Neural mechanisms to exploit positional geometry for collision avoidance

Adaptation to visual sparsity enhances responses to isolated stimuli

Sensory systems adapt their response properties to the statistics of their inputs. For instance, visual systems adapt to low-order statistics like mean and variance to encode stimuli efficiently or to facilitate specific downstream computations. However, it remains unclear how other statistical features affect sensory adaptation. Here, we explore how Drosophila's visual motion circuits adapt to stimulus sparsity, a measure of the signal's intermittency not captured by low-order statistics alone. Early visual neurons in both ON and OFF pathways alter their responses dramatically with stimulus sparsity, responding positively to both light and dark sparse stimuli but linearly to dense stimuli. These changes extend to downstream ON and OFF direction-selective neurons, which are activated by sparse stimuli of both polarities but respond with opposite signs to light and dark regions of dense stimuli. Thus, sparse stimuli activate both ON and OFF pathways, recruiting a larger fraction of the circuit and potentially enhancing the salience of isolated stimuli. Overall, our results reveal visual response properties that increase the fraction of the circuit responding to sparse, isolated stimuli.

Neuronal identity control at the resolution of a single transcription factor isoform

The brain exhibits remarkable neuronal diversity which is critical for its functional integrity. From the sheer number of cell types emerging from extensive transcriptional, morphological, and connectome datasets, the question arises of how the brain is capable of generating so many unique identities. 'Terminal selectors' are transcription factors hypothesized to determine the final identity characteristics in post-mitotic cells. Which transcription factors function as terminal selectors and the level of control they exert over different terminal characteristics are not well defined. Here, we establish a novel role for the transcription factor broad as a terminal selector in Drosophila melanogaster. We capitalize on existing large sequencing and connectomics datasets and employ a comprehensive characterization of terminal characteristics including Perturb-seq and whole-cell electrophysiology. We find a single isoform broad-z4 serves as the switch between the identity of two visual projection neurons LPLC1 and LPLC2. Broad-z4 is natively expressed in LPLC1, and is capable of transforming the transcriptome, morphology, and functional connectivity of LPLC2 cells into LPLC1 cells when perturbed. Our comprehensive work establishes a single isoform as the smallest unit underlying an identity switch, which may serve as a conserved strategy replicated across developmental programs.

NeuronBridge: an intuitive web application for neuronal morphology search across large data sets

BACKGROUND

Neuroscience research in Drosophila is benefiting from large-scale connectomics efforts using electron microscopy (EM) to reveal all the neurons in a brain and their connections. To exploit this knowledge base, researchers relate a connectome's structure to neuronal function, often by studying individual neuron cell types. Vast libraries of fly driver lines expressing fluorescent reporter genes in sets of neurons have been created and imaged using confocal light microscopy (LM), enabling the targeting of neurons for experimentation. However, creating a fly line for driving gene expression within a single neuron found in an EM connectome remains a challenge, as it typically requires identifying a pair of driver lines where only the neuron of interest is expressed in both. This task and other emerging scientific workflows require finding similar neurons across large data sets imaged using different modalities.

RESULTS

Here, we present NeuronBridge, a web application for easily and rapidly finding putative morphological matches between large data sets of neurons imaged using different modalities. We describe the functionality and construction of the NeuronBridge service, including its user-friendly graphical user interface (GUI), extensible data model, serverless cloud architecture, and massively parallel image search engine.

CONCLUSIONS

NeuronBridge fills a critical gap in the Drosophila research workflow and is used by hundreds of neuroscience researchers around the world. We offer our software code, open APIs, and processed data sets for integration and reuse, and provide the application as a service at http://neuronbridge.janelia.org .

Long-term neuropeptide modulation of female sexual drive via the TRP channel in Drosophila melanogaster

Connectomics research has made it more feasible to explore how neural circuits can generate multiple outputs. Female sexual drive provides a good model for understanding reversible, long-term functional changes in motivational circuits. After emerging, female flies avoid male courtship, but they become sexually receptive over 2 d. Mating causes females to reject further mating for several days. Here, we report that pC1 neurons, which process male courtship and regulate copulation behavior, exhibit increased CREB (cAMP response element binding protein) activity during sexual maturation and decreased CREB activity after mating. This increased CREB activity requires the neuropeptide Dh44 (Diuretic hormone 44) and its receptors. A subset of the pC1 neurons secretes Dh44, which stimulates CREB activity and increases expression of the TRP channel Pyrexia (Pyx) in more pC1 neurons. This, in turn, increases pC1 excitability and sexual drive. Mating suppresses pyx expression and pC1 excitability. Dh44 is orthologous to the conserved corticotrophin-releasing hormone family, suggesting similar roles in other species.

Neural mechanisms to incorporate visual counterevidence in self-movement estimation

In selecting appropriate behaviors, animals should weigh sensory evidence both for and against specific beliefs about the world. For instance, animals measure optic flow to estimate and control their own rotation. However, existing models of flow detection can be spuriously triggered by visual motion created by objects moving in the world. Here, we show that stationary patterns on the retina, which constitute evidence against observer rotation, suppress inappropriate stabilizing rotational behavior in the fruit fly Drosophila. In silico experiments show that artificial neural networks (ANNs) that are optimized to distinguish observer movement from external object motion similarly detect stationarity and incorporate negative evidence. Employing neural measurements and genetic manipulations, we identified components of the circuitry for stationary pattern detection, which runs parallel to the fly's local motion and optic-flow detectors. Our results show how the fly brain incorporates negative evidence to improve heading stability, exemplifying how a compact brain exploits geometrical constraints of the visual world.

Long-timescale anti-directional rotation in Drosophila optomotor behavior

Locomotor movements cause visual images to be displaced across the eye, a retinal slip that is counteracted by stabilizing reflexes in many animals. In insects, optomotor turning causes the animal to turn in the direction of rotating visual stimuli, thereby reducing retinal slip and stabilizing trajectories through the world. This behavior has formed the basis for extensive dissections of motion vision. Here, we report that under certain stimulus conditions, two Drosophila species, including the widely studied Drosophila melanogaster, can suppress and even reverse the optomotor turning response over several seconds. Such 'anti-directional turning' is most strongly evoked by long-lasting, high-contrast, slow-moving visual stimuli that are distinct from those that promote syn-directional optomotor turning. Anti-directional turning, like the syn-directional optomotor response, requires the local motion detecting neurons T4 and T5. A subset of lobula plate tangential cells, CH cells, show involvement in these responses. Imaging from a variety of direction-selective cells in the lobula plate shows no evidence of dynamics that match the behavior, suggesting that the observed inversion in turning direction emerges downstream of the lobula plate. Further, anti-directional turning declines with age and exposure to light. These results show that Drosophila optomotor turning behaviors contain rich, stimulus-dependent dynamics that are inconsistent with simple reflexive stabilization responses.

Neural mechanisms to incorporate visual counterevidence in self motion estimation

In selecting appropriate behaviors, animals should weigh sensory evidence both for and against specific beliefs about the world. For instance, animals measure 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 silico experiments show that artificial neural networks optimized to distinguish self and world motion similarly detect stationarity and incorporate negative evidence. Employing neural measurements and genetic manipulations, we identified components of the circuitry for stationary pattern detection, which runs 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.

Visual processing in the fly, from photoreceptors to behavior

Originally a genetic model organism, the experimental use of Drosophila melanogaster has grown to include quantitative behavioral analyses, sophisticated perturbations of neuronal function, and detailed sensory physiology. A highlight of these developments can be seen in the context of vision, where pioneering studies have uncovered fundamental and generalizable principles of sensory processing. Here we begin with an overview of vision-guided behaviors and common methods for probing visual circuits. We then outline the anatomy and physiology of brain regions involved in visual processing, beginning at the sensory periphery and ending with descending motor control. Areas of focus include contrast and motion detection in the optic lobe, circuits for visual feature selectivity, computations in support of spatial navigation, and contextual associative learning. Finally, we look to the future of fly visual neuroscience and discuss promising topics for further study.

Bioinspired figure-ground discrimination via visual motion smoothing

Flies detect and track moving targets among visual clutter, and this process mainly relies on visual motion. Visual motion is analyzed or computed with the pathway from the retina to T4/T5 cells. The computation of local directional motion was formulated as an elementary movement detector (EMD) model more than half a century ago. Solving target detection or figure-ground discrimination problems can be equivalent to extracting boundaries between a target and the background based on the motion discontinuities in the output of a retinotopic array of EMDs. Individual EMDs cannot measure true velocities, however, due to their sensitivity to pattern properties such as luminance contrast and spatial frequency content. It remains unclear how local directional motion signals are further integrated to enable figure-ground discrimination. Here, we present a computational model inspired by fly motion vision. Simulations suggest that the heavily fluctuating output of an EMD array is naturally surmounted by a lobula network, which is hypothesized to be downstream of the local motion detectors and have parallel pathways with distinct directional selectivity. The lobula network carries out a spatiotemporal smoothing operation for visual motion, especially across time, enabling the segmentation of moving figures from the background. The model qualitatively reproduces experimental observations in the visually evoked response characteristics of one type of lobula columnar (LC) cell. The model is further shown to be robust to natural scene variability. Our results suggest that the lobula is involved in local motion-based target detection.

Long timescale anti-directional rotation in Drosophila optomotor behavior

Locomotor movements cause visual images to be displaced across the eye, a retinal slip that is counteracted by stabilizing reflexes in many animals. In insects, optomotor turning causes the animal to turn in the direction of rotating visual stimuli, thereby reducing retinal slip and stabilizing trajectories through the world. This behavior has formed the basis for extensive dissections of motion vision. Here, we report that under certain stimulus conditions, two Drosophila species, including the widely studied D. melanogaster, can suppress and even reverse the optomotor turning response over several seconds. Such "anti-directional turning" is most strongly evoked by long-lasting, high-contrast, slow-moving visual stimuli that are distinct from those that promote syn-directional optomotor turning. Anti-directional turning, like the syn-directional optomotor response, requires the local motion detecting neurons T4 and T5. A subset of lobula plate tangential cells, CH cells, show involvement in these responses. Imaging from a variety of direction-selective cells in the lobula plate shows no evidence of dynamics that match the behavior, suggesting that the observed inversion in turning direction emerges downstream of the lobula plate. Further, anti-directional turning declines with age and exposure to light. These results show that Drosophila optomotor turning behaviors contain rich, stimulus-dependent dynamics that are inconsistent with simple reflexive stabilization responses.

A searchable image resource of Drosophila GAL4 driver expression patterns with single neuron resolution

Precise, repeatable genetic access to specific neurons via GAL4/UAS and related methods is a key advantage of Drosophila neuroscience. Neuronal targeting is typically documented using light microscopy of full GAL4 expression patterns, which generally lack the single-cell resolution required for reliable cell type identification. Here, we use stochastic GAL4 labeling with the MultiColor FlpOut approach to generate cellular resolution confocal images at large scale. We are releasing aligned images of 74,000 such adult central nervous systems. An anticipated use of this resource is to bridge the gap between neurons identified by electron or light microscopy. Identifying individual neurons that make up each GAL4 expression pattern improves the prediction of split-GAL4 combinations targeting particular neurons. To this end, we have made the images searchable on the NeuronBridge website. We demonstrate the potential of NeuronBridge to rapidly and effectively identify neuron matches based on morphology across imaging modalities and datasets.

A visuomotor circuit for evasive flight turns in Drosophila

Visual systems extract multiple features from a scene using parallel neural circuits. Ultimately, the separate neural signals must come together to coherently influence action. Here, we characterize a circuit in Drosophila that integrates multiple visual features related to imminent threats to drive evasive locomotor turns. We identified, using genetic perturbation methods, a pair of visual projection neurons (LPLC2) and descending neurons (DNp06) that underlie evasive flight turns in response to laterally moving or approaching visual objects. Using two-photon calcium imaging or whole-cell patch clamping, we show that these cells indeed respond to both translating and approaching visual patterns. Furthermore, by measuring visual responses of LPLC2 neurons after genetically silencing presynaptic motion-sensing neurons, we show that their visual properties emerge by integrating multiple visual features across two early visual structures: the lobula and the lobula plate. This study highlights a clear example of how distinct visual signals converge on a single class of visual neurons and then activate premotor neurons to drive action, revealing a concise visuomotor pathway for evasive flight maneuvers in Drosophila.

Synaptic gradients transform object location to action

To survive, animals must convert sensory information into appropriate behaviours^1,2. Vision is a common sense for locating ethologically relevant stimuli and guiding motor responses^3-5. How circuitry converts object location in retinal coordinates to movement direction in body coordinates remains largely unknown. Here we show through behaviour, physiology, anatomy and connectomics in Drosophila that visuomotor transformation occurs by conversion of topographic maps formed by the dendrites of feature-detecting visual projection neurons (VPNs)^6,7 into synaptic weight gradients of VPN outputs onto central brain neurons. We demonstrate how this gradient motif transforms the anteroposterior location of a visual looming stimulus into the fly's directional escape. Specifically, we discover that two neurons postsynaptic to a looming-responsive VPN type promote opposite takeoff directions. Opposite synaptic weight gradients onto these neurons from looming VPNs in different visual field regions convert localized looming threats into correctly oriented escapes. For a second looming-responsive VPN type, we demonstrate graded responses along the dorsoventral axis. We show that this synaptic gradient motif generalizes across all 20 primary VPN cell types and most often arises without VPN axon topography. Synaptic gradients may thus be a general mechanism for conveying spatial features of sensory information into directed motor outputs.

Visual and motor signatures of locomotion dynamically shape a population code for feature detection in Drosophila

Natural vision is dynamic: as an animal moves, its visual input changes dramatically. How can the visual system reliably extract local features from an input dominated by self-generated signals? In Drosophila, diverse local visual features are represented by a group of projection neurons with distinct tuning properties. Here, we describe a connectome-based volumetric imaging strategy to measure visually evoked neural activity across this population. We show that local visual features are jointly represented across the population, and a shared gain factor improves trial-to-trial coding fidelity. A subset of these neurons, tuned to small objects, is modulated by two independent signals associated with self-movement, a motor-related signal, and a visual motion signal associated with rotation of the animal. These two inputs adjust the sensitivity of these feature detectors across the locomotor cycle, selectively reducing their gain during saccades and restoring it during intersaccadic intervals. This work reveals a strategy for reliable feature detection during locomotion.

Descending neuron population dynamics during odor-evoked and spontaneous limb-dependent behaviors

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 grooming. However, what additional role the larger population of DNs plays during natural behaviors remains largely unknown. For example, they may modulate core behavioral commands or comprise parallel pathways that are engaged depending on sensory context. We evaluated these possibilities by recording populations of nearly 100 DNs in individual tethered flies while they generated limb-dependent behaviors, including walking and grooming. We found that the largest fraction of recorded DNs encode walking while fewer are active during head grooming and resting. A large fraction of walk-encoding DNs encode turning and far fewer weakly encode speed. Although odor context does not determine which behavior-encoding DNs are recruited, a few DNs encode odors rather than behaviors. Lastly, we illustrate how one can identify individual neurons from DN population recordings by using their spatial, functional, and morphological properties. These results set the stage for a comprehensive, population-level understanding of how the brain's descending signals regulate complex motor actions.

Motion vision: Drosophila neural pathways that go with the visual flow

Processing visual motion cues to interpret self-motion, the movement of others, and the environment's structure is vital to all animals, whether prey or predator. A new study in Drosophila identifies multiple pathways likely contributing to visual motion-dependent computations and behaviors.

Feature maps: How the insect visual system organizes information

A new study explores how a population of neurons in the insect brain responds to different features of visual scenes and discovers an unusual topographic map that organizes the information they encode.

Feature encoding: How back-to-front motion guides the polite fly

Motion of a visual image from back-to-front across a visual field can provide an early-stage cue for impending collisions. A new study reveals visual feature encoding neurons that drive behavioral responses to back-to-front motion in the fly Drosophila melanogaster.