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Identifying a pair of antennal deflection-encoding descending neurons (DNs) from population recordings. (a) A pair of head groom-encoding DNs (purple circles and white arrowheads) can be identified from DN population recordings based on their shapes, locations, and activity patterns. (b) Example ∆F/F traces (black) of DNs highlighted in panel (a). Sample time points with bilaterally asymmetric neural activity are indicated (gray arrowheads). Overlaid is a prediction of neural activity derived by convolving left and right antennal collisions measured through kinematic replay in the NeuroMechFly physics simulation (purple). (c) Kinematic replay of recorded joint angles in NeuroMechFly allow one to infer antennal collisions from real, recorded head grooming. (d) Left and right antennal collisions during simulated replay of head grooming shown in panel (b). Sample time points with bilaterally asymmetric collisions are indicated (gray arrowheads). (e) Two-photon image of the cervical connective in a R65D11>OpGCaMP6f, tdTomato animal. Overlaid are regions of interest (ROIs) identified using AxoID. The pair of axonal ROIs are in a similar ventral location and have a similarly large relative size like those seen in DN population recordings. Scale bar is 5 μm. (f) Sample neural activity traces from ROIs 0 and 1. Bilaterally asymmetric neural activity events (gray arrowheads), behaviors (color bar), and CO 2 puffs directed at the antennae (gray bars) are indicated. (g, h) CO 2 puff-triggered average of neural activity for ROIs (g) 0 and (h) 1. Only events in which animals did not respond with head grooming or front leg rubbing were used. Stimuli were presented at t = 0 . Shown are individual responses (gray lines) and their means (black lines). (i) Confocal volume z-projection of MultiColor FlpOut (MCFO) expression in an R65D11-GAL4 animal. Cyan neuron morphology closely resembles DNx01 (Namiki et al., 2018). Scale bar is 50 μm. (j, k) Higher-magnification MCFO image, isolating the putative DNx01 from panel (i), of the (j) brain and (k) ventral nerve cord (VNC). Scale bars are 20 μm. (l) The locations of axons in the cervical connective (purple) from neurons identified as DNx01. Scale bar is 10 μm. (m) Manual reconstruction of a DNx01 from panel (l). Scale bar is 50 μm.
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Deciphering how the brain regulates motor circuits to control complex behaviors is an important, long-standing challenge in neuroscience. In the fly, Drosophila melanogaster , this is coordinated by a population of ~ 1100 descending neurons (DNs). Activating only a few DNs is known to be sufficient to drive complex behaviors like walking and groomi...
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... illustrate how this might be accomplished, we aimed to identify specific DNs within our population imaging dataset. Specifically, while analyzing head grooming DNs we noticed a large pair of ventral neurons (Figure 5a) that sometimes exhibited asymmetric activity (Figure 5b, gray arrowheads) when flies appeared to touch one rather than both antennae (Video 5). To quantify this observation, we replayed limb 3D kinematics in NeuroMechFly, a biomechanical simulation of Drosophila ( Lobato-Rios et al., 2022; Figure 5c). ...
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... illustrate how this might be accomplished, we aimed to identify specific DNs within our population imaging dataset. Specifically, while analyzing head grooming DNs we noticed a large pair of ventral neurons (Figure 5a) that sometimes exhibited asymmetric activity (Figure 5b, gray arrowheads) when flies appeared to touch one rather than both antennae (Video 5). To quantify this observation, we replayed limb 3D kinematics in NeuroMechFly, a biomechanical simulation of Drosophila ( Lobato-Rios et al., 2022; Figure 5c). ...
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... while analyzing head grooming DNs we noticed a large pair of ventral neurons (Figure 5a) that sometimes exhibited asymmetric activity (Figure 5b, gray arrowheads) when flies appeared to touch one rather than both antennae (Video 5). To quantify this observation, we replayed limb 3D kinematics in NeuroMechFly, a biomechanical simulation of Drosophila ( Lobato-Rios et al., 2022; Figure 5c). By detecting leg-antennal collisions as a proxy for antenna deflection, we found that occasional asymmetries (Figure 5d) did coincide with asymmetric activity in corresponding neural data (Figure 5b, purple traces). ...
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... quantify this observation, we replayed limb 3D kinematics in NeuroMechFly, a biomechanical simulation of Drosophila ( Lobato-Rios et al., 2022; Figure 5c). By detecting leg-antennal collisions as a proxy for antenna deflection, we found that occasional asymmetries (Figure 5d) did coincide with asymmetric activity in corresponding neural data (Figure 5b, purple traces). These results suggested that this pair of DNs encodes mechanosensory signals associated with antennal deflections. ...
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... quantify this observation, we replayed limb 3D kinematics in NeuroMechFly, a biomechanical simulation of Drosophila ( Lobato-Rios et al., 2022; Figure 5c). By detecting leg-antennal collisions as a proxy for antenna deflection, we found that occasional asymmetries (Figure 5d) did coincide with asymmetric activity in corresponding neural data (Figure 5b, purple traces). These results suggested that this pair of DNs encodes mechanosensory signals associated with antennal deflections. ...
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... this dataset, we observed similar asymmetric activity during antennal grooming in R65D11-GAL4. Coronal (x-z) two-photon imaging in R65D11 animals expressing OpGCaMP6f and tdTomato shows axons that are similarly large and ventromedially located within the cervical connective (Figure 5e). These also produce asymmetric activity during antennal grooming (Figure 5f). ...
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... (x-z) two-photon imaging in R65D11 animals expressing OpGCaMP6f and tdTomato shows axons that are similarly large and ventromedially located within the cervical connective (Figure 5e). These also produce asymmetric activity during antennal grooming (Figure 5f). This suggests that these neurons may report something unique to head grooming (e.g., coincident front limb movements) or simply antennal deflection. ...
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... distinguish between these possibilities we analyzed neural responses to CO 2 puff stimulation of the antennae while discarding data with resulting head grooming or front leg rubbing to ensure that the antennae were not touched by the legs. We measured an increase in the activity of both DNs upon puff stimulation (Figure 5g and h) suggesting that, like the neurons recorded in DN populations, R65D11 neurons also encode sensory signals-antennal deflection-rather than behavior. ...
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... drives expression in several neurons. However, we found similarly large axonal projections from only one set of neurons that descend from the brain to the VNC (Figure 5i, cyan). Close examination of these neurites in the brain (Figure 5j) and VNC (Figure 5k) revealed a striking resemblance to the reported structure of DNx01 neurons ) with cell bodies outside of the brain-putatively in the antennae and enabling antennal mechanosensing. ...
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... we found similarly large axonal projections from only one set of neurons that descend from the brain to the VNC (Figure 5i, cyan). Close examination of these neurites in the brain (Figure 5j) and VNC (Figure 5k) revealed a striking resemblance to the reported structure of DNx01 neurons ) with cell bodies outside of the brain-putatively in the antennae and enabling antennal mechanosensing. ...
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... we found similarly large axonal projections from only one set of neurons that descend from the brain to the VNC (Figure 5i, cyan). Close examination of these neurites in the brain (Figure 5j) and VNC (Figure 5k) revealed a striking resemblance to the reported structure of DNx01 neurons ) with cell bodies outside of the brain-putatively in the antennae and enabling antennal mechanosensing. ...
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... results enable the analysis of synaptic connectivity in identified DNs. To illustrate this, based on their unique location and size, we identified DNx01s in a VNC electron microscopy dataset (Phelps et al., 2021; Figure 5l) via manual reconstruction and observed a striking morphological similarity to R65D11 DNs (Figure 5m). From this reconstruction, once the full VNC connectome becomes available, one may identify synaptic partners of DNx01s to further understand how they contribute to controlling antennal grooming and other behaviors. ...
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... results enable the analysis of synaptic connectivity in identified DNs. To illustrate this, based on their unique location and size, we identified DNx01s in a VNC electron microscopy dataset (Phelps et al., 2021; Figure 5l) via manual reconstruction and observed a striking morphological similarity to R65D11 DNs (Figure 5m). From this reconstruction, once the full VNC connectome becomes available, one may identify synaptic partners of DNx01s to further understand how they contribute to controlling antennal grooming and other behaviors. ...
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... additional details and a description of the stimulation system, see Chen, 2022. Note that images of the connective in Figure 5a and e appear to have different heights due to a difference in z-step size during image acquisition. ...
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... an electron microscopy dataset of the VNC and neck connective (Phelps et al., 2021), we identified the pair of DNx01s based on their large-caliber axons in the cervical connective positioned ventral to the giant fiber neurons axons (Figure 5l). We then manually reconstructed all branches of one DNx01 neuron using CATMAID (Saalfeld et al., 2009;Schneider-Mizell et al., 2016) as described in Phelps et al., 2021. ...
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... in this study we report cross-validation means. In Figure 5b, we perform a regression to predict one neuron's activity using the intercept as well as a crf-convolved antennal collision regressor derived from data in Figure 5d. ...
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... in this study we report cross-validation means. In Figure 5b, we perform a regression to predict one neuron's activity using the intercept as well as a crf-convolved antennal collision regressor derived from data in Figure 5d. ...
Citations
... For the DN population, a range of behaviours are linked to individual or small groups of DNs: aDN, DNg11 and DNg12 for anterior grooming sequences (Guo et al., 2022;Hampel et al., 2015), DNa02 for turning (Rayshubskiy et al., 2020), DNp50/MDN for backwards walking (Bidaye et al., 2014), DNp01/GF for escape (Lima & Miesenböck, 2005), DNp07 and DNp10 for landing (Ache et al., 2019), DNp15/DNHS1, DNp20/DNOVS1, DNp22/DNOVS2 for flight and neck control (Suver et al., 2016) and others. However, our understanding remains incompleteonly a few studies have examined larger groups of DNs by morphology or behaviour (Aymanns et al., 2022;Cande et al., 2018), and even less is known about ANs . ...
... DNx02 are sensory descending neurons, two on each side, that enter the brain via the occipital nerve, a nerve that was only recently identified (Eichler et al., 2024). Analogously to DNx01, that responds to mechanosensory stimuli on the antenna and is serial to the bilateral campaniform sensillum (bCS) neurons, we predict DNx02 would respond to mechanosensory stimuli from the eye potentially with grooming behaviours (Aymanns et al., 2022;Namiki et al., 2018). DNx02 in the brain stays within the GNG, while in the VNC they project into the neck and haltere neuropils (Fig.4d). ...
In most complex nervous systems there is a clear anatomical separation between the nerve cord, which contains most of the final motor outputs necessary for behaviour, and the brain. In insects, the neck connective is both a physical and information bottleneck connecting the brain and the ventral nerve cord (VNC, spinal cord analogue) and comprises diverse populations of descending (DN), ascending (AN) and sensory ascending neurons, which are crucial for sensorimotor signalling and control.
Integrating three separate EM datasets, we now provide a complete connectomic description of the ascending and descending neurons of the female nervous system of Drosophila and compare them with neurons of the male nerve cord. Proofread neuronal reconstructions have been matched across hemispheres, datasets and sexes. Crucially, we have also matched 51% of DN cell types to light level data defining specific driver lines as well as classifying all ascending populations.
We use these results to reveal the general architecture, tracts, neuropil innervation and connectivity of neck connective neurons. We observe connected chains of descending and ascending neurons spanning the neck, which may subserve motor sequences. We provide a complete description of sexually dimorphic DN and AN populations, with detailed analysis of circuits implicated in sex-related behaviours, including female ovipositor extrusion (DNp13), male courtship (DNa12/aSP22) and song production (AN hemilineage 08B). Our work represents the first EM-level circuit analyses spanning the entire central nervous system of an adult animal.
... Most directly, for many DNs, sparse optogenetic activation does not clearly and reliably drive a coordinated behaviour 18 . In addition, previously, we observed the co-activation of many DNs during walking 29 , and others have shown that a group of 15 DNs can modulate wing beat amplitude 30 and that the activation of individual DNs has a lower probability of eliciting take-off than the co-activation of multiple DNs 31 . Furthermore, beyond controlling kinematics, DNs can also be neuromodulatory 32,33 . ...
... 1c). To further restrict our neural recordings to DNs, we performed two-photon microscopy of DN axons passing through the thoracic cervical connective 29 (Fig. 1c). We further increased the specificity of comDN optogenetic activation by restricting stimulation of DN axons to the neck connective ( Fig. 1d, red, and Extended Data Fig. 1e,f). ...
... We performed two-photon microscopy with a ThorLabs Bergamo II two-photon microscope augmented with a behavioural tracking system as described in ref. 29. In brief, we recorded a coronal section of the thoracic cervical connective using galvo-resonance scanning at around 16-Hz frame rate. ...
To convert intentions into actions, movement instructions must pass from the brain to downstream motor circuits through descending neurons (DNs). These include small sets of command-like neurons that are sufficient to drive behaviours¹—the circuit mechanisms for which remain unclear. Here we show that command-like DNs in Drosophila directly recruit networks of additional DNs to orchestrate behaviours that require the active control of numerous body parts. Specifically, we found that command-like DNs previously thought to drive behaviours alone2–4 in fact co-activate larger populations of DNs. Connectome analyses and experimental manipulations revealed that this functional recruitment can be explained by direct excitatory connections between command-like DNs and networks of interconnected DNs in the brain. Descending population recruitment is necessary for behavioural control: DNs with many downstream descending partners require network co-activation to drive complete behaviours and drive only simple stereotyped movements in their absence. These DN networks reside within behaviour-specific clusters that inhibit one another. These results support a mechanism for command-like descending control in which behaviours are generated through the recruitment of increasingly large DN networks that compose behaviours by combining multiple motor subroutines.
... Other DNs promote stopping behavior (Cande et al. 2018, Carreira-Rosasrio et al. 2018, Lee and Doe, 2021, Sapkal et al. 2023. While a small handful of DNs appear to function as "command-like neurons" and generate specific locomotor gestures, imaging studies suggest that many more DNs participate in locomotor control (Yang et al. 2023, Aymanns et al. 2022, Bresovec et al. 2023. Further, graded activity in specific DNs or brain regions often correlates with locomotor features; in particular, bilateral activity often correlates with forward velocity while differences in activity across the two brain hemispheres correlate with angular velocity to the left or right (Bidaye et al. 2020, Rayshubsky et al. 2020, Yang et al. 2023, Bresovec et al. 2023. ...
... A physiologically-inspired model of locomotor control We next sought to develop a physiologically-inspired conceptual model of locomotor control that could account for the statistics we observed experimentally-in particular the shifts in statistics that occur between baseline walking and search behavior, and the changes in odor-evoked run length with baseline ground speed state. As our starting point, we considered that (1) multiple DNs contribute to both forward and angular velocity (Rayshubskiy et al. 2020, Yang et al. 2023, Braun et al. 2023, (2) different units make different contributions to forward versus angular velocity (Rayshubskiy et al. 2020, Yang et al. 2023, Bresovec et al. 2024, Aymanns et al, 2022, (3) bilateral activity correlates with forward velocity while activity differences between hemispheres correlate with angular velocity, both in some single neurons (Bidaye et al. 2020, Yang et al. 2023, and in population imaging (Bresovec et al. 2024, Aymanns et al. 2022, and (4) distinct sets of DNs promote stopping Doe 2021, Sapkal et al. 2023). Based on these considerations, we developed a simple model of locomotor control (Fig. 3A-D). ...
... A physiologically-inspired model of locomotor control We next sought to develop a physiologically-inspired conceptual model of locomotor control that could account for the statistics we observed experimentally-in particular the shifts in statistics that occur between baseline walking and search behavior, and the changes in odor-evoked run length with baseline ground speed state. As our starting point, we considered that (1) multiple DNs contribute to both forward and angular velocity (Rayshubskiy et al. 2020, Yang et al. 2023, Braun et al. 2023, (2) different units make different contributions to forward versus angular velocity (Rayshubskiy et al. 2020, Yang et al. 2023, Bresovec et al. 2024, Aymanns et al, 2022, (3) bilateral activity correlates with forward velocity while activity differences between hemispheres correlate with angular velocity, both in some single neurons (Bidaye et al. 2020, Yang et al. 2023, and in population imaging (Bresovec et al. 2024, Aymanns et al. 2022, and (4) distinct sets of DNs promote stopping Doe 2021, Sapkal et al. 2023). Based on these considerations, we developed a simple model of locomotor control (Fig. 3A-D). ...
In order to forage for food, many animals regulate not only specific limb movements but the statistics of locomotor behavior over time, for example switching between long-range dispersal behaviors and more localized search depending on the availability of resources. How pre-motor circuits regulate such locomotor statistics is not clear. Here we took advantage of the robust changes in locomotor statistics evoked by attractive odors in walking Drosophila to investigate their neural control. We began by analyzing the statistics of ground speed and angular velocity during three well-defined motor regimes: baseline walking, upwind running during odor, and search behavior following odor offset. We find that during search behavior, flies adopt higher angular velocities and slower ground speeds, and tend to turn for longer periods of time in one direction. We further find that flies spontaneously adopt periods of different mean ground speed, and that these changes in state influence the length of odor-evoked runs. We next developed a simple physiologically-inspired computational model of locomotor control that can recapitulate these statistical features of fly locomotion. Our model suggests that contralateral inhibition plays a key role both in regulating the difference between baseline and search behavior, and in modulating the response to odor with ground speed. As the fly connectome predicts decussating inhibitory neurons in the lateral accessory lobe (LAL), a pre-motor structure, we generated genetic tools to target these neurons and test their role in behavior. Consistent with our model, we found that activation of neurons labeled in one line increased curvature. In a second line labeling distinct neurons, activation and inactivation strongly and reciprocally regulated ground speed and altered the length of the odor-evoked run. Additional targeted light activation experiments argue that these effects arise from the brain rather than from neurons in the ventral nerve cord, while sparse activation experiments argue that speed control in the second line arises from both LAL neurons and a population of neurons in the dorsal superior medial protocerebrum (SMP). Together, our work develops a biologically plausible computational architecture that captures the statistical features of fly locomotion across behavioral states and identifies potential neural substrates of these computations.
... The co-option of the VPO command neurons vpoDN, which integrate both sensory and motivational information 33 , would allow a receptive female hearing a potent male song to express the new behavior WS, and thereby communicate her interests to the male. Descending neurons like vpoDN act as a critical information bottleneck that compresses high-dimensional brain dynamics to low-dimensional commands that interface with motor circuits 59,60 . The cooption of vpoDN in WS suggests that existing descending pathways might be restrictive neural substrates favored by evolution to drive new behaviors, because they readily permit the expression of newly originated behavior in a meaningful social context. ...
Courtship interactions are remarkably diverse in form and complexity among species. How neural circuits evolve to encode new behaviors that are functionally integrated into these dynamic social interactions is unknown. Here we report a recently originated female sexual behavior in the island endemic Drosophila species D. santomea , where females signal receptivity to male courtship songs by spreading their wings, which in turn promotes prolonged songs in courting males. Copulation success depends on this female signal and correlates with males' ability to adjust his singing in such a social feedback loop. Functional comparison of sexual circuitry across species suggests that a pair of descending neurons, which integrates male song stimuli and female internal state to control a conserved female abdominal behavior, drives wing spreading in D. santomea . This co-option occurred through the refinement of a pre-existing, plastic circuit that can be optogenetically activated in an outgroup species. Combined, our results show that the ancestral potential of a socially-tuned key circuit node to engage the wing motor program facilitates the expression of a new female behavior in appropriate sensory and motivational contexts. More broadly, our work provides insights into the evolution of social behaviors, particularly female behaviors, and the underlying neural mechanisms.
... When these features enter the central brain, they are integrated across a large visual field by converging either to a small brain region or to a single cell (Mauss et al., 2015;Wu et al., 2016). These features are subsequently relayed to other central brain regions or descending neural circuits, which ultimately control action (Aymanns et al., 2022;Namiki et al., 2018). ...
Drosophila visuomotor processing has been intensively studied in recent years, leading to a qualitative understanding of individual neural circuits. However, the collective operation of these circuits during naturalistic behaviors, in which flies encounter a mixture of complex visual stimuli -- including those caused by their own actions -- remains unexplored. In this study, we developed an integrative model of Drosophila visuomotor processing, wherein multiple visuomotor circuits interconnect through an efference copy (EC) mechanism. To derive the model experimentally, we analyzed the wingbeat responses of flying Drosophila to individual, rotating visual patterns. We then combined these models to build an integrative model for superposed visual patterns, using three different strategies: the addition-only, the graded EC, and the all-or-none EC models. We compared orientation behaviors of these models with those of flying Drosophila that rotates their body freely in response to complex visual patterns. Results of these experiments support the all-or-none EC model, in which the amplitude of the flight turn is unimpeded by the background scene. Together, our "virtual fly" model provides a formal description of vision-based navigation strategies of Drosophila in complex visual environments and offers a novel framework for assessing the role of constituent visuomotor neural circuits in real-world contexts.
... Many DN types initiate motor activity when activated. In some cases DN activity triggers clear motor programs, such as walking, flight, takeoff, or singing (Bidaye et al., 2014;Cande et al., 2018;Guo et al., 2022;Koto et al., 1981;McKellar et al., 2019;Rayshubskiy et al., 2020;von Philipsborn et al., 2011) and in other cases it modulates ongoing motor activity (Aymanns et al., 2022;Namiki et al., 2022). Some DN types produce uncoordinated actions when optogenetically activated (Cande et al., 2018), suggesting that some behavioral patterning requires the simultaneous activation of multiple DNs or DN activation during the correct internal state. ...
... Population calcium imaging of supraesophageal DNs in spontaneously walking and behaving flies suggest that 60% of imaged DNs encode walking, likely encoding turning and to a lesser extent, speed. It was surmised that activity in these DNs more likely represented high-level behavior rather than low-level kinematic control (Aymanns et al., 2022). We describe 177 leg DNs and their leg MN connectivity with some detailed examples of DN circuits involved in turning and backwards walking. ...
In most animals, a relatively small number of descending neurons (DNs) connect higher brain centers in the animal's head to motor neurons (MNs) in the nerve cord of the animal's body that effect movement of the limbs. To understand how brain signals generate behavior, it is critical to understand how these descending pathways are organized onto the body MNs. In the fly, Drosophila melanogaster, MNs controlling muscles in the leg, wing, and other motor systems reside in a ventral nerve cord (VNC), analogous to the mammalian spinal cord. In companion papers, we introduced a densely-reconstructed connectome of the Drosophila Male Adult Nerve Cord (MANC, Takemura et al., 2023), including cell type and developmental lineage annotation (Marin et al., 2023), which provides complete VNC connectivity at synaptic resolution. Here, we present a first look at the organization of the VNC networks connecting DNs to MNs based on this new connectome information. We proofread and curated all DNs and MNs to ensure accuracy and reliability, then systematically matched DN axon terminals and MN dendrites with light microscopy data to link their VNC morphology with their brain inputs or muscle targets. We report both broad organizational patterns of the entire network and fine-scale analysis of selected circuits of interest. We discover that direct DN-MN connections are infrequent and identify communities of intrinsic neurons linked to control of different motor systems, including putative ventral circuits for walking, dorsal circuits for flight steering and power generation, and intermediate circuits in the lower tectulum for coordinated action of wings and legs. Our analysis generates hypotheses for future functional experiments and, together with the MANC connectome, empowers others to investigate these and other circuits of the Drosophila ventral nerve cord in richer mechanistic detail.
... At least three DNs in flies have been directly implicated in control of walking and turning: DNa01, DNa02, and DNp09 Rayshubskiy et al. 2020;Bidaye et al. 2020). However, a recent imaging study of all DNs outside the SEZ found that ~ 60% participated in walking (Aymanns et al. 2022), consistent with a population code for control of walking and turning. Flight maneuvers also appear to be encoded at a population level. ...
Using odors to find food and mates is one of the most ancient and highly conserved behaviors. Arthropods from flies to moths to crabs use broadly similar strategies to navigate toward odor sources—such as integrating flow information with odor information, comparing odor concentration across sensors, and integrating odor information over time. Because arthropods share many homologous brain structures—antennal lobes for processing olfactory information, mechanosensors for processing flow, mushroom bodies (or hemi-ellipsoid bodies) for associative learning, and central complexes for navigation, it is likely that these closely related behaviors are mediated by conserved neural circuits. However, differences in the types of odors they seek, the physics of odor dispersal, and the physics of locomotion in water, air, and on substrates mean that these circuits must have adapted to generate a wide diversity of odor-seeking behaviors. In this review, we discuss common strategies and specializations observed in olfactory navigation behavior across arthropods, and review our current knowledge about the neural circuits subserving this behavior. We propose that a comparative study of arthropod nervous systems may provide insight into how a set of basic circuit structures has diversified to generate behavior adapted to different environments.
... Unfortunately, the circuitry that translates the activity of LPTCs and other lobula neurons into the turning commands remains poorly understood. Recent functional studies have begun to uncover descending neurons that are involved in stimulus-driven turning, such as DNa02 (Aymanns et al., 2022;Rayshubskiy et al., 2020). It is of future interest to identify neurons that link LPTCs to these descending neurons, and look for where the signature of suppression by stationary patterns emerges. ...
In selecting a behavior, animals should weigh sensory evidence both for and against their beliefs about the world. For instance, animals use optic flow to estimate and control their own rotation. However, existing models of flow detection can confuse the movement of external objects with genuine self motion. Here, we show that stationary patterns on the retina, which constitute negative evidence against self rotation, are used by the fruit fly Drosophila to suppress inappropriate stabilizing rotational behavior. In parallel in silico experiments, we show that artificial neural networks trained to distinguish self and world motion incorporate similar negative evidence. We used neural measurements and genetic manipulations to identify components of the circuitry for stationary pattern detection, which is parallel to the fly’s motion- and optic flow-detectors. Our results exemplify how the compact brain of the fly incorporates negative evidence to improve heading stability, exploiting geometrical constraints of the visual world.
A deep understanding of how the brain controls behaviour requires mapping neural circuits down to the muscles that they control. Here, we apply automated tools to segment neurons and identify synapses in an electron microscopy dataset of an adult female Drosophila melanogaster ventral nerve cord (VNC)¹, which functions like the vertebrate spinal cord to sense and control the body. We find that the fly VNC contains roughly 45 million synapses and 14,600 neuronal cell bodies. To interpret the output of the connectome, we mapped the muscle targets of leg and wing motor neurons using genetic driver lines² and X-ray holographic nanotomography³. With this motor neuron atlas, we identified neural circuits that coordinate leg and wing movements during take-off. We provide the reconstruction of VNC circuits, the motor neuron atlas and tools for programmatic and interactive access as resources to support experimental and theoretical studies of how the nervous system controls behaviour.
To navigate, we must continuously estimate the direction we are headed in, and we must correct deviations from our goal¹. Direction estimation is accomplished by ring attractor networks in the head direction system2,3. However, we do not fully understand how the sense of direction is used to guide action. Drosophila connectome analyses4,5 reveal three cell populations (PFL3R, PFL3L and PFL2) that connect the head direction system to the locomotor system. Here we use imaging, electrophysiology and chemogenetic stimulation during navigation to show how these populations function. Each population receives a shifted copy of the head direction vector, such that their three reference frames are shifted approximately 120° relative to each other. Each cell type then compares its own head direction vector with a common goal vector; specifically, it evaluates the congruence of these vectors via a nonlinear transformation. The output of all three cell populations is then combined to generate locomotor commands. PFL3R cells are recruited when the fly is oriented to the left of its goal, and their activity drives rightward turning; the reverse is true for PFL3L. Meanwhile, PFL2 cells increase steering speed, and are recruited when the fly is oriented far from its goal. PFL2 cells adaptively increase the strength of steering as directional error increases, effectively managing the tradeoff between speed and accuracy. Together, our results show how a map of space in the brain can be combined with an internal goal to generate action commands, via a transformation from world-centric coordinates to body-centric coordinates.
