Social state gates vision using three circuit mechanisms in Drosophila.

Animals are often bombarded with visual information and must prioritize specific visual features based on their current needs. The neuronal circuits that detect and relay visual features have been well-studied. Yet, much less is known about how an animal adjusts its visual attention as its goals or environmental conditions change. During social behaviors, flies need to focus on nearby flies. Here, we study how the flow of visual information is altered when female Drosophila enter an aggressive state. From the connectome, we identified three state-dependent circuit motifs poised to selectively amplify the response of an aggressive female to fly-sized visual objects: convergence of excitatory inputs from neurons conveying select visual features and internal state; dendritic disinhibition of select visual feature detectors; and a switch that toggles between two visual feature detectors. Using cell-type-specific genetic tools, together with behavioral and neurophysiological analyses, we show that each of these circuit motifs function during female aggression. We reveal that features of this same switch operate in males during courtship pursuit, suggesting that disparate social behaviors may share circuit mechanisms. Our work provides a compelling example of using the connectome to infer circuit mechanisms that underlie dynamic processing of sensory signals.

Cell types and neuronal circuitry underlying female aggression in Drosophila.

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 adult Drosophila melanogaster central 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.

Mapping the Neural Substrates of Behavior.

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.

Machine vision methods for analyzing social interactions.

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.

Mushroom body output neurons encode valence and guide memory-based action selection in Drosophila.

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.

Genome-wide identification of Drosophila Hb9 targets reveals a pivotal role in directing the transcriptome within eight neuronal lineages, including activation of nitric oxide synthase and Fd59a/Fox-D.

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.

JAABA: interactive machine learning for automatic annotation of animal behavior.

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.

Object preference by walking fruit flies, Drosophila melanogaster, is mediated by vision and graviperception.

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.

High-throughput ethomics in large groups of Drosophila.

We present a camera-based method for automatically quantifying the individual and social behaviors of fruit flies, Drosophila melanogaster, interacting in 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 in a given period. We found that behavioral differences between individuals were consistent over time and were sufficient to accurately predict gender and genotype. In addition, we found 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.

Episodic bouts of activity accompany recovery of rhythmic output by a neuromodulator- and activity-deprived adult neural network.

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.