processing and higher brain regions. Here we characterize lobula columnar (LC) cells, a class of Drosophila VPNs that project to distinct central brain structures called optic glomeruli. We anatomically describe 22 different LC types and show that, for several types, optogenetic activation in freely moving flies evokes specific behaviors. The activation phenotypes of two LC types closely resemble natural avoidance behaviors triggered by a visual loom. In vivo two-photon calcium imaging reveals that these LC types respond to looming stimuli, while another type does not, but instead responds to the motion of a small object. Activation of LC neurons on only one side of the brain can result in attractive or aversive turning behaviors depending on the cell type. Our results indicate that LC neurons convey information on the presence and location of visual features relevant for specific behaviors. DOI: http://dx.doi.org/10.7554/eLife.21022.001, eLife digest Many animals rely heavily on what they can see to interact with the world around them. But how does the brain use such visual information to guide behavior? Light-sensitive neurons in the eye cannot distinguish between the visual signals associated with, say, an approaching predator or a source of food. Yet the brain can make this distinction. Networks of neurons in the brain perform computations to extract information from a visual scene that indicates the need for a particular behavior, such as an escape response. These networks are found in regions of the brain that communicate closely with the eyes. Cells known as visual projection neurons then relay the output of these networks to more central parts of the brain. By studying visual projection neurons, it is possible to work out what the eye tells the brain, and how the brain uses this information to control behavior. The fruit fly Drosophila is a suitable model organism in which to study these phenomena. This insect shows a range of behavioral responses to visual stimuli, and can be studied using sophisticated genetic tools. Wu, Nern et al. set out to explore how a group of visual projection neurons known as lobula columnar cells help fruit flies respond appropriately to visual stimuli. Experiments revealed that individual subtypes of lobula columnar cells convey information about the presence and general location of specific visual features. Wu, Nern et al. identified a number of lobular columnar subtypes involved in triggering escape responses to specific stimuli – such as walking backwards or taking off in flight – as well as others that can trigger the flies to approach a target. A next step is to map the circuits of neurons that act upstream and downstream of lobula columnar cells. This can help to reveal how these neurons detect specific visual features and how the fly then chooses and executes an appropriate behavior in response. Such studies in flies can provide insights into general principles of how brains use sensory information to guide behavior. DOI: http://dx.doi.org/10.7554/eLife.21022.002

Synaptic circuits for identified behaviors in the Drosophila brain have typically been considered from either a developmental or functional perspective without reference to how the circuits might have been inherited from ancestral forms. For example, two candidate pathways for ON- and OFF-edge motion detection in the visual system act via circuits that use respectively either T4 or T5, two cell types of the fourth neuropil, or lobula plate, that exhibit narrow-field direction-selective responses and provide input to wide-field tangential neurons. T4 or T5 both have four subtypes that terminate one each in the four strata of the lobula plate. Representatives are reported in a wide range of Diptera, and both cell types exhibit various similarities in: 1) the morphology of their dendritic arbors; 2) their four morphological and functional subtypes; 3) their cholinergic profile in Drosophila; 4) their input from the pathways of L3 cells in the first neuropil, or lamina, and by one of a pair of lamina cells, L1 (to the T4 pathway) and L2 (to the T5 pathway); and 5) their innervation by a single, wide-field contralateral tangential neuron from the central brain. Progenitors of both also express the gene atonal early in their proliferation from the inner anlage of the developing optic lobe, being alone among many other cell type progeny to do so. Yet T4 receives input in the second neuropil, or medulla, and T5 in the third neuropil or lobula. Here we suggest that these two cell types were originally one, that their ancestral cell population duplicated and split to innervate separate medulla and lobula neuropils, and that a fiber crossing – the internal chiasma – arose between the two neuropils. The split most plausibly occurred, we suggest, with the formation of the lobula as a new neuropil that formed when it separated from its ancestral neuropil to leave the medulla, suggesting additionally that medulla input neurons to T4 and T5 may also have had a common origin.

In this comparative in vivo study of dendritic calcium accumulation, we describe the time course and spatial integration properties of two classes of visual interneurons in the lobula plate of the blowfly. Calcium accumulation was measured during visual motion stimulation, ensuring synaptic activation of the neurons within their natural spatial and temporal operating range. The compared cell classes, centrifugal horizontal (CH) and horizontal system (HS) cells, are known to receive retinotopic input of similar direction selectivity, but to differ in morphology, biophysics, presence of dendrodendritic synapses, and computational task. 1) The time course of motion-induced calcium accumulation was highly invariant with respect to stimulus parameters such as pattern contrast and size. In HS cells, the rise of [Ca2+]i can be described by a single exponential with a time constant of 5–6 s. The initial rise of [Ca2+]i in CH cells was much faster (τ ≈ 1 s). The decay time constant in both cell classes was estimated to be at least 3.5 times longer than the corresponding rise time constant. 2) The voltage-[Ca2+]i relationship was best described by an expansive nonlinearity in HS cells and an approximately linear relationship in CH cells. 3) Both cell classes displayed a size-dependent saturation nonlinearity of the calcium accumulation. Although in CH cells calcium saturation was indistinguishable from saturation of the membrane potential, saturation of the two response parameters differed in HS cells. 4) There was spatial overlap of the calcium signal in response to nonoverlapping visual stimuli. Both the area and the amplitude of the overlap profile was larger in CH cells than in HS cells. Thus calcium accumulation in CH cells is spatially blurred to a greater extent than in HS cells. 5) The described differences between the two cell classes may reflect the following computational tasks of these neurons: CH cells relay retinotopic information within the lobula plate via dendritic synapses with pronounced spatial low-pass filtering. HS cells are output neurons of the lobula plate, in which the slow, local calcium accumulation may be suitable for local modulatory functions.