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The brain's synaptic networks endow an animal with powerfully adaptive biological behavior. Maps of such synaptic circuits densely reconstructed in those model brains that can be examined and manipulated by genetic means offer the best prospect for understanding the underlying biological bases of behavior. That prospect is now technologically feasible and a scientifically enabling possibility in neurobiology, much as genomics has been in molecular biology and genetics. In Drosophila, two major advances are in electron microscopic technology, using focused ion beam–scanning electron microscopy (FIB-SEM) milling to capture and align digital images, and in computer-aided reconstruction of neuron morphologies. The last decade has witnessed enormous progress in detailed knowledge of the actual synaptic circuits formed by real neurons. Advances in various brain regions that heralded identification of the motion-sensing circuits in the optic lobe are now extending to other brain regions, with the prospect of encompassing the fly's entire nervous system, both brain and ventral nerve cord.

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 inDrosophila, 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.

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., eLife digest Animal brains of all sizes, from the smallest to the largest, work in broadly similar ways. Studying the brain of any one animal in depth can thus reveal the general principles behind the workings of all brains. The fruit fly Drosophila is a popular choice for such research. With about 100,000 neurons – compared to some 86 billion in humans – the fly brain is small enough to study at the level of individual cells. But it nevertheless supports a range of complex behaviors, including navigation, courtship and learning. Thanks to decades of research, scientists now have a good understanding of which parts of the fruit fly brain support particular behaviors. But exactly how they do this is often unclear. This is because previous studies showing the connections between cells only covered small areas of the brain. This is like trying to understand a novel when all you can see is a few isolated paragraphs. To solve this problem, Scheffer, Xu, Januszewski, Lu, Takemura, Hayworth, Huang, Shinomiya et al. prepared the first complete map of the entire central region of the fruit fly brain. The central brain consists of approximately 25,000 neurons and around 20 million connections. To prepare the map – or connectome – the brain was cut into very thin 8nm slices and photographed with an electron microscope. A three-dimensional map of the neurons and connections in the brain was then reconstructed from these images using machine learning algorithms. Finally, Scheffer et al. used the new connectome to obtain further insights into the circuits that support specific fruit fly behaviors. The central brain connectome is freely available online for anyone to access. When used in combination with existing methods, the map will make it easier to understand how the fly brain works, and how and why it can fail to work correctly. Many of these findings will likely apply to larger brains, including our own. In the long run, studying the fly connectome may therefore lead to a better understanding of the human brain and its disorders. Performing a similar analysis on the brain of a small mammal, by scaling up the methods here, will be a likely next step along this path.

The neural circuits responsible for behavior remain largely unknown. Previous efforts have reconstructed the complete circuits of small animals, with hundreds of neurons, and selected circuits for larger animals. Here we (the FlyEM project at Janelia and collaborators at Google) summarize new methods and present the complete circuitry of a large fraction of the brain of a much more complex animal, the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses, and proofread such large data sets; new methods that define cell types based on connectivity in addition to morphology; and new methods to simplify access to a large and evolving data set. From the resulting data we derive a better definition of computational compartments and their connections; an exhaustive atlas of cell examples and types, many of them novel; detailed circuits for most of the central brain; and exploration of the statistics and structure of different brain compartments, and the brain as a whole. We make the data public, with a web site and resources specifically designed to make it easy to explore, for all levels of expertise from the expert to the merely curious. The public availability of these data, and the simplified means to access it, dramatically reduces the effort needed to answer typical circuit questions, such as the identity of upstream and downstream neural partners, the circuitry of brain regions, and to link the neurons defined by our analysis with genetic reagents that can be used to study their functions.Note: In the next few weeks, we will release a series of papers with more involved discussions. One paper will detail the hemibrain reconstruction with more extensive analysis and interpretation made possible by this dense connectome. Another paper will explore the central complex, a brain region involved in navigation, motor control, and sleep. A final paper will present insights from the mushroom body, a center of multimodal associative learning in the fly brain.

Deriving the detailed synaptic connections of the entire nervous system has been a long term but unrealized goal of the nascent field of connectomics. For Drosophila, in particular, three sample preparation problems must be solved before the requisite imaging and analysis can even begin. The first is dissecting the brain, connectives, and ventral nerve cord (roughly comparable to the brain, neck, and spinal cord of vertebrates) as a single contiguous unit. Second is fixing and staining the resulting specimen, too large for previous techniques such as flash freezing, so as to permit the necessary automated segmentation of neuron membranes. Finally the contrast must be sufficient to support synapse detection at imaging speeds that enable the entire connectome to be collected. To address these issues, we report three major novel methods to dissect, fix, dehydrate and stain this tiny but complex nervous system in its entirety; together they enable us to uncover a Focused Ion-Beam Scanning Electron Microscopy (FIB-SEM) connectome of the entire Drosophila brain. They reliably recover fixed neurons as round profiles with darkly stained synapses, suitable for machine segmentation and automatic synapse detection, for which only minimal human intervention is required. Our advanced procedures use: a custom-made jig to microdissect both regions of the central nervous system, dorsal and ventral, with their connectives; fixation and Durcupan embedment, followed by a special hot-knife slicing protocol to reduce the brain to dimensions suited to FIB; contrast enhancement by heavy metals; together with a progressive lowering of temperature protocol for dehydration. Collectively these optimize the brain’s morphological preservation, imaging it at a usual resolution of 8nm per voxel while simultaneously speeding the formerly slow rate of FIB-SEM. With these methods we could recently obtain a FIB-SEM image stack of the Drosophila brain eight times faster than hitherto, at approximately the same rate as, but without the requirement to cut, nor imperfections in, EM serial sections.

Understanding the circuit mechanisms behind motion detection is a long-standing question in visual neuroscience. In Drosophila melanogaster, recently discovered synapse-level connectomes in the optic lobe, particularly in ON-pathway (T4) receptive-field circuits, in concert with physiological studies, suggest a motion model that is increasingly intricate when compared with the ubiquitous Hassenstein-Reichardt model. By contrast, our knowledge of OFF-pathway (T5) has been incomplete. Here, we present a conclusive and comprehensive connectome that, for the first time, integrates detailed connectivity information for inputs to both the T4 and T5 pathways in a single EM dataset covering the entire optic lobe. With novel reconstruction methods using automated synapse prediction suited to such a large connectome, we successfully corroborate previous findings in the T4 pathway and comprehensively identify inputs and receptive fields for T5. Although the two pathways are probably evolutionarily linked and exhibit many similarities, we uncover interesting differences and interactions that may underlie their distinct functional properties.

Using FIB-SEM we report the entire synaptic connectome of glomerulus VA1v of the right antennal lobe in Drosophila melanogaster. Within the glomerulus we densely reconstructed all neurons, including hitherto elusive local interneurons. The fruitless-positive, sexually dimorphic VA1v included >11,140 presynaptic sites with ~38,050 postsynaptic dendrites. These connected input olfactory receptor neurons (ORNs, 51 ipsilateral, 56 contralateral), output projection neurons (18 PNs), and local interneurons (56 of >150 previously reported LNs). ORNs are predominantly presynaptic and PNs predominantly postsynaptic; newly reported LN circuits are largely an equal mixture and confer extensive synaptic reciprocity, except the newly reported LN2V with input from ORNs and outputs mostly to monoglomerular PNs, however. PNs were more numerous than previously reported from genetic screens, suggesting that the latter failed to reach saturation. We report a matrix of 192 bodies each having >50 connections; these form 88% of the glomerulus’ pre/postsynaptic sites.

Connectomes derived from volume EM imaging of the brain can generate detailed physical models of every neuron, and simulators such as NEURON or GENESIS are designed to work with such models. In principal, combining these technologies, plus transmitter and channel models, should allow detailed and accurate simulation of real neural circuits. Here we experiment with this combination, using a well-studied system (motion detection in Drosophila). Since simulation requires both the physical geometry (which we have) and the models of the synapses (which are not currently available), we built approximate synapses corresponding to their known and estimated function. Once we did so, we reproduced direction selectivity in T4 cells, one of the main functions of this neural circuit. This verified the basic functionality of both extraction and simulations, and provided a biologically relevant computation we could use in further experiments. We then compared models with different degrees of physical realism, from full detailed models down to models consisting of a single node, to examine the tradeoff of simulation resources required versus accuracy achieved.Our results show that much simpler models may be adequate, at least in the case of medulla neurons in Drosophila. Such models can be easily derived from fully detailed models, and result in simulations that are much smaller, much faster, and accurate enough for many purposes. Biologically, we show that a lumped neuron model reproduces the main motion detector operation, confirming the result of Gruntman[1], that dendritic compution is not required for this function.

Understanding memory formation, storage and retrieval requires knowledge of the underlying neuronal circuits. In Drosophila, the mushroom body (MB) is the major site of associative learning. We reconstructed the morphologies and synaptic connections of all 983 neurons within the three functional units, or compartments, that compose the adult MB’s α lobe, using a dataset of isotropic 8 nm voxels collected by focused ion-beam milling scanning electron microscopy. We found that Kenyon cells (KCs), whose sparse activity encodes sensory information, each make multiple en passant synapses to MB output neurons (MBONs) in each compartment. Some MBONs have inputs from all KCs, while others differentially sample sensory modalities. Only 6% of KC>MBON synapses receive a direct synapse from a dopaminergic neuron (DAN). We identified two unanticipated classes of synapses, KC>DAN and DAN>MBON. DAN activation produces a slow depolarization of the MBON in these DAN>MBON synapses and can weaken memory recall. DOI: http://dx.doi.org/10.7554/eLife.26975.001

Analysing computations in neural circuits often uses simplified models because the actual neuronal implementation is not known. For example, a problem in vision, how the eye detects image motion, has long been analysed using Hassenstein-Reichardt (HR) detector or Barlow-Levick (BL) models. These both simulate motion detection well, but the exact neuronal circuits undertaking these tasks remain elusive. We reconstructed a comprehensive connectome of the circuits of Drosophila‘s motion-sensing T4 cells using a novel EM technique. We uncover complex T4 inputs and reveal that putative excitatory inputs cluster at T4’s dendrite shafts, while inhibitory inputs localize to the bases. Consistent with our previous study, we reveal that Mi1 and Tm3 cells provide most synaptic contacts onto T4. We are, however, unable to reproduce the spatial offset between these cells reported previously. Our comprehensive connectome reveals complex circuits that include candidate anatomical substrates for both HR and BL types of motion detectors.

Animal behaviour arises from computations in neuronal circuits, but our understanding of these computations has been frustrated by the lack of detailed synaptic connection maps, or connectomes. For example, despite intensive investigations over half a century, the neuronal implementation of local motion detection in the insect visual system remains elusive. Here we develop a semi-automated pipeline using electron microscopy to reconstruct a connectome, containing 379 neurons and 8,637 chemical synaptic contacts, within the Drosophila optic medulla. By matching reconstructed neurons to examples from light microscopy, we assigned neurons to cell types and assembled a connectome of the repeating module of the medulla. Within this module, we identified cell types constituting a motion detection circuit, and showed that the connections onto individual motion-sensitive neurons in this circuit were consistent with their direction selectivity. Our results identify cellular targets for future functional investigations, and demonstrate that connectomes can provide key insights into neuronal computations. Reconstruction of a connectome within the fruitfly visual medulla, containing more than 300 neurons and over 8,000 chemical synapses, reveals a candidate motion detection circuit; such a circuit operates by combining displaced visual inputs, an operation consistent with correlation based motion detection. Three papers in this issue of Nature use the retina as a model for mapping neuronal circuits from the level of individual synaptic contacts to the long-range scale of dendritic interactions. Helmstaedter et al. used electron microscopy to map a mammalian retinal circuit of close to a thousand neurons. The work reveals a new type of retinal bipolar neuron and suggests functional mechanisms for known visual computations. The other two groups study the detection of visual motion in the Drosophila visual system — a classic neural computation model. Takemura et al. used semi-automated electron microscopy to reconstruct the basic connectome (8,637 chemical synapses among 379 neurons) of Drosophila's optic medulla. Their results reveal a candidate motion detection circuit with a wiring plan consistent with direction selectivity. Maisak et al. used calcium imaging to show that T4 and T5 neurons are divided into specific subpopulations responding to motion in four cardinal directions, and are specific to 'ON' versus 'OFF' edges, respectively.

We reconstructed the synaptic circuits of seven columns in the second neuropil or medulla behind the fly's compound eye. These neurons embody some of the most stereotyped circuits in one of the most miniaturized of animal brains. The reconstructions allow us, for the first time to our knowledge, to study variations between circuits in the medulla's neighboring columns. This variation in the number of synapses and the types of their synaptic partners has previously been little addressed because methods that visualize multiple circuits have not resolved detailed connections, and existing connectomic studies, which can see such connections, have not so far examined multiple reconstructions of the same circuit. Here, we address the omission by comparing the circuits common to all seven columns to assess variation in their connection strengths and the resultant rates of several different and distinct types of connection error. Error rates reveal that, overall

Synaptic circuits for identified behaviors in the Drosophila brain have typically been considered from either a developmental or functional perspective without reference to how the circuits might have been inherited from ancestral forms. For example, two candidate pathways for ON- and OFF-edge motion detection in the visual system act via circuits that use respectively either T4 or T5, two cell types of the fourth neuropil, or lobula plate, that exhibit narrow-field direction-selective responses and provide input to wide-field tangential neurons. T4 or T5 both have four subtypes that terminate one each in the four strata of the lobula plate. Representatives are reported in a wide range of Diptera, and both cell types exhibit various similarities in: 1) the morphology of their dendritic arbors; 2) their four morphological and functional subtypes; 3) their cholinergic profile in Drosophila; 4) their input from the pathways of L3 cells in the first neuropil, or lamina, and by one of a pair of lamina cells, L1 (to the T4 pathway) and L2 (to the T5 pathway); and 5) their innervation by a single, wide-field contralateral tangential neuron from the central brain. Progenitors of both also express the gene atonal early in their proliferation from the inner anlage of the developing optic lobe, being alone among many other cell type progeny to do so. Yet T4 receives input in the second neuropil, or medulla, and T5 in the third neuropil or lobula. Here we suggest that these two cell types were originally one, that their ancestral cell population duplicated and split to innervate separate medulla and lobula neuropils, and that a fiber crossing – the internal chiasma – arose between the two neuropils. The split most plausibly occurred, we suggest, with the formation of the lobula as a new neuropil that formed when it separated from its ancestral neuropil to leave the medulla, suggesting additionally that medulla input neurons to T4 and T5 may also have had a common origin.

WOS: 000345484000033 PubMed ID: 25362474 Synaptic connectivity and molecular composition provide a blueprint for information processing in neural circuits. Detailed structural analysis of neural circuits requires nanometer resolution, which can be obtained with serial-section electron microscopy. However, this technique remains challenging for reconstructing molecularly defined synapses. We used a genetically encoded synaptic marker for electron microscopy (GESEM) based on intra-vesicular generation of electron-dense labeling in axonal boutons. This approach allowed the identification of synapses from Cre recombinase expressing or GAL4-expressing neurons in the mouse and fly with excellent preservation of ultrastructure. We applied this tool to visualize long-range connectivity of AGRP and POMC neurons in the mouse, two molecularly defined hypothalamic populations that are important for feeding behavior. Combining selective ultrastructural reconstruction of neuropil with functional and viral circuit mapping, we characterized some basic features of circuit organization for axon projections of these cell types. Our findings demonstrate that GESEM labeling enables long-range connectomics with molecularly defined cell types. Howard Hughes Medical Institute We thank A. Wardlaw for mouse breeding and genotyping, K. Morris for HSV stereotaxic injections, A. Hu and M. Copeland for histology, and L. Lo, D. Anderson and R. Gong with advice on HSV129 anterograde tracing. This research was funded by the Howard Hughes Medical Institute. The HSV129 Delta TK-TT anterograde trans-synaptic tracer virus was provided by the Center for Neuroanatomy with Neurotropic Viruses (P40RR018604).