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Brains contain networks of interconnected neurons, so knowing the network architecture is essential for understanding brain function. We therefore mapped the synaptic-resolution connectome of an insect brain (Drosophilalarva) with rich behavior, including learning, value-computation, and action-selection, comprising 3,013 neurons and 544,000 synapses. We characterized neuron-types, hubs, feedforward and feedback pathways, and cross-hemisphere and brain-nerve cord interactions. We found pervasive multisensory and interhemispheric integration, highly recurrent architecture, abundant feedback from descending neurons, and multiple novel circuit motifs. The brain’s most recurrent circuits comprised the input and output neurons of the learning center. Some structural features, including multilayer shortcuts and nested recurrent loops, resembled powerful machine learning architectures. The identified brain architecture provides a basis for future experimental and theoretical studies of neural circuits.One-Sentence SummaryWe generated a synaptic-resolution brain connectome and characterized its connection types, neuron types, and circuit motifs.

Animal behavior is shaped both by evolution and by individual experience. Parallel brain pathways encode innate and learned valences of cues, but the way in which they are integrated during action-selection is not well understood. We used electron microscopy to comprehensively map with synaptic resolution all neurons downstream of all mushroom body (MB) output neurons (encoding learned valences) and characterized their patterns of interaction with lateral horn (LH) neurons (encoding innate valences) in Drosophila larva. The connectome revealed multiple convergence neuron types that receive convergent MB and LH inputs. A subset of these receives excitatory input from positive-valence MB and LH pathways and inhibitory input from negative-valence MB pathways. We confirmed functional connectivity from LH and MB pathways and behavioral roles of two of these neurons. These neurons encode integrated odor value and bidirectionally regulate turning. Based on this, we speculate that learning could potentially skew the balance of excitation and inhibition onto these neurons and thereby modulate turning. Together, our study provides insights into the circuits that integrate learned and innate valences to modify behavior.

The assembly of neural circuits is dependent upon precise spatiotemporal expression of cell recognition molecules1–6. Factors controlling cell-type specificity have been identified7–9, but how timing is determined remains unknown. Here we describe the induction of a cascade of transcription factors by a steroid hormone (Ecdysone) in all fly visual system neurons spanning target recognition and synaptogenesis. We demonstrate through single cell sequencing that the Ecdysone pathway regulates the expression of a common set of targets required for synaptic maturation and cell-type specific targets enriched for cell surface proteins regulating wiring specificity. Transcription factors in the cascade regulate the expression of the same wiring genes in complex ways, including activation in one cell-type and repression in another. We show that disruption of the Ecdysone-pathway generates specific defects in dendritic and axonal processes and synaptic connectivity, with the order of transcription factor expression correlating with sequential steps in wiring. We also identify shared targets of a cell-type specific transcription factor and the Ecdysone pathway which regulate specificity. We propose neurons integrate a global temporal transcriptional module with cell-type specific transcription factors to generate different cell-type specific patterns of cell recognition molecules regulating wiring.

Summary Precise patterns of synaptic connections between neurons are encoded in their genetic programs. Here, we use single-cell RNA sequencing to profile neuronal transcriptomes at multiple stages in the developing Drosophila visual system. We devise an efficient strategy for profiling neurons at multiple time points in a single pool, thereby minimizing batch effects and maximizing the reliability of time-course data. A transcriptional atlas spanning multiple stages is generated, including more than 150 distinct neuronal populations; of these, 88 are followed through synaptogenesis. This analysis reveals a common (pan-neuronal) program unfolding in highly coordinated fashion in all neurons, including genes encoding proteins comprising the core synaptic machinery and membrane excitability. This program is overlaid by cell-type-specific programs with diverse cell recognition molecules expressed in different combinations and at different times. We propose that a pan-neuronal program endows neurons with the competence to form synapses and that cell-type-specific programs control synaptic specificity.

Animal behavior is shaped both by evolution and by individual experience. In many species parallel brain pathways are thought to encode innate and learnt behavior drives and as a result may link the same sensory cue to different actions if innate and learnt drives are in opposition. How these opposing drives are integrated into a single coherent action is not well understood. In insects, the Mushroom Body Output Neurons (MBONs) and the Lateral Horn Neurons (LHNs) are thought to provide the learnt and innate drives, respectively. However their patterns of convergence and the mechanisms by which their outputs are used to select actions are not well understood. We used electron microscopy reconstruction to comprehensively map the downstream targets of all MBONs in Drosophila larva and characterise their patterns of convergence with LHNs. We discovered convergence neurons that receive direct input from MBONs and LHNs and compare opposite behaviour drives. Functional imaging and optogenetic manipulation suggest these convergence neurons compute the overall predicted value of approaching or avoiding an odor and mediate action selection. Our study describes the circuit mechanisms allowing integration of opposing drives from parallel olfactory pathways.

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 the first synaptic-resolution connectome of the circuitry upstream of all DANs in a learning center, the mush-room body (MB) of Drosophila larva. We discover afferent sensory pathways and a large population of neurons that provide feedback from MB 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 modelling. 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 modelling 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.

SummaryOur nervous system is organized into circuits with specifically matched and tuned cell-to-cell connections that are essential for proper function. The mechanisms by which presynaptic axon terminals and postsynaptic dendrites recognize each other and establish the correct number of connections are still incompletely understood. Sperry’s chemoaffinity hypothesis proposes that pre- and postsynaptic partners express specific combinations of molecules that enable them to recognize each other. Alternatively, Peters’ rule proposes that presynaptic axons and postsynaptic dendrites use non-partner-derived global positional cues to independently reach their target area, and once there they randomly connect with any available neuron. These connections can then be further refined by additional mechanisms based on synaptic activity. We used the tractable genetic model system, the Drosophila embryo and larva, to test these hypotheses and elucidate the roles of 1) global positional cues, 2) partner-derived cues and 3) synaptic activity in the establishment of selective connections in the developing nerve cord. We altered the position or activity of presynaptic partners and analyzed the effect of these manipulations on the number of synapses with specific postsynaptic partners, strength of functional connections, and behavior controlled by these neurons. For this purpose, we combined developmental live imaging, electron microscopy reconstruction of circuits, functional imaging of neuronal activity, and behavioral experiments in wildtype and experimental animals. We found that postsynaptic dendrites are able to find, recognize, and connect to their presynaptic partners even when these have been shifted to ectopic locations through the overexpression of receptors for midline guidance cues. This suggests that neurons use partner-derived cues that allow them to identify and connect to each other. However, while partner-derived cues are sufficient for recognition between specific partners and establishment of connections;; without orderly positioning of axon terminals by positional cues and without synaptic activity during embryonic development, the numbers of functional connections are altered with significant consequences for behavior. Thus, multiple mechanisms including global positional cues, partner-derived cues, and synaptic activity contribute to proper circuit assembly in the developing Drosophila nerve cord.

Modulatory (e.g. dopaminergic) neurons provide “teaching signals” that drive associative learning across the animal kingdom, but the circuits that regulate their activity and compute teaching signals are still poorly understood. We provide the first synaptic-resolution connectome of the circuitry upstream of all modulatory neurons in a brain center for associative learning, the mushroom body (MB) of theDrosophilalarva. We discovered afferent pathways from sensory neurons, as well as an unexpected large population of 61 feedback neuron pairs that provide one- and two-step feedback from MB output neurons. The majority of these feedback pathways link distinct memory systems (e.g. aversive and appetitive). We functionally confirmed some of the structural pathways and found that some modulatory neurons compare inhibitory input from their own compartment and excitatory input from compartments of opposite valence, enabling them to compute integrated common-currency predicted values across aversive and appetitive memory systems. This architecture suggests that the MB functions as an interconnected ensemble during learning and that distinct types of previously formed memories can regulate future learning about a stimulus. We developed a model of the circuit constrained by the connectome and by the functional data which revealed that the newly discovered architectural motifs, namely the multilevel feedback architecture and the extensive cross-compartment connections, increase the computational performance and flexibility on learning tasks. Together our study provides the most detailed view to date of a recurrent brain circuit that computes teaching signals and provides insights into the architectural motifs that support reinforcement learning in a biological system.

Natural events present multiple types of sensory cues, each detected by a specialized sensory modality. Combining information from several modalities is essential for the selection of appropriate actions. Key to understanding multimodal computations is determining the structural patterns of multimodal convergence and how these patterns contribute to behaviour. Modalities could converge early, late or at multiple levels in the sensory processing hierarchy. Here we show that combining mechanosensory and nociceptive cues synergistically enhances the selection of the fastest mode of escape locomotion in Drosophila larvae. In an electron microscopy volume that spans the entire insect nervous system, we reconstructed the multisensory circuit supporting the synergy, spanning multiple levels of the sensory processing hierarchy. The wiring diagram revealed a complex multilevel multimodal convergence architecture. Using behavioural and physiological studies, we identified functionally connected circuit nodes that trigger the fastest locomotor mode, and others that facilitate it, and we provide evidence that multiple levels of multimodal integration contribute to escape mode selection. We propose that the multilevel multimodal convergence architecture may be a general feature of multisensory circuits enabling complex input-output functions and selective tuning to ecologically relevant combinations of cues.

High-throughput sequencing of the antibody repertoire is enabling a thorough analysis of B cell diversity and clonal selection, which may improve the novel antibody discovery process. Theoretically, an adequate bioinformatic analysis could allow identification of candidate antigen-specific antibodies, requiring their recombinant production for experimental validation of their specificity. Gene synthesis is commonly used for the generation of recombinant antibodies identified in silico. Novel strategies that bypass gene synthesis could offer more accessible antibody identification and validation alternatives. We developed a hybridization-based recovery strategy that targets the complementarity-determining region 3 (CDRH3) for the enrichment of cDNA of candidate antigen-specific antibody sequences. Ten clonal groups of interest were identified through bioinformatic analysis of the heavy chain antibody repertoire of mice immunized with hen egg white lysozyme (HEL). cDNA from eight of the targeted clonal groups was recovered efficiently, leading to the generation of recombinant antibodies. One representative heavy chain sequence from each clonal group recovered was paired with previously reported anti-HEL light chains to generate full antibodies, later tested for HEL-binding capacity. The recovery process proposed represents a simple and scalable molecular strategy that could enhance antibody identification and specificity assessment, enabling a more cost-efficient generation of recombinant antibodies.

Summary The mechanisms by which synaptic partners recognize each other and establish appropriate numbers of connections during embryonic development to form functional neural circuits are poorly understood. We combined electron microscopy reconstruction, functional imaging of neural activity, and behavioral experiments to elucidate the roles of (1) partner identity, (2) location, and (3) activity in circuit assembly in the embryonic nerve cord of Drosophila. We found that postsynaptic partners are able to find and connect to their presynaptic partners even when these have been shifted to ectopic locations or silenced. However, orderly positioning of axon terminals by positional cues and synaptic activity is required for appropriate numbers of connections between specific partners, for appropriate balance between excitatory and inhibitory connections, and for appropriate functional connectivity and behavior. Our study reveals with unprecedented resolution the fine connectivity effects of multiple factors that work together to control the assembly of neural circuits., Highlights • Synaptic partners manage to find and connect with each other in aberrant locations • Partner recognition alone does not ensure proper circuit formation and function • Appropriate location and partner identity are required for proper synapse numbers • Developmental activity affects balance of excitation and inhibition in the circuit, Valdes-Aleman et al. show that neurons manage to find and connect to their partners even at abnormal locations or when silenced. However, inappropriate numbers of connections are established, disrupting the balance of excitation and inhibition and generating deficient behavior. Neuron location, identity, and developmental activity work together to control circuit assembly.