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      A connectome and analysis of the adult Drosophila central brain.

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      eLife
      eLife Sciences Publications, Ltd
      D. melanogaster, brain regions, cell types, computational biology, connectome, connectome reconstuction methods, graph properties, neuroscience, synapse detecton, systems biology

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          Abstract

          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.

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          Most cited references145

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          Basic local alignment search tool.

          A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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            Deep Residual Learning for Image Recognition

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              U-Net: Convolutional Networks for Biomedical Image Segmentation

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                Author and article information

                Journal
                Elife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                2050-084X
                September 07 2020
                : 9
                Affiliations
                [1 ] Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States.
                [2 ] Google Research, Mountain View, United States.
                [3 ] Life Sciences Centre, Dalhousie University, Halifax, Canada.
                [4 ] Google Research, Google LLC, Zurich, Switzerland.
                [5 ] Institute for Quantitative Biosciences, University of Tokyo, Tokyo, Japan.
                [6 ] MRC Laboratory of Molecular Biology, Cambridge, United States.
                [7 ] Institute of Zoology, Biocenter Cologne, University of Cologne, Cologne, Germany.
                [8 ] Department of Zoology, University of Cambridge, Cambridge, United Kingdom.
                Article
                57443
                10.7554/eLife.57443
                7546738
                32880371
                cd6b7a43-ff82-48af-94e7-5a9266f05732
                © 2020, Scheffer et al.
                History

                D. melanogaster,brain regions,cell types,computational biology,connectome,connectome reconstuction methods,graph properties,neuroscience,synapse detecton,systems biology

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