When 3D electron microscopy and calcium imaging are used to investigate the structure and function of neural circuits, the resulting datasets pose new challenges of visualization and interpretation. Here, we present a new kind of digital resource that encompasses almost 400 ganglion cells from a single patch of mouse retina. An online "museum" provides a 3D interactive view of each cell's anatomy, as well as graphs of its visual responses. The resource reveals two aspects of the retina's inner plexiform layer: an arbor segregation principle governing structure along the light axis and a density conservation principle governing structure in the tangential plane. Structure is related to visual function; ganglion cells with arbors near the layer of ganglion cell somas are more sustained in their visual responses on average. Our methods are potentially applicable to dense maps of neuronal anatomy and physiology in other parts of the nervous system.

Due to advances in automated image acquisition and analysis, whole-brain connectomes with 100,000 or more neurons are on the horizon. Proofreading of whole-brain automated reconstructions will require many person-years of effort, due to the huge volumes of data involved. Here we present FlyWire, an online community for proofreading neural circuits in a Drosophila melanogaster brain, and explain how its computational and social structures are organized to scale up to whole-brain connectomics. Browser-based 3D interactive segmentation by collaborative editing of a spatially chunked supervoxel graph makes it possible to distribute proofreading to individuals located virtually anywhere in the world. Information in the edit history is programmatically accessible for a variety of uses such as estimating proofreading accuracy or building incentive systems. An open community accelerates proofreading by recruiting more participants and accelerates scientific discovery by requiring information sharing. We demonstrate how FlyWire enables circuit analysis by reconstructing and analysing the connectome of mechanosensory neurons.

Connections between neurons can be mapped by acquiring and analyzing electron microscopic (EM) brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative, yet inadequate for understanding brain function more globally. Here, we present the first neuronal wiring diagram of a whole adult brain, containing 5x10^7 chemical synapses between ~130,000 neurons reconstructed from a female Drosophila melanogaster. The resource also incorporates annotations of cell classes and types, nerves, hemilineages, and predictions of neurotransmitter identities. Data products are available by download, programmatic access, and interactive browsing and made interoperable with other fly data resources. We show how to derive a projectome, a map of projections between regions, from the connectome. We demonstrate the tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine, and descending neurons), across both hemispheres, and between the central brain and the optic lobes. Tracing from a subset of photoreceptors all the way to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviors. The technologies and open ecosystem of the FlyWire Consortium set the stage for future large-scale connectome projects in other species.

Learning from experience depends at least in part on changes in neuronal connections. We present the largest map of connectivity to date between cortical neurons of a defined type (L2/3 pyramidal cells), which was enabled by automated analysis of serial section electron microscopy images with improved handling of image defects. We used the map to identify constraints on the learning algorithms employed by the cortex. Previous cortical studies modeled a continuum of synapse sizes (Arellano et al. , 2007) by a log-normal distribution (Loewenstein, Kuras and Rumpel, 2011;de Vivo et al. , 2017;Santuy et al. , 2018) . A continuum is consistent with most neural network models of learning, in which synaptic strength is a continuously graded analog variable. Here we show that synapse size, when restricted to synapses between L2/3 pyramidal cells, is well-modeled by the sum of a binary variable and an analog variable drawn from a log-normal distribution. Two synapses sharing the same presynaptic and postsynaptic cells are known to be correlated in size (Sorra and Harris, 1993;Koester and Johnston, 2005;Bartol et al. , 2015;Kasthuri et al. , 2015;Dvorkin and Ziv, 2016;Bloss et al. , 2018;Motta et al. , 2019) . We show that the binary variables of the two synapses are highly correlated, while the analog variables are not. Binary variation could be the outcome of a Hebbian or other synaptic plasticity rule depending on activity signals that are relatively uniform across neuronal arbors, while analog variation may be dominated by other influences. We discuss the implications for the stability-plasticity dilemma. †Correspondence to svenmd@princeton.edu ,

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Most digital brain atlases have macroscopic resolution and are confined to a single imaging modality. Here we present a new kind of resource that combines dense maps of anatomy and physiology at cellular resolution. The resource encompasses 5 almost 400 ganglion cells from a single patch of mouse retina, and a digital "museum" provides a 3D interactive view of each cell's anatomy as well as graphs of its visual responses. To demonstrate the utility of the resource, we use it to divide the inner plexiform layer of the retina into four sublaminae defined 10 by a purely anatomical principle of arbor segregation. We also test the hypothesis that the aggregate neurite density of a ganglion cell type should be approximately uniform ("density conservation"). Finally, we find that ganglion cells arborizing in the inner marginal sublamina of the inner plexiform layer ex- 15hibit significantly more sustained visual responses on average.Calcium imaging followed by 3D electron microscopy (EM) has become established as a powerful approach for obtaining anatomical and physiological information about the same neurons. The approach has been used to study visual neurons in mouse cortex 20 [Bock et al., 2011, Lee et al., 2016 and retina [Briggman et al., 2011], and oculomotor neurons in larval zebrafish hindbrain [Vishwanathan et al., 2017]. These prior studies have been limited to tens of neurons sparsely sampled from a single individual. Here we present as a resource the anatomy and physiology of almost 25 400 ganglion cells (GCs) densely sampled from a single patch of mouse retina.To facilitate exploratory data analysis using the resource, we have constructed the Eyewire Museum (http://museum. eyewire.org), where every reconstructed ganglion cell can be in-30 teractively viewed along with its visual response properties. Due to its cellular and subcellular resolution, the Museum is novel relative to traditional brain atlases, which typically divide the brain into macroscopic regions [Lein et al., 2007, Amunts et al., 2013, Zingg et al., 2014. Because it fuses anatomy with physiology, 35 the Museum is also novel relative to traditional atlases of neuronal morphologies such as neuromorpho.org [Ascoli et al., 2007] and wormatlas.org [Hall et al., 2007]. Previous dense EM reconstructions in the mouse retina [Helmstaedter et al., 2013] and larval zebrafish olfactory bulb [Wanner et al., 2016] were limited to 40 anatomy only. A recent large-scale calcium imaging study contains the visual responses of more than 11,000 neurons from 50 retinas, but less than 1% of the cells had their dendritic arbors reconstructed [Baden et al., 2016].To illustrate the utility of our resource, we use it to reveal new 45 principles of retinal organization. First, we show how to optimally subdivide the inner plexiform layer (IPL) of the retina using the purely anatomical principle that arbors should segregate into distinct sublamina. For GC dendritic arbors, segregation is maximized by subdividing the IPL into two marginal sublamina flanking a...

Sustained changes in mood or action require persistent changes in neural activity, but it has been difficult to identify the neural circuit mechanisms that underlie persistent activity and contribute to long-lasting changes in behavior. Here, we show that a subset of Doublesex+ pC1 neurons in the Drosophila female brain, called pC1d/e, can drive minutes-long changes in female behavior in the presence of males. Using automated reconstruction of a volume electron microscopic (EM) image of the female brain, we map all inputs and outputs to both pC1d and pC1e. This reveals strong recurrent connectivity between, in particular, pC1d/e neurons and a specific subset of Fruitless+ neurons called aIPg. We additionally find that pC1d/e activation drives long-lasting persistent neural activity in brain areas and cells overlapping with the pC1d/e neural network, including both Doublesex+ and Fruitless+ neurons. Our work thus links minutes-long persistent changes in behavior with persistent neural activity and recurrent circuit architecture in the female brain.

Like the vertebrate spinal cord, the insect ventral nerve cord (VNC) mediates limb sensation and motor control. Here, we applied automated tools for electron microscopy (EM) volume alignment, neuron reconstruction, and synapse prediction to create a draft connectome of theDrosophilaVNC. To interpret the VNC connectome, it is crucial to know its relationship with the rest of the body. We therefore mapped the muscle targets of leg and wing motor neurons in the connectome by comparing their morphology to genetic driver lines, dye fills, and x-ray holographic nano-tomography volumes of the fly leg and wing. Knowing the outputs of the connectome allowed us to identify neural circuits that coordinate the wings with the middle and front legs during escape takeoff. We provide the draft VNC connectome and motor neuron atlas, along with tools for programmatic and interactive access, as community resources.