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Neuroscience research in Drosophila is benefiting from large-scale connectomics efforts using electron microscopy (EM) to reveal all the neurons in a brain and their connections. In order to exploit this knowledge base, researchers target individual neurons and study their function. Therefore, vast libraries of fly driver lines expressing fluorescent reporter genes in sets of neurons have been created and imaged using confocal light microscopy (LM). However, creating a fly line for driving gene expression within a single neuron found in the EM connectome remains a challenge, as it typically requires identifying a pair of fly lines where only the neuron of interest is expressed in both. This task and other emerging scientific workflows require finding similar neurons across large data sets imaged using different modalities. Here, we present NeuronBridge, a web application for easily and rapidly finding putative morphological matches between large datasets of neurons imaged using different modalities. We describe the functionality and construction of the NeuronBridge service, including its user-friendly GUI, data model, serverless cloud architecture, and massively parallel image search engine. NeuronBridge is openly accessible at http://neuronbridge.janelia.org/.

Many animals rely on a representation of head direction for flexible, goal-directed navigation. In insects, a compass-like head direction representation is maintained in a conserved brain region called the central complex. This head direction representation is updated by self-motion information and by tethering to sensory cues in the surroundings through a plasticity mechanism. However, under natural settings, some of these sensory cues may temporarily disappear—for example, when clouds hide the sun—and prominent landmarks at different distances from the insect may move across the animal’s field of view during translation, creating potential conflicts for a neural compass. We used two-photon calcium imaging in head-fixed Drosophila behaving in virtual reality to monitor the fly’s compass during navigation in immersive naturalistic environments with approachable local landmarks. We found that the fly’s compass remains stable even in these settings by tethering to available global cues, likely preserving the animal’s ability to perform compass-driven behaviors such as maintaining a constant heading.

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 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.

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.

Recent advances in automatic image segmentation and synapse prediction in electron microscopy (EM) datasets of the brain enable more efficient reconstruction of neural connectivity. In these datasets, a single neuron can span thousands of images containing complex tree-like arbors with thousands of synapses. While image segmentation algorithms excel within narrow fields of views, the algorithms sometimes struggle to correctly segment large neurons, which require large context given their size and complexity. Conversely, humans are comparatively good at reasoning with large objects. In this paper, we introduce several semi-automated strategies that combine 3D visualization and machine guidance to accelerate connectome reconstruction. In particular, we introduce a strategy to quickly correct a segmentation through merging and cleaving, or splitting a segment along supervoxel boundaries, with both operations driven by affinity scores in the underlying segmentation. We deploy these algorithms as streamlined workflows in a tool called Neu3 and demonstrate superior performance compared to prior work, thus enabling efficient reconstruction of much larger datasets. The insights into proofreading from our work clarify the trade-offs to consider when tuning the parameters of image segmentation algorithms.

A major goal in computer graphics is realistic image synthesis. To this end, illumination methods have evolved from simple local shading models to physically based global illumination algorithms. Local illumination methods consider only the light energy transfer between an emitter and a surface (direct lighting), while global methods account for light energy interactions between all surfaces in an environment, considering both direct and indirect lighting. Even though the realistic effects that global illumination algorithms provide are frequently desirable, the computational expense of these methods is too great for many applications. Dynamic environments and scenes containing a very large number of surfaces often pose problems for global illumination methods. This article presents a different approach to calculating the global illumination of objects. Instead of striving for accuracy at the expense of performance, we rephrase the goal: to achieve a reasonable approximation with high performance. This places global illumination effects within reach of many applications in which visual appearance is more important than absolute numerical accuracy.

This article presents the density estimation framework for generating view-independent global illumination solutions. It works by probabilistically simulating the light flow in an environment with light particles that trace random walks origination at luminaires and then using statistical density estimation techniques to reconstruct the lighting on each surface. By splitting the computation into separate transport and reconstruction stages, we gain many advantages including reduced memory usage, the ability to simulate nondiffuse transport, and natural parallelism. Solutions to several theoretical and practical difficulties in implementing this framework are also described. Light sources that vary spectrally and directionally are integrated into a spectral particle tracer using nonuniform rejection. A new local linear density estimation technique eliminates boundary bias and extends to arbitrary polygons. A mesh decimation algorithm with perceptual calibration is introduced to simplify the Gouraud-shaded representation of the solution for interactive display.

The Collaboratory for Microscopic Digital Anatomy (CMDA) will allow biologists at remote locations to acquire over the network three-dimensional data on biological structure using a sophisticated high-voltage electron mi croscope (HVEM) located in San Diego. The CMDA will transparently distribute to high-performance machines on the network computationally intensive tasks for the deriva tion of three-dimensional datasets from HVEM images using tomography and for volume visualization. Implemen tation of the CMDA includes development of automated microscope functions for specimen positioning, image fo cus, registration, and acquisition; an asynchronous com munications system for transporting messages and large data sets between processes; a resource manager for access control and routing of tasks to computing or acqui sition resources; and systems for tomographic reconstruc tion and three-dimensional volume visualization. The CMDA is designed to be extensible to other unique cen tralized instruments. It will increase access to and use of these instruments and high-performance computers by biologists and facilitate remote collaboration.

We present an interactive modeling and animation system that facilitates the integration of a variety of simulation and animation paradigms. This system permits the modeling of diverse objects that change in shape, appearance, and behaviour over time. Our system thus extends modeling tools to include animation controls. Changes can be effected by various methods of control, including scripted, gestural, and behavioral specification. The system is an extensible testbed that supports research in the interaction of disparate control methods embodied in controller objects. This paper discusses some of the issues involved in modeling such interactions and the mechanisms implemented to provide solutions to some of these issues.The system's object-oriented architecture uses delegation hierarchies to let objects change all of their attributes dynamically. Objects include displayable objects, controllers, cameras, lights, renderers, and user interfaces. Techniques used to obtain interactive performance include the use of data-dependency networks, lazy evaluation, and extensive caching to exploit inter- and intra-frame coherency.

This paper presents a new multimedia data type based on time-varying light fields. Such a light field movie may be precomputed using computer graphics, or photographically captured from real objects as they change over time. Such movies allow a user to experience motion parallax in response to head motion in addition to motion cues within the scene as well as stereo vision cues. Important elements to make such media practical include light field movie capture devices, effective compression schemes with real-time playback, and accurate head tracking. This paper addresses these areas and presents results from the current system as well as directions for future work.

This paper presents a new method for the production of view-independent global illumi-nation solutions of complex static environments. A key innovation of this new approach is its decomposition of the problem into a loosely coupled sequence of simple modules. This approach decouples the global energy transport computation from the construction of the displayable shaded representation of the environment. This decoupling eliminates many constraints of previous global illumination approaches, yielding accurate solutions for environments with non-diffuse surfaces and high geometric complexity.