The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly's brain.

Flexible behaviors over long timescales are thought to engage recurrent neural networks in deep brain regions, which are experimentally challenging to study. In insects, recurrent circuit dynamics in a brain region called the central complex (CX) enable directed locomotion, sleep, and context- and experience-dependent spatial navigation. We describe the first complete electron-microscopy-based connectome of the Drosophila CX, including all its neurons and circuits at synaptic resolution. We identified new CX neuron types, novel sensory and motor pathways, and network motifs that likely enable the CX to extract the fly’s head-direction, maintain it with attractor dynamics, and combine it with other sensorimotor information to perform vector-based navigational computations. We also identified numerous pathways that may facilitate the selection of CX-driven behavioral patterns by context and internal state. The CX connectome provides a comprehensive blueprint necessary for a detailed understanding of network dynamics underlying sleep, flexible navigation, and state-dependent action selection.

Making inferences about the computations performed by neuronal circuits from synapse-level connectivity maps is an emerging opportunity in neuroscience. The mushroom body (MB) is well positioned for developing and testing such an approach due to its conserved neuronal architecture, recently completed dense connectome, and extensive prior experimental studies of its roles in learning, memory and activity regulation. Here we identify new components of the MB circuit in Drosophila, including extensive visual input and MB output neurons (MBONs) with direct connections to descending neurons. We find unexpected structure in sensory inputs, in the transfer of information about different sensory modalities to MBONs, and in the modulation of that transfer by dopaminergic neurons (DANs). We provide insights into the circuitry used to integrate MB outputs, connectivity between the MB and the central complex and inputs to DANs, including feedback from MBONs. Our results provide a foundation for further theoretical and experimental work.

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The neural circuits responsible for animal behavior remain largely unknown. We 31 summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly 32 Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, 33 segment, find synapses in, and proofread such large data sets. We define cell types, refine 34 computational compartments, and provide an exhaustive atlas of cell examples and types, many of 35 them novel. We provide detailed circuits consisting of neurons and their chemical synapses for 36 most of the central brain. We make the data public and simplify access, reducing the effort needed 37 to answer circuit questions, and provide procedures linking the neurons defined by our analysis 38 with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs 39 on different scales, electrical consequences of compartmentalization, and evidence that 40 maximizing packing density is an important criterion in the evolution of the fly's brain. 41 1 of 57 53 Producing this data set required advances in sample preparation, imaging, image alignment, ma-54 chine segmentation of cells, synapse detection, data storage, proofreading software, and protocols 55 to arbitrate each decision. A number of new tests for estimating the completeness and accuracy 56 were required and therefore developed, in order to verify the correctness of the connectome. 57 These data describe whole-brain properties and circuits, as well as contain new methods to 58 classify cell types based on connectivity. Computational compartments are now more carefully 59 defined, we identify actual synaptic circuits, and each neuron is annotated by name and putative 60 cell type, making this the first complete census of neuropils, tracts, cells, and connections in this 61 2 of 57 Manuscript submitted to eLife Figure 2. Brain regions contained and defined in the hemibrain, following the naming conventions of (Ito et al., 2014) with the addition of (R) and (L) to specify the side of the soma for that region. Gray italics indicate master regions not explicitly defined in the hemibrain. Region LA is not included in the volume. The regions are hierarchical, with the more indented regions forming subsets of the less indented. The only exceptions are dACA, lACA, and vACA which are considered part of the mushroom body but are not contained in the master region MB.portion of the brain. We compare the statistics and structure of different brain regions, and for 62 the brain as a whole, without the confounds introduced by studying different circuitry in different 63 animals. 64 All data are publicly available through web interfaces. This includes a browser interface, Ne-65 uPrint (Clements et al., 2020), designed so that any interested user can query the hemibrain con-66 nectome even without specific training. NeuPrint can query the connectivity, partners, connection 67 strengths and morphologies of all specified neurons, thus making identifica...

Highlights d LPLC2 and LC4 are the primary direct visual inputs to the giant fiber (GF) d The GF sums LPLC2 and LC4 input to drive escape from looming d LPLC2-GF input encodes looming size, whereas LC4-GF input encodes looming speed d A model summing looming size and speed optical variables reproduces GF responses

Flexible behaviors over long timescales are thought to engage recurrent neural networks in deep brain regions, which are experimentally challenging to study. In insects, recurrent circuit dynamics in a brain region called the central complex (CX) enable directed locomotion, sleep, and context- and experience-dependent spatial navigation. We describe the first complete electron-microscopy-based connectome of the Drosophila CX, including all its neurons and circuits at synaptic resolution. We identified new CX neuron types, novel sensory and motor pathways, and network motifs that likely enable the CX to extract the fly’s head-direction, maintain it with attractor dynamics, and combine it with other sensorimotor information to perform vector-based navigational computations. We also identified numerous pathways that may facilitate the selection of CX-driven behavioral patterns by context and internal state. The CX connectome provides a comprehensive blueprint necessary for a detailed understanding of network dynamics underlying sleep, flexible navigation, and state-dependent action selection.

No abstract