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We can now measure the connectivity of every neuron in a neural circuit, but we are still blind to other biological details, including the dynamical characteristics of each neuron. The degree to which connectivity measurements alone can inform understanding of neural computation is an open question. Here we show that with only measurements of the connectivity of a biological neural network, we can predict the neural activity underlying neural computation. We constructed a model neural network with the experimentally determined connectivity for 64 cell types in the motion pathways of the fruit fly optic lobe but with unknown parameters for the single neuron and single synapse properties. We then optimized the values of these unknown parameters using techniques from deep learning, to allow the model network to detect visual motion. Our mechanistic model makes detailed experimentally testable predictions for each neuron in the connectome. We found that model predictions agreed with experimental measurements of neural activity across 24 studies. Our work demonstrates a strategy for generating detailed hypotheses about the mechanisms of neural circuit function from connectivity measurements. We show that this strategy is more likely to be successful when neurons are sparsely connected—a universally observed feature of biological neural networks across species and brain regions.

Deriving the detailed synaptic connections of an entire nervous system is the unrealized goal of the nascent field of connectomics. For the fruit fly Drosophila, in particular, we need to dissect the brain, connectives, and ventral nerve cord as a single continuous unit, fix and stain it, and undertake automated segmentation of neuron membranes. To achieve this, we designed a protocol using progressive lowering of temperature dehydration (PLT), a technique routinely used to preserve cellular structure and antigenicity. We combined PLT with low temperature en bloc staining (LTS) and recover fixed neurons as round profiles with darkly stained synapses, suitable for machine segmentation and automatic synapse detection. Here we report three different PLT-LTS methods designed to meet the requirements for FIB-SEM imaging of the Drosophila brain. These requirements include: good preservation of ultrastructural detail, high level of en bloc staining, artifact-free microdissection, and smooth hot-knife cutting to reduce the brain to dimensions suited to FIB-SEM. In addition to PLT-LTS, we designed a jig to microdissect and pre-fix the fly’s delicate brain and central nervous system. Collectively these methods optimize morphological preservation, allow us to image the brain usually at 8 nm per voxel, and simultaneously speed the formerly slow rate of FIB-SEM imaging.

SummaryThe detection of visual motion enables sophisticated animal navigation, and studies in flies have provided profound insights into the cellular and circuit basis of this neural computation. The fly’s directionally selective T4 and T5 neurons respectively encode ON and OFF motion. Their axons terminate in one of four retinotopic layers in the lobula plate, where each layer encodes one of four cardinal directions of motion. While the input circuitry of the directionally selective neurons has been studied in detail, the synaptic connectivity of circuits integrating T4/T5 motion signals is largely unknown. Here we report a 3D electron microscopy reconstruction, wherein we comprehensively identified T4/T5’s synaptic partners in the lobula plate, revealing a diverse set of new cell types and attributing new connectivity patterns to known cell types. Our reconstruction explains how the ON and OFF motion pathways converge. T4 and T5 cells that project to the same layer, connect to common synaptic partners symmetrically, that is with similar weights, and also comprise a core motif together with bilayer interneurons, detailing the circuit basis for computing motion opponency. We discovered pathways that likely encode new directions of motion by integrating vertical and horizontal motion signals from upstream T4/T5 neurons. Finally, we identify substantial projections into the lobula, extending the known motion pathways and suggesting that directionally selective signals shape feature detection there. The circuits we describe enrich the anatomical basis for experimental and computations analyses of motion vision and bring us closer to understanding complete sensory-motor pathways.

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.

In Drosophila, color vision and wavelength-selective behaviors are mediated by the compound eye's narrow-spectrum photoreceptors R7 and R8 and their downstream medulla projection (Tm) neurons Tm5a, Tm5b, Tm5c, and Tm20 in the second optic neuropil or medulla. These chromatic Tm neurons project axons to a deeper optic neuropil, the lobula, which in insects has been implicated in processing and relaying color information to the central brain. The synaptic targets of the chromatic Tm neurons in the lobula are not known, however. Using a modified GFP reconstitution across synaptic partners (GRASP) method to probe connections between the chromatic Tm neurons and 28 known and novel types of lobula neurons, we identify anatomically the visual projection neurons LT11 and LC14 and the lobula intrinsic neurons Li3 and Li4 as synaptic targets of the chromatic Tm neurons. Single-cell GRASP analyses reveal that Li4 receives synaptic contacts from over 90% of all four types of chromatic Tm neurons, whereas LT11 is postsynaptic to the chromatic Tm neurons, with only modest selectivity and at a lower frequency and density. To visualize synaptic contacts at the ultrastructural level, we develop and apply a "two-tag" double-labeling method to label LT11's dendrites and the mitochondria in Tm5c's presynaptic terminals. Serial electron microscopic reconstruction confirms that LT11 receives direct contacts from Tm5c. This method would be generally applicable to map the connections of large complex neurons in Drosophila and other animals.

Summary Background During development neurons are generated by sequential divisions of neural stem cells, or neuroblasts. In the insect brain progeny of certain stem cells form lineage-specific sets of projections that arborize in distinct brain regions, called clonal units. Though this raises the possibility that the entire neural network in the brain might be organized in a clone-dependent fashion, only a small portion of clones has been identified. Results Using Drosophila melanogaster , we randomly labeled one of about 100 stem cells at the beginning of the larval stage, analyzed the projection patterns of their progeny in the adult, and identified 96 clonal units in the central part of the fly brain, the cerebrum. Neurons of all the clones arborize in distinct regions of the brain, though many clones feature heterogeneous groups of neurons in terms of their projection patterns and neurotransmitters. Arborizations of clones overlap preferentially to form several groups of closely associated clones. Fascicles and commissures were all made by unique sets of clones. Whereas well-investigated brain regions such as the mushroom body and central complex consist of relatively small numbers of clones and are specifically connected with a limited number of neuropils, seemingly disorganized neuropils surrounding them are composed by a much larger number of clones and have extensive specific connections with many other neuropils. Conclusions Our study showed that the insect brain is formed by a composition of cell-lineage-dependent modules. Clonal analysis reveals organized architecture even in those neuropils without obvious structural landmarks.

The brain of fruit fly Drosophila melanogaster has been used as a model system for functional analysis of neuronal circuits, including connectomics research, due to its modest size (~700 μm) and availability of abundant molecular genetics tools for visualizing neurons. Three-dimensional (3D) reconstruction of high-resolution images of neurons or circuits visualized with appropriate methods is a critical step for obtaining information such as morphology and connectivity patterns of neuronal circuits. In this chapter, we introduce methods for generating 3D reconstructed images with data acquired from confocal laser scanning microscopy (CLSM) or electron microscopy (EM) to analyze neuronal circuits found in the central nervous system (CNS) of the fruit fly. Comparisons of different algorithms and strategies for reconstructing neuronal circuits, using actual studies as references, will be discussed within this chapter.

The long history of neuroscience has accumulated information about numerous types of neurons in the brain of various organisms. Because such neurons have been reported in diverse publications without controlled format, it is not easy to keep track of all the known neurons in a particular nervous system. To address this issue we constructed an online database called Flybrain Neuron Database (Flybrain NDB), which serves as a platform to collect and provide information about all the types of neurons published so far in the brain of Drosophila melanogaster. Projection patterns of the identified neurons in diverse areas of the brain were recorded in a unified format, with text-based descriptions as well as images and movies wherever possible. In some cases projection sites and the distribution of the post- and presynaptic sites were determined with greater detail than described in the original publication. Information about the labeling patterns of various antibodies and expression driver strains to visualize identified neurons are provided as a separate sub-database. We also implemented a novel visualization tool with which users can interactively examine three-dimensional reconstruction of the confocal serial section images with desired viewing angles and cross sections. Comprehensive collection and versatile search function of the anatomical information reported in diverse publications make it possible to analyze possible connectivity between different brain regions. We analyzed the preferential connectivity among optic lobe layers and the plausible olfactory sensory map in the lateral horn to show the usefulness of such a database.

In Drosophila, color vision and wavelength-selective behaviors are mediated by the compound eye's narrow-spectrum photoreceptors R7 and R8 and their downstream medulla projection (Tm) neurons Tm5a, Tm5b, Tm5c, and Tm20 in the second optic neuropil or medulla. These chromatic Tm neurons project axons to a deeper optic neuropil, the lobula, which in insects has been implicated in processing and relaying color information to the central brain. The synaptic targets of the chromatic Tm neurons in the lobula are not known, however. Using a modified GFP reconstitution across synaptic partners (GRASP) method to probe connections between the chromatic Tm neurons and 28 known and novel types of lobula neurons, we identify anatomically the visual projection neurons LT11 and LC14 and the lobula intrinsic neurons Li3 and Li4 as synaptic targets of the chromatic Tm neurons. Single-cell GRASP analyses reveal that Li4 receives synaptic contacts from over 90% of all four types of chromatic Tm neurons, whereas LT11 is postsynaptic to the chromatic Tm neurons, with only modest selectivity and at a lower frequency and density. To visualize synaptic contacts at the ultrastructural level, we develop and apply a "two-tag" double-labeling method to label LT11's dendrites and the mitochondria in Tm5c's presynaptic terminals. Serial electron microscopic reconstruction confirms that LT11 receives direct contacts from Tm5c. This method would be generally applicable to map the connections of large complex neurons in Drosophila and other animals.

Compared with connections between the retinae and primary visual centers, relatively less is known in both mammals and insects about the functional segregation of neural pathways connecting primary and higher centers of the visual processing cascade. Here, using the Drosophila visual system as a model, we demonstrate two levels of parallel computation in the pathways that connect primary visual centers of the optic lobe to computational circuits embedded within deeper centers in the central brain. We show that a seemingly simple achromatic behavior, namely phototaxis, is under the control of several independent pathways, each of which is responsible for navigation towards unique wavelengths. Silencing just one pathway is enough to disturb phototaxis towards one characteristic monochromatic source, whereas phototactic behavior towards white light is not affected. The response spectrum of each demonstrable pathway is different from that of individual photoreceptors, suggesting subtractive computations. A choice assay between two colors showed that these pathways are responsible for navigation towards, but not for the detection itself of, the monochromatic light. The present study provides novel insights about how visual information is separated and processed in parallel to achieve robust control of an innate behavior.