Visual systems can exploit spatial correlations in the visual scene by using retinotopy. However, retinotopy is often lost, such as when visual pathways are integrated with other sensory modalities. How is spatial information processed outside of strictly visual brain areas? Here, we focused on visual looming responsive LC6 cells in , a population whose dendrites collectively cover the visual field, but whose axons form a single glomerulus-a structure without obvious retinotopic organization-in the central brain. We identified multiple cell types downstream of LC6 in the glomerulus and found that they more strongly respond to looming in different portions of the visual field, unexpectedly preserving spatial information. Through EM reconstruction of all LC6 synaptic inputs to the glomerulus, we found that LC6 and downstream cell types form circuits within the glomerulus that enable spatial readout of visual features and contralateral suppression-mechanisms that transform visual information for behavioral control.

Visually guided decision-making requires integration of information from distributed brain areas, necessitating a brain-wide approach to examine its neural mechanisms. New tools in Drosophila melanogaster enable circuits spanning the brain to be charted with single cell-type resolution. Here, we highlight recent advances uncovering the computations and circuits that transform and integrate visual information across the brain to make behavioral choices. Visual information flows from the optic lobes to three primary central brain regions: a sensorimotor mapping area and two 'higher' centers for memory or spatial orientation. Rapid decision-making during predator evasion emerges from the spike timing dynamics in parallel sensorimotor cascades. Goal-directed decisions may occur through memory, navigation and valence processing in the central complex and mushroom bodies.

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 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.

The mating decisions of Drosophila melanogaster females are primarily revealed through either of two discrete actions: opening of the vaginal plates to allow copulation, or extrusion of the ovipositor to reject the male. Both actions are triggered by the male courtship song, and both are dependent upon the female's mating status. Virgin females are more likely to open their vaginal plates in response to song; mated females are more likely to extrude their ovipositor. Here, we examine the neural cause and behavioral consequence of ovipositor extrusion. We show that the DNp13 descending neurons act as command-type neurons for ovipositor extrusion, and that ovipositor extrusion is an effective deterrent only when performed by females that have previously mated. The DNp13 neurons respond to male song via direct synaptic input from the pC2l auditory neurons. Mating status does not modulate the song responses of DNp13 neurons, but rather how effectively they can engage the motor circuits for ovipositor extrusion. We present evidence that mating status information is mediated by ppk sensory neurons in the uterus, which are activated upon ovulation. Vaginal plate opening and ovipositor extrusion are thus controlled by anatomically and functionally distinct circuits, highlighting the diversity of neural decision-making circuits even in the context of closely related behaviors with shared exteroceptive and interoceptive inputs.

Wiring a complex brain requires many neurons with intricate cell specificity, generated by a limited number of neural stem cells. central brain lineages are a predetermined series of neurons, born in a specific order. To understand how lineage identity translates to neuron morphology, we mapped 18 central brain lineages. While we found large aggregate differences between lineages, we also discovered shared patterns of morphological diversification. Lineage identity plus Notch-mediated sister fate govern primary neuron trajectories, whereas temporal fate diversifies terminal elaborations. Further, morphological neuron types may arise repeatedly, interspersed with other types. Despite the complexity, related lineages produce similar neuron types in comparable temporal patterns. Different stem cells even yield two identical series of dopaminergic neuron types, but with unrelated sister neurons. Together, these phenomena suggest that straightforward rules drive incredible neuronal complexity, and that large changes in morphology can result from relatively simple fating mechanisms.

Dopaminergic neurons (DANs) drive learning across the animal kingdom, but the upstream circuits that regulate their activity and thereby learning remain poorly understood. We provide a synaptic-resolution connectome of the circuitry upstream of all DANs in a learning center, the mushroom body of Drosophila larva. We discover afferent sensory pathways and a large population of neurons that provide feedback from mushroom body output neurons and link distinct memory systems (aversive and appetitive). We combine this with functional studies of DANs and their presynaptic partners and with comprehensive circuit modeling. We find that DANs compare convergent feedback from aversive and appetitive systems, which enables the computation of integrated predictions that may improve future learning. Computational modeling reveals that the discovered feedback motifs increase model flexibility and performance on learning tasks. Our study provides the most detailed view to date of biological circuit motifs that support associative learning.

Mating and egg laying are tightly cooordinated events in the reproductive life of all oviparous females. Oviposition is typically rare in virgin females but is initiated after copulation. Here we identify the neural circuitry that links egg laying to mating status in Drosophila melanogaster. Activation of female-specific oviposition descending neurons (oviDNs) is necessary and sufficient for egg laying, and is equally potent in virgin and mated females. After mating, sex peptide-a protein from the male seminal fluid-triggers many behavioural and physiological changes in the female, including the onset of egg laying. Sex peptide is detected by sensory neurons in the uterus, and silences these neurons and their postsynaptic ascending neurons in the abdominal ganglion. We show that these abdominal ganglion neurons directly activate the female-specific pC1 neurons. GABAergic (γ-aminobutyric-acid-releasing) oviposition inhibitory neurons (oviINs) mediate feed-forward inhibition from pC1 neurons to both oviDNs and their major excitatory input, the oviposition excitatory neurons (oviENs). By attenuating the abdominal ganglion inputs to pC1 neurons and oviINs, sex peptide disinhibits oviDNs to enable egg laying after mating. This circuitry thus coordinates the two key events in female reproduction: mating and egg laying.

The anatomy of many neural circuits is being characterized with increasing resolution, but their molecular properties remain mostly unknown. Here, we characterize gene expression patterns in distinct neural cell types of the visual system using genetic lines to access individual cell types, the TAPIN-seq method to measure their transcriptomes, and a probabilistic method to interpret these measurements. We used these tools to build a resource of high-resolution transcriptomes for 100 driver lines covering 67 cell types, available at http://www.opticlobe.com. Combining these transcriptomes with recently reported connectomes helps characterize how information is transmitted and processed across a range of scales, from individual synapses to circuit pathways. We describe examples that include identifying neurotransmitters, including cases of apparent co-release, generating functional hypotheses based on receptor expression, as well as identifying strong commonalities between different cell types.