Neurotransmitter classification from electron microscopy images at synaptic sites in Drosophila melanogaster.

High-resolution electron microscopy of nervous systems has enabled the reconstruction of synaptic connectomes. However, we do not know the synaptic sign for each connection (i.e., whether a connection is excitatory or inhibitory), which is implied by the released transmitter. We demonstrate that artificial neural networks can predict transmitter types for presynapses from electron micrographs: a network trained to predict six transmitters (acetylcholine, glutamate, GABA, serotonin, dopamine, octopamine) achieves an accuracy of 87% for individual synapses, 94% for neurons, and 91% for known cell types across a D. melanogaster whole brain. We visualize the ultrastructural features used for prediction, discovering subtle but significant differences between transmitter phenotypes. We also analyze transmitter distributions across the brain and find that neurons that develop together largely express only one fast-acting transmitter (acetylcholine, glutamate, or GABA). We hope that our publicly available predictions act as an accelerant for neuroscientific hypothesis generation for the fly.

Mapping model units to visual neurons reveals population code for social behaviour.

The rich variety of behaviours observed in animals arises through the interplay between sensory processing and motor control. To understand these sensorimotor transformations, it is useful to build models that predict not only neural responses to sensory input1-5 but also how each neuron causally contributes to behaviour6,7. Here we demonstrate a novel modelling approach to identify a one-to-one mapping between internal units in a deep neural network and real neurons by predicting the behavioural changes that arise from systematic perturbations of more than a dozen neuronal cell types. A key ingredient that we introduce is 'knockout training', which involves perturbing the network during training to match the perturbations of the real neurons during behavioural experiments. We apply this approach to model the sensorimotor transformations of Drosophila melanogaster males during a complex, visually guided social behaviour8-11. The visual projection neurons at the interface between the optic lobe and central brain form a set of discrete channels12, and prior work indicates that each channel encodes a specific visual feature to drive a particular behaviour13,14. Our model reaches a different conclusion: combinations of visual projection neurons, including those involved in non-social behaviours, drive male interactions with the female, forming a rich population code for behaviour. Overall, our framework consolidates behavioural effects elicited from various neural perturbations into a single, unified model, providing a map from stimulus to neuronal cell type to behaviour, and enabling future incorporation of wiring diagrams of the brain15 into the model.

Flexible circuit mechanisms for context-dependent song sequencing.

Sequenced behaviours, including locomotion, reaching and vocalization, are patterned differently in different contexts, enabling animals to adjust to their environments. How contextual information shapes neural activity to flexibly alter the patterning of actions is not fully understood. Previous work has indicated that this could be achieved via parallel motor circuits, with differing sensitivities to context1,2. Here we demonstrate that a single pathway operates in two regimes dependent on recent sensory history. We leverage the Drosophila song production system3 to investigate the role of several neuron types4-7 in song patterning near versus far from the female fly. Male flies sing 'simple' trains of only one mode far from the female fly but complex song sequences comprising alternations between modes when near her. We find that ventral nerve cord (VNC) circuits are shaped by mutual inhibition and rebound excitability8 between nodes driving the two song modes. Brief sensory input to a direct brain-to-VNC excitatory pathway drives simple song far from the female, whereas prolonged input enables complex song production via simultaneous recruitment of functional disinhibition of VNC circuitry. Thus, female proximity unlocks motor circuit dynamics in the correct context. We construct a compact circuit model to demonstrate that the identified mechanisms suffice to replicate natural song dynamics. These results highlight how canonical circuit motifs8,9 can be combined to enable circuit flexibility required for dynamic communication.

Acoustic Pattern Recognition and Courtship Songs: Insights from Insects.

Across the animal kingdom, social interactions rely on sound production and perception. From simple cricket chirps to more elaborate bird songs, animals go to great lengths to communicate information critical for reproduction and survival via acoustic signals. Insects produce a wide array of songs to attract a mate, and the intended receivers must differentiate these calls from competing sounds, analyze the quality of the sender from spectrotemporal signal properties, and then determine how to react. Insects use numerically simple nervous systems to analyze and respond to courtship songs, making them ideal model systems for uncovering the neural mechanisms underlying acoustic pattern recognition. We highlight here how the combination of behavioral studies and neural recordings in three groups of insects-crickets, grasshoppers, and fruit flies-reveals common strategies for extracting ethologically relevant information from acoustic patterns and how these findings might translate to other systems.

SLEAP: A deep learning system for multi-animal pose tracking.

The desire to understand how the brain generates and patterns behavior has driven rapid methodological innovation in tools to quantify natural animal behavior. While advances in deep learning and computer vision have enabled markerless pose estimation in individual animals, extending these to multiple animals presents unique challenges for studies of social behaviors or animals in their natural environments. Here we present Social LEAP Estimates Animal Poses (SLEAP), a machine learning system for multi-animal pose tracking. This system enables versatile workflows for data labeling, model training and inference on previously unseen data. SLEAP features an accessible graphical user interface, a standardized data model, a reproducible configuration system, over 30 model architectures, two approaches to part grouping and two approaches to identity tracking. We applied SLEAP to seven datasets across flies, bees, mice and gerbils to systematically evaluate each approach and architecture, and we compare it with other existing approaches. SLEAP achieves greater accuracy and speeds of more than 800 frames per second, with latencies of less than 3.5 ms at full 1,024 × 1,024 image resolution. This makes SLEAP usable for real-time applications, which we demonstrate by controlling the behavior of one animal on the basis of the tracking and detection of social interactions with another animal.

FlyWire: online community for whole-brain connectomics.

Due to advances in automated image acquisition and analysis, whole-brain connectomes with 100,000 or more neurons are on the horizon. Proofreading of whole-brain automated reconstructions will require many person-years of effort, due to the huge volumes of data involved. Here we present FlyWire, an online community for proofreading neural circuits in a Drosophila melanogaster brain and explain how its computational and social structures are organized to scale up to whole-brain connectomics. Browser-based three-dimensional interactive segmentation by collaborative editing of a spatially chunked supervoxel graph makes it possible to distribute proofreading to individuals located virtually anywhere in the world. Information in the edit history is programmatically accessible for a variety of uses such as estimating proofreading accuracy or building incentive systems. An open community accelerates proofreading by recruiting more participants and accelerates scientific discovery by requiring information sharing. We demonstrate how FlyWire enables circuit analysis by reconstructing and analyzing the connectome of mechanosensory neurons.

Fast animal pose estimation using deep neural networks.

The need for automated and efficient systems for tracking full animal pose has increased with the complexity of behavioral data and analyses. Here we introduce LEAP (LEAP estimates animal pose), a deep-learning-based method for predicting the positions of animal body parts. This framework consists of a graphical interface for labeling of body parts and training the network. LEAP offers fast prediction on new data, and training with as few as 100 frames results in 95% of peak performance. We validated LEAP using videos of freely behaving fruit flies and tracked 32 distinct points to describe the pose of the head, body, wings and legs, with an error rate of <3% of body length. We recapitulated reported findings on insect gait dynamics and demonstrated LEAP's applicability for unsupervised behavioral classification. Finally, we extended the method to more challenging imaging situations and videos of freely moving mice.

Dynamic sensory cues shape song structure in Drosophila.

The generation of acoustic communication signals is widespread across the animal kingdom, and males of many species, including Drosophilidae, produce patterned courtship songs to increase their chance of success with a female. For some animals, song structure can vary considerably from one rendition to the next; neural noise within pattern generating circuits is widely assumed to be the primary source of such variability, and statistical models that incorporate neural noise are successful at reproducing the full variation present in natural songs. In direct contrast, here we demonstrate that much of the pattern variability in Drosophila courtship song can be explained by taking into account the dynamic sensory experience of the male. In particular, using a quantitative behavioural assay combined with computational modelling, we find that males use fast modulations in visual and self-motion signals to pattern their songs, a relationship that we show is evolutionarily conserved. Using neural circuit manipulations, we also identify the pathways involved in song patterning choices and show that females are sensitive to song features. Our data not only demonstrate that Drosophila song production is not a fixed action pattern, but establish Drosophila as a valuable new model for studies of rapid decision-making under both social and naturalistic conditions.

Experimental and statistical reevaluation provides no evidence for Drosophila courtship song rhythms.

From 1980 to 1992, a series of influential papers reported on the discovery, genetics, and evolution of a periodic cycling of the interval between Drosophila male courtship song pulses. The molecular mechanisms underlying this periodicity were never described. To reinitiate investigation of this phenomenon, we previously performed automated segmentation of songs but failed to detect the proposed rhythm [Arthur BJ, et al. (2013) BMC Biol 11:11; Stern DL (2014) BMC Biol 12:38]. Kyriacou et al. [Kyriacou CP, et al. (2017) Proc Natl Acad Sci USA 114:1970-1975] report that we failed to detect song rhythms because (i) our flies did not sing enough and (ii) our segmenter did not identify many of the song pulses. Kyriacou et al. manually annotated a subset of our recordings and reported that two strains displayed rhythms with genotype-specific periodicity, in agreement with their original reports. We cannot replicate this finding and show that the manually annotated data, the original automatically segmented data, and a new dataset provide no evidence for either the existence of song rhythms or song periodicity differences between genotypes. Furthermore, we have reexamined our methods and analysis and find that our automated segmentation method was not biased to prevent detection of putative song periodicity. We conclude that there is no evidence for the existence of Drosophila courtship song rhythms.

A neuropeptide circuit that coordinates sperm transfer and copulation duration in Drosophila.

Innate behaviors are often executed in concert with accompanying physiological programs. How this coordination is achieved is poorly understood. Mating behavior and the transfer of sperm and seminal fluid (SSFT) provide a model for understanding how concerted behavioral and physiological programs are coordinated. Here we identify a male-specific neural pathway that coordinates the timing of SSFT with the duration of copulation behavior in Drosophila. Silencing four abdominal ganglion (AG) interneurons (INs) that contain the neuropeptide corazonin (Crz) both blocked SSFT and substantially lengthened copulation duration. Activating these Crz INs caused rapid ejaculation in isolated males, a phenotype mimicked by injection of Crz peptide. Crz promotes SSFT by activating serotonergic (5-HT) projection neurons (PNs) that innervate the accessory glands. Activation of these PNs in copulo caused premature SSFT and also shortened copulation duration. However, mating terminated normally when these PNs were silenced, indicating that SSFT is not required for appropriate copulation duration. Thus, the lengthened copulation duration phenotype caused by silencing Crz INs is independent of the block to SSFT. We conclude that four Crz INs independently control SSFT and copulation duration, thereby coupling the timing of these two processes.

Multi-channel acoustic recording and automated analysis of Drosophila courtship songs.

BACKGROUND: Drosophila melanogaster has served as a powerful model system for genetic studies of courtship songs. To accelerate research on the genetic and neural mechanisms underlying courtship song, we have developed a sensitive recording system to simultaneously capture the acoustic signals from 32 separate pairs of courting flies as well as software for automated segmentation of songs. RESULTS: Our novel hardware design enables recording of low amplitude sounds in most laboratory environments. We demonstrate the power of this system by collecting, segmenting and analyzing over 18 hours of courtship song from 75 males from five wild-type strains of Drosophila melanogaster. Our analysis reveals previously undetected modulation of courtship song features and extensive natural genetic variation for most components of courtship song. Despite having a large dataset with sufficient power to detect subtle modulations of song, we were unable to identify previously reported periodic rhythms in the inter-pulse interval of song. We provide detailed instructions for assembling the hardware and for using our open-source segmentation software. CONCLUSIONS: Analysis of a large dataset of acoustic signals from Drosophila melanogaster provides novel insight into the structure and dynamics of species-specific courtship songs. Our new system for recording and analyzing fly acoustic signals should therefore greatly accelerate future studies of the genetics, neurobiology and evolution of courtship song.

Inferring neural dynamics of memory during naturalistic social communication.

Memory processes in complex behaviors like social communication require forming representations of the past that grow with time. The neural mechanisms that support such continually growing memory remain unknown. We address this gap in the context of fly courtship, a natural social behavior involving the production and perception of long, complex song sequences. To study female memory for male song history in unrestrained courtship, we present 'Natural Continuation' (NC)-a general, simulation-based model comparison procedure to evaluate candidate neural codes for complex stimuli using naturalistic behavioral data. Applying NC to fly courtship revealed strong evidence for an adaptive population mechanism for how female auditory neural dynamics could convert long song histories into a rich mnemonic format. Song temporal patterning is continually transformed by heterogeneous nonlinear adaptation dynamics, then integrated into persistent activity, enabling common neural mechanisms to retain continuously unfolding information over long periods and yielding state-of-the-art predictions of female courtship behavior. At a population level this coding model produces multi-dimensional advection-diffusion-like responses that separate songs over a continuum of timescales and can be linearly transformed into flexible output signals, illustrating its potential to create a generic, scalable mnemonic format for extended input signals poised to drive complex behavioral responses. This work thus shows how naturalistic behavior can directly inform neural population coding models, revealing here a novel process for memory formation.

The role of fruitless in specifying courtship behaviors across divergent Drosophila species.

Sex-specific behaviors are critical for reproduction and species survival. The sex-specifically spliced transcription factor fruitless (fru) helps establish male courtship behaviors in invertebrates. Forcing male-specific fru (fruM) splicing in Drosophila melanogaster females produces male-typical behaviors while disrupting female-specific behaviors. However, whether fru's joint role in specifying male and inhibiting female behaviors is conserved across species is unknown. We used CRISPR-Cas9 to force FruM expression in female Drosophila virilis, a species in which males and females produce sex-specific songs. In contrast to D. melanogaster, in which one fruM allele is sufficient to generate male behaviors in females, two alleles are needed in D. virilis females. D. virilis females expressing FruM maintain the ability to sing female-typical song as well as lay eggs, whereas D. melanogaster FruM females cannot lay eggs. These results reveal potential differences in fru function between divergent species and underscore the importance of studying diverse behaviors and species for understanding the genetic basis of sex differences.

From connectome to effectome: learning the causal interaction map of the fly brain.

A long-standing goal of neuroscience is to obtain a causal model of the nervous system. This would allow neuroscientists to explain animal behavior in terms of the dynamic interactions between neurons. The recently reported whole-brain fly connectome [1-7] specifies the synaptic paths by which neurons can affect each other but not whether, or how, they do affect each other in vivo. To overcome this limitation, we introduce a novel combined experimental and statistical strategy for efficiently learning a causal model of the fly brain, which we refer to as the "effectome". Specifically, we propose an estimator for a dynamical systems model of the fly brain that uses stochastic optogenetic perturbation data to accurately estimate causal effects and the connectome as a prior to drastically improve estimation efficiency. We then analyze the connectome to propose circuits that have the greatest total effect on the dynamics of the fly nervous system. We discover that, fortunately, the dominant circuits significantly involve only relatively small populations of neurons-thus imaging, stimulation, and neuronal identification are feasible. Intriguingly, we find that this approach also re-discovers known circuits and generates testable hypotheses about their dynamics. Overall, our analyses of the connectome provide evidence that global dynamics of the fly brain are generated by a large collection of small and often anatomically localized circuits operating, largely, independently of each other. This in turn implies that a causal model of a brain, a principal goal of systems neuroscience, can be feasibly obtained in the fly.

Network Statistics of the Whole-Brain Connectome of Drosophila.

Brains comprise complex networks of neurons and connections. Network analysis applied to the wiring diagrams of brains can offer insights into how brains support computations and regulate information flow. The completion of the first whole-brain connectome of an adult Drosophila, the largest connectome to date, containing 130,000 neurons and millions of connections, offers an unprecedented opportunity to analyze its network properties and topological features. To gain insights into local connectivity, we computed the prevalence of two- and three-node network motifs, examined their strengths and neurotransmitter compositions, and compared these topological metrics with wiring diagrams of other animals. We discovered that the network of the fly brain displays rich club organization, with a large population (30% percent of the connectome) of highly connected neurons. We identified subsets of rich club neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions or neuropils. These data products are shared within the FlyWire Codex and will serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.

Comparative connectomics of the descending and ascending neurons of the Drosophila nervous system: stereotypy and sexual dimorphism.

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.

Neural representations of courtship song in the Drosophila brain.

Acoustic communication in drosophilid flies is based on the production and perception of courtship songs, which facilitate mating. Despite decades of research on courtship songs and behavior in Drosophila, central auditory responses have remained uncharacterized. In this study, we report on intracellular recordings from central neurons that innervate the Drosophila antennal mechanosensory and motor center (AMMC), the first relay for auditory information in the fly brain. These neurons produce graded-potential (nonspiking) responses to sound; we compare recordings from AMMC neurons to extracellular recordings of the receptor neuron population [Johnston's organ neurons (JONs)]. We discover that, while steady-state response profiles for tonal and broadband stimuli are significantly transformed between the JON population in the antenna and AMMC neurons in the brain, transient responses to pulses present in natural stimuli (courtship song) are not. For pulse stimuli in particular, AMMC neurons simply low-pass filter the receptor population response, thus preserving low-frequency temporal features (such as the spacing of song pulses) for analysis by postsynaptic neurons. We also compare responses in two closely related Drosophila species, Drosophila melanogaster and Drosophila simulans, and find that pulse song responses are largely similar, despite differences in the spectral content of their songs. Our recordings inform how downstream circuits may read out behaviorally relevant information from central neurons in the AMMC.

Sec5, a member of the exocyst complex, mediates Drosophila embryo cellularization.

Cellularization of the Drosophila embryo is the process by which a syncytium of approximately 6000 nuclei is subdivided into discrete cells. In order to individualize the cells, massive membrane addition needs to occur by a process that is not fully understood. The exocyst complex is required for some, but not all, forms of exocytosis and plays a role in directing vesicles to appropriate domains of the plasma membrane. Sec5 is a central component of this complex and we here report the isolation of a new allele of sec5 that has a temperature-sensitive phenotype. Using this allele, we investigated whether the exocyst complex is required for cellularization. Embryos from germline clones of the sec5(ts1) allele progress normally through cycle 13. At cellularization, however, cleavage furrows do not invaginate between nuclei and consequently cells do not form. A zygotically translated membrane protein, Neurotactin, is not inserted into the plasma membrane and instead accumulates in cytoplasmic puncta. During cellularization, Sec5 becomes concentrated at the apical end of the lateral membranes, which is likely to be the major site of membrane addition. Subsequently, Sec5 concentrates at the sub-apical complex, indicating a role for Sec5 in the polarized epithelium. Thus, the exocyst is necessary for, and is likely to direct, the polarized addition of new membrane during this form of cytokinesis.

Transcriptional profiling of Drosophila male-specific P1 (pC1) neurons.

In Drosophila melanogaster, the P1 (pC1) cluster of male-specific neurons both integrates sensory cues and drives or modulates behavioral programs such as courtship, in addition to contributing to a social arousal state. The behavioral function of these neurons is linked to the genes they express, which underpin their capacity for synaptic signaling, neuromodulation, and physiology. Yet, P1 (pC1) neurons have not been fully characterized at the transcriptome level. Moreover, it is unknown how the molecular landscape of P1 (pC1) neurons acutely changes after flies engage in social behaviors, where baseline P1 (pC1) neural activity is expected to increase. To address these two gaps, we use single cell-type RNA sequencing to profile and compare the transcriptomes of P1 (pC1) neurons harvested from socially paired versus solitary male flies. Compared to control transcriptome datasets, we find that P1 (pC1) neurons are enriched in 2,665 genes, including those encoding receptors, neuropeptides, and cell-adhesion molecules (dprs/DIPs). Furthermore, courtship is characterized by changes in ~300 genes, including those previously implicated in regulating behavior (e.g. DopEcR, Octß3R, Fife, kairos, rad). Finally, we identify a suite of genes that link conspecific courtship with the innate immune system. Together, these data serve as a molecular map for future studies of an important set of higher-order and sexually-dimorphic neurons.