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.

:Reconstruction of neural circuits from volume electron microscopy data requires the tracing of complete cells including all their neurites. Automated approaches have been developed to perform the tracing, but without costly human proofreading their error rates are too high to obtain reliable circuit diagrams. We present a method for automated segmentation that, like the majority of previous efforts, employs convolutional neural networks, but contains in addition a recurrent pathway that allows the iterative optimization and extension of the reconstructed shape of individual neural processes. We used this technique, which we call flood-filling networks, to trace neurons in a data set obtained by serial block-face electron microscopy from a male zebra finch brain. Our method achieved a mean error-free neurite path length of 1.1 mm, an order of magnitude better than previously published approaches applied to the same dataset. Only 4 mergers were observed in a neurite test set of 97 mm path length.

Reconstruction of neural circuits from volume electron microscopy data requires the tracing of cells in their entirety, including all their neurites. Automated approaches have been developed for tracing, but their error rates are too high to generate reliable circuit diagrams without extensive human proofreading. We present flood-filling networks, a method for automated segmentation that, similar to most previous efforts, uses convolutional neural networks, but contains in addition a recurrent pathway that allows the iterative optimization and extension of individual neuronal processes. We used flood-filling networks to trace neurons in a dataset obtained by serial block-face electron microscopy of a zebra finch brain. Using our method, we achieved a mean error-free neurite path length of 1.1 mm, and we observed only four mergers in a test set with a path length of 97 mm. The performance of flood-filling networks was an order of magnitude better than that of previous approaches applied to this dataset, although with substantially increased computational costs.

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...

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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.

The function of a neural circuit is shaped by the computations performed by its interneurons, which in many cases are not easily accessible to experimental investigation. Here, we elucidate the transformation of visual signals flowing from the input to the output of the primate retina, using a combination of large-scale multi-electrode recordings from an identified ganglion cell type, visual stimulation targeted at individual cone photoreceptors, and a hierarchical computational model. The results reveal nonlinear subunits in the circuity of OFF midget ganglion cells, which subserve high-resolution vision. The model explains light responses to a variety of stimuli more accurately than a linear model, including stimuli targeted to cones within and across subunits. The recovered model components are consistent with known anatomical organization of midget bipolar interneurons. These results reveal the spatial structure of linear and nonlinear encoding, at the resolution of single cells and at the scale of complete circuits.DOI: http://dx.doi.org/10.7554/eLife.05241.001

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