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Aggressive social interactions are used to compete for limited resources and are regulated by complex sensory cues and the organism’s internal state. While both sexes exhibit aggression, its neuronal underpinnings are understudied in females. Here, we identify a population of sexually dimorphic aIPg neurons in the adultDrosophila melanogastercentral brain whose optogenetic activation increased, and genetic inactivation reduced, female aggression. Analysis of GAL4 lines identified in an unbiased screen for increased female chasing behavior revealed the involvement of another sexually dimorphic neuron, pC1d, and implicated aIPg and pC1d neurons as core nodes regulating female aggression. Connectomic analysis demonstrated that aIPg neurons and pC1d are interconnected and suggest that aIPg neurons may exert part of their effect by gating the flow of visual information to descending neurons. Our work reveals important regulatory components of the neuronal circuitry that underlies female aggressive social interactions and provides tools for their manipulation.

Aggressive social interactions are used to compete for limited resources and are regulated by complex sensory cues and the organism’s internal state. While both sexes exhibit aggression, its neuronal underpinnings are understudied in females. Here, we describe a set of connected neurons in the adultDrosophila melanogastercentral brain that drive female aggression. We identified a population of sexually dimorphic aIPg neurons whose optogenetic activation increased, and genetic inactivation reduced, female aggression. Analysis of GAL4 lines identified in an unbiased screen for increased female chasing behavior revealed the involvement of another sexually dimorphic neuron, pC1d, and implicated pC1d and aIPg neurons as core nodes regulating female aggression. pC1d activation increased female aggression and electron microscopy (EM) connectomic analysis demonstrated that aIPg neurons and pC1d have strong reciprocal connections. Our work reveals important regulatory components of the neuronal circuitry that underlies female aggressive social interactions and provides tools for their manipulation.

Insects, like most animals, tend to steer away from imminent threats [1-7]. Drosophila melanogaster, for example, generally initiate an escape take-off in response to a looming visual stimulus, mimicking a potential predator [8]. The escape response to a visual threat is, however, flexible [9-12] and can alternatively consist of walking backward away from the perceived threat [11], which may be a more effective response to ambush predators such as nymphal praying mantids [7]. Flexibility in escape behavior may also add an element of unpredictability that makes it difficult for predators to anticipate or learn the prey's likely response [3-6]. Whereas the fly's escape jump has been well studied [8, 9, 13-18], the neuronal underpinnings of evasive walking remain largely unexplored. We previously reported the identification of a cluster of descending neurons-the moonwalker descending neurons (MDNs)-the activity of which is necessary and sufficient to trigger backward walking [19], as well as a population of visual projection neurons-the lobula columnar 16 (LC16) cells-that respond to looming visual stimuli and elicit backward walking and turning [11]. Given the similarity of their activation phenotypes, we hypothesized that LC16 neurons induce backward walking via MDNs and that turning while walking backward might reflect asymmetric activation of the left and right MDNs. Here, we present data from functional imaging, behavioral epistasis, and unilateral activation experiments that support these hypotheses. We conclude that LC16 and MDNs are critical components of the neural circuit that transduces threatening visual stimuli into directional locomotor output.

Recent developments in machine vision methods for automatic, quantitative analysis of social behavior have immensely improved both the scale and level of resolution with which we can dissect interactions between members of the same species. In this paper, we review these methods, with a particular focus on how biologists can apply them to their own work. We discuss several components of machine vision-based analyses: methods to record high-quality video for automated analyses, video-based tracking algorithms for estimating the positions of interacting animals, and machine learning methods for recognizing patterns of interactions. These methods are extremely general in their applicability, and we review a subset of successful applications of them to biological questions in several model systems with very different types of social behaviors.

Assigning behavioral functions to neural structures has long been a central goal in neuroscience and is a necessary first step toward a circuit-level understanding of how the brain generates behavior. Here, we map the neural substrates of locomotion and social behaviors for Drosophila melanogaster using automated machine-vision and machine-learning techniques. From videos of 400,000 flies, we quantified the behavioral effects of activating 2,204 genetically targeted populations of neurons. We combined a novel quantification of anatomy with our behavioral analysis to create brain-behavior correlation maps, which are shared as browsable web pages and interactive software. Based on these maps, we generated hypotheses of regions of the brain causally related to sensory processing, locomotor control, courtship, aggression, and sleep. Our maps directly specify genetic tools to target these regions, which we used to identify a small population of neurons with a role in the control of walking.

Hb9 is a homeodomain-containing transcription factor that acts in combination with Nkx6, Lim3, and Tail-up (Islet) to guide the stereotyped differentiation, connectivity, and function of a subset of neurons in Drosophila. The role of Hb9 in directing neuronal differentiation is well documented, but the lineage of Hb9(+) neurons is only partly characterized, its regulation is poorly understood, and most of the downstream genes through which it acts remain at large. Here, we complete the lineage tracing of all embryonic Hb9(+) neurons (to eight neuronal lineages) and provide evidence that hb9, lim3, and tail-up are coordinately regulated by a common set of upstream factors. Through the parallel use of micro-array gene expression profiling and the Dam-ID method, we searched for Hb9-regulated genes, uncovering transcription factors as the most over-represented class of genes regulated by Hb9 (and Nkx6) in the CNS. By a nearly ten-to-one ratio, Hb9 represses rather than activates transcription factors, highlighting transcriptional repression of other transcription factors as a core mechanism by which Hb9 governs neuronal determination. From the small set of genes activated by Hb9, we characterized the expression and function of two - fd59a/foxd, which encodes a transcription factor, and Nitric oxide synthase. Under standard lab conditions, both genes are dispensable for Drosophila development, but Nos appears to inhibit hyper-active behavior and fd59a appears to act in octopaminergic neurons to control egg-laying behavior. Together our data clarify the mechanisms through which Hb9 governs neuronal specification and differentiation and provide an initial characterization of the expression and function of Nos and fd59a in the Drosophila CNS.

SUMMARY Walking fruit flies, Drosophila melanogaster, use visual information to orient towards salient objects in their environment, presumably as a search strategy for finding food, shelter or other resources. Less is known, however, about the role of vision or other sensory modalities such as mechanoreception in the evaluation of objects once they have been reached. To study the role of vision and mechanoreception in exploration behavior, we developed a large arena in which we could track individual fruit flies as they walked through either simple or more topologically complex landscapes. When exploring a simple, flat environment lacking three-dimensional objects, flies used visual cues from the distant background to stabilize their walking trajectories. When exploring an arena containing an array of cones, differing in geometry, flies actively oriented towards, climbed onto, and explored the objects, spending most of their time on the tallest, steepest object. A fly's behavioral response to the geometry of an object depended upon the intrinsic properties of each object and not a relative assessment to other nearby objects. Furthermore, the preference was not due to a greater attraction towards tall, steep objects, but rather a change in locomotor behavior once a fly reached and explored the surface. Specifically, flies are much more likely to stop walking for long periods when they are perched on tall, steep objects. Both the vision system and the antennal chordotonal organs (Johnston's organs) provide sufficient information about the geometry of an object to elicit the observed change in locomotor behavior. Only when both these sensory systems were impaired did flies not show the behavioral preference for the tall, steep objects.

We present a camera-based method for automatically quantifying the individual and social behaviors of fruit flies, Drosophila melanogaster, interacting within a planar arena. Our system includes machine vision algorithms that accurately track many individuals without swapping identities and classification algorithms that detect behaviors. The data may be represented as an ethogram that plots the time course of behaviors exhibited by each fly, or as a vector that concisely captures the statistical properties of all behaviors displayed within a given period. We found that behavioral differences between individuals are consistent over time and are sufficient to accurately predict gender and genotype. In addition, we show that the relative positions of flies during social interactions vary according to gender, genotype, and social environment. We expect that our software, which permits high-throughput screening, will complement existing molecular methods available in Drosophila, facilitating new investigations into the genetic and cellular basis of behavior.

Modern supervised learning algorithms can learn very accurate and complex discriminating functions. But when these classifiers fail, this complexity can also be a drawback because there is no easy, intuitive way to diagnose why they are failing and remedy the problem. This important question has received little attention. To address this problem, we propose a novel method to analyze and understand a classifier's errors. Our method centers around a measure of how much influence a training example has on the classifier's prediction for a test example. To understand why a classifier is mispredicting the label of a given test example, the user can find and review the most influential training examples that caused this misprediction, allowing them to focus their attention on relevant areas of the data space. This will aid the user in determining if and how the training data is inconsistently labeled or lacking in diversity, or if the feature representation is insufficient. As computing the influence of each training example is computationally impractical, we propose a novel distance metric to approximate influence for boosting classifiers that is fast enough to be used interactively. We also show several novel use paradigms of our distance metric. Through experiments, we show that it can be used to find incorrectly or inconsistently labeled training examples, to find specific areas of the data space that need more training data, and to gain insight into which features are missing from the current representation.

Animals discriminate stimuli, learn their predictive value and use this knowledge to modify their behavior. In Drosophila, the mushroom body (MB) plays a key role in these processes. Sensory stimuli are sparsely represented by ∼2000 Kenyon cells, which converge onto 34 output neurons (MBONs) of 21 types. We studied the role of MBONs in several associative learning tasks and in sleep regulation, revealing the extent to which information flow is segregated into distinct channels and suggesting possible roles for the multi-layered MBON network. We also show that optogenetic activation of MBONs can, depending on cell type, induce repulsion or attraction in flies. The behavioral effects of MBON perturbation are combinatorial, suggesting that the MBON ensemble collectively represents valence. We propose that local, stimulus-specific dopaminergic modulation selectively alters the balance within the MBON network for those stimuli. Our results suggest that valence encoded by the MBON ensemble biases memory-based action selection. DOI: http://dx.doi.org/10.7554/eLife.04580.001, eLife digest An animal's survival depends on its ability to respond appropriately to its environment, approaching stimuli that signal rewards and avoiding any that warn of potential threats. In fruit flies, this behavior requires activity in a region of the brain called the mushroom body, which processes sensory information and uses that information to influence responses to stimuli. Aso et al. recently mapped the mushroom body of the fruit fly in its entirety. This work showed, among other things, that the mushroom body contained 21 different types of output neurons. Building on this work, Aso et al. have started to work out how this circuitry enables flies to learn to associate a stimulus, such as an odor, with an outcome, such as the presence of food. Two complementary techniques—the use of molecular genetics to block neuronal activity, and the use of light to activate neurons (a technique called optogenetics)—were employed to study the roles performed by the output neurons in the mushroom body. Results revealed that distinct groups of output cells must be activated for flies to avoid—as opposed to approach—odors. Moreover, the same output neurons are used to avoid both odors and colors that have been associated with punishment. Together, these results indicate that the output cells do not encode the identity of stimuli: rather, they signal whether a stimulus should be approached or avoided. The output cells also regulate the amount of sleep taken by the fly, which is consistent with the mushroom body having a broader role in regulating the fly's internal state. The results of these experiments—combined with new knowledge about the detailed structure of the mushroom body—lay the foundations for new studies that explore associative learning at the level of individual circuits and their component cells. Given that the organization of the mushroom body has much in common with that of the mammalian brain, these studies should provide insights into the fundamental principles that underpin learning and memory in other species, including humans. DOI: http://dx.doi.org/10.7554/eLife.04580.002

We present a machine learning-based system for automatically computing interpretable, quantitative measures of animal behavior. Through our interactive system, users encode their intuition about behavior by annotating a small set of video frames. These manual labels are converted into classifiers that can automatically annotate behaviors in screen-scale data sets. Our general-purpose system can create a variety of accurate individual and social behavior classifiers for different organisms, including mice and adult and larval Drosophila.

SummaryAn important role of visual systems is to detect nearby predators, prey, and potential mates [1], which may be distinguished in part by their motion. When an animal is at rest, an object moving in any direction may easily be detected by motion-sensitive visual circuits [2, 3]. During locomotion, however, this strategy is compromised because the observer must detect a moving object within the pattern of optic flow created by its own motion through the stationary background. However, objects that move creating back-to-front (regressive) motion may be unambiguously distinguished from stationary objects because forward locomotion creates only front-to-back (progressive) optic flow. Thus, moving animals should exhibit an enhanced sensitivity to regressively moving objects. We explicitly tested this hypothesis by constructing a simple fly-sized robot that was programmed to interact with a real fly. Our measurements indicate that whereas walking female flies freeze in response to a regressively moving object, they ignore a progressively moving one. Regressive motion salience also explains observations of behaviors exhibited by pairs of walking flies. Because the assumptions underlying the regressive motion salience hypothesis are general, we suspect that the behavior we have observed in Drosophila may be widespread among eyed, motile organisms.

To understand the relationship between neural anatomy and behavior, the ultimate output of the nervous system, we performed a high-throughput, thermogenetic screen of 2,200 transgenic Drosophila lines from the Janelia GAL4 collection. Each GAL4 line drives expression in a different, sparse subset of neurons in the fly nervous system. Using genetic techniques, we selectively activated these sparse subsets of neurons, and measured the behavioral effects. We developed a high-throughput, automated system for measuring the flies’ locomotion and social behavior with breadth and depth. We recorded 20,000 videos of groups of flies freely behaving in an open-field walking arena, totaling ∼400 TB of raw data. From the video, we tracked the flies’ body and wing positions using our tracking software, Ctrax. We used our machine learning-based behavior classification system, JAABA, to create 15 behavior classifiers (e.g. walking, chasing) that input trajectories from Ctrax and output predictions for each frame of each fly's behaviors (totaling ∼175 billion annotations of behavior). For each line of flies, we computed ∼200 behavior statistics, such as the fraction of time spent chasing, or average speed while walking, summarizing the behavioral effects of activating the targeted neurons in a concise, interpretable manner. Concurrent with our screen, the Janelia Fly Light project imaged the expression pattern of each GAL4 line, producing image stacks indicating which neurons are likely being activated in each line. By jointly analyzing these behavior and anatomy measurements, we created brain-behavior maps suggesting neural substrates for the generation and/or regulation of each behavior. This study is groundbreaking on two technological fronts. It represents the application of video-based behavior recognition to a highly phenotypically diverse data set of thousands of Drosophila genotypes, and the largest unbiased screen to assign function to neurons throughout the nervous system.

The pyloric rhythm of the stomatogastric ganglion of the crab, Cancer borealis, slows or stops when descending modulatory inputs are acutely removed. However, the rhythm spontaneously resumes after one or more days in the absence of neuromodulatory input. We recorded continuously for days to characterize quantitatively this recovery process. Activity bouts lasting 40–900 s began several hours after removal of neuromodulatory input and were followed by stable rhythm recovery after 1–4 days. Bout duration was not related to the intervals (0.3–800 min) between bouts. During an individual bout, the frequency rapidly increased and then decreased more slowly. Photoablation of back-filled neuromodulatory terminals in the stomatogastric ganglion (STG) neuropil had no effect on activity bouts or recovery, suggesting that these processes are intrinsic to the STG neuronal network. After removal of neuromodulatory input, the phase relationships of the components of the triphasic pyloric rhythm were altered, and then over time the phase relationships moved toward their control values. Although at low pyloric rhythm frequency the phase relationships among pyloric network neurons depended on frequency, the changes in frequency during recovery did not completely account for the change in phase seen after rhythm recovery. We suggest that activity bouts represent underlying mechanisms controlling the restructuring of the pyloric network to allow resumption of an appropriate output after removal of neuromodulatory input.

Backfills of the cerebral-buccal connective (CBC) of Aplysia californica revealed a cluster of five to seven pedal-buccal projection neurons in the anterolateral quadrant of the ventral surface of each pedal ganglion. Intra- and extracellular recordings showed that the pedal-buccal projection neurons shared common electrophysiological properties and synaptic inputs. However, they exhibited considerable heterogeneity with respect to their projection patterns. All pedal-buccal projection neurons that were tested received a slow excitatory postsynaptic potential from the ipsi- and contralateral cerebral-pedal regulator (C-PR) neuron, a cell that is thought to play a key role in the generation of a food-induced arousal state. Tests were conducted to identify potential synaptic follower neurons of the pedal-buccal projection neurons in the cerebral and buccal ganglia, but none were detected. Finally, nerve recordings revealed projections from the pedal-buccal projection neurons in the nerves associated with the buccal ganglion. In tests designed to determine the functional properties of these peripheral projections, no evidence was obtained supporting a mechanosensory or proprioceptive role and no movements were observed when they were fired. It is proposed that peripheral elements utilized in consummatory phases of Aplysia feeding may be directly influenced by a neuronal pathway that is activated during the food-induced arousal state.