Biology subjects

Seung, H. S.

Publications and source records attributed to Seung, H. S..

At least 19 recordsLinked to original sources

bioRxiv2026

Uncovering Sex Differences in the Drosophila Ventral Nerve Cord Through Connectome Alignment

There are now multiple Drosophila connectomes available, comprising both its brain and spinal cord - comparing these connectomes requires first classifying neurons into cell types. Existing approaches for cell typing typically require extensive manual curation. Here we present a new method that automatically aligns connectomes via network topology alone. Using two complete male ventral nerve cord (VNC) connectomes as references, we assign cell types to [~]13,000 neurons intrinsic to the female VNC, and automatically identify sex-specific and sexually dimorphic cell types. We not only provide a comprehensive census of cell types across male and female nerve cords, but we uncover connectivity differences that underlie differences in function. We focus on circuits underlying song production in males and oviposition behaviors in females, and investigate the counterparts of these circuits across sexes. Our automated methods and analyses provide insights into sex differences in circuits that connect the brain and body, and pave the way for comparative connectomics at scale.

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bioRxiv2026

Eyewire II - A connectomic resource for resolving cell types and circuits of the mouse retina

Comprehensive wiring diagrams from electron microscopy (EM) are a powerful tool to understand the inner workings of the brain. The retina is an easily accessible part of the brain that performs complex visual computations. Its thin, layered structure offers a unique opportunity to decipher neural cell types and map their connectivity. A major obstacle has been the limited size of existing retinal EM datasets, which could not resolve rare cell types and neurons with large dendritic arbors. Here, we describe Eyewire II, a large-scale EM dataset covering nearly 1 mm2 of the adult mouse retina - roughly 10-100 times larger than previous retinal EM volumes. Human proofreading of an automated reconstruction has so far yielded more than 8, 000 bipolar cells, 13, 000 amacrine cells, and 4, 000 retinal ganglion cells. Automated detection is complete for synaptic ribbons and in progress for conventional synapses. Prior to EM imaging, visual responses to diverse stimuli - including natural movies - were recorded in a subset of neurons using two-photon Ca2+ imaging, enabling direct alignment of morphological and functional cell type identity. To enable high-throughput, automatic cell typing, we devised a human-in-the-loop approach that combines deep learning with human expert annotations. As a proof-of-principle, we show that morphological features of reconstructed bipolar cells are sufficient to recover all 15 known bipolar cell types with regular, non-overlapping mosaics. Together, these data and tools establish Eyewire II as a shared resource for the field of retina research. Already now, more than 30 laboratories worldwide are contributing proofreading, expert annotations, and software tools, advancing Eyewire II towards a complete cell type catalog and synaptic wiring diagram of a mammalian retina.

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bioRxiv2025

New Synapse Detection in the Whole-Brain Connectome of Drosophila

The FlyWire Drosophila brain connectome1 is a graph of roughly 140K neurons and >50 million synaptic connections2,3 reconstructed from the FAFB EM dataset4. Challenges in synapse detection were identified for neurons with features such as dark cytosols, axo-axonic synapses, and complex polyadic synapses, due to limitations in ground truth data for these cells and the inherent complexity of these synapse types. To address these issues, we trained new neural networks using iteratively generated ground truth annotations and detected synapses across the entire FAFB dataset, producing what we refer to here as the Princeton synapses. These synapses were evaluated in both control regions, such as subareas of the mushroom body calyx and lateral horn, which were also chosen by Buhmann et al.3 for evaluation, as well as challenging regions, including Johnstons Organ neurons (JONs), photoreceptors, and other cell types. The new model shows significant improvements, achieving up to a 0.23 F-score increase in challenging areas, while maintaining performance in control regions. Princeton synapses also show an 8-9% improvement in neuron clustering within cell types and better left/right symmetry scores, especially for photoreceptors. Additionally, neuron type membership can be predicted from connectivity patterns alone with weighted F-scores of 0.93 for Princeton synapses versus 0.91 for Buhmann synapses. The updated Princeton synapses are now accessible via Codex (codex.flywire.ai).

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bioRxiv2025

Connectomic reconstruction from hippocampal CA3 reveals spatially graded mossy fiber inputs and selective feedforward inhibition to pyramidal cells

The mossy fiber (MF) connections to pyramidal cells in hippocampal CA3 are hypothesized to participate in pattern separation and memory encoding, yet no large-scale neuronal wiring diagram exists for these connections. We assembled a 3D electron microscopy volume ([~]1x1x0.1mm3) from mouse hippocampal CA3. By proofreading and automated segmentation, we reconstructed and classified all soma-containing neurons--including 1,815 pyramidal cells and 229 inhibitory cells--and over 55,000 MFs. Pyramidal cells receive more numerous MF inputs along a proximodistal gradient. Some distal cells show surprisingly high convergence via relatively small terminals with fewer vesicles. Pyramidal cells share significantly more MF inputs than networks randomized by degree-preserving swap, and are better approximated by networks randomized by proximity-preserving swap. We identify a feedforward inhibitory circuit from MFs via perisomatic interneurons that selectively target a pyramidal subtype. We demonstrated large-scale mapping across levels in the hippocampus--from circuits to cell types to vesicles. The dataset is shared through Pyr, an online platform for hippocampal connectomics.

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bioRxiv2025

Sexually-dimorphic neurons in the Drosophila whole-brain connectome

Sexual dimorphisms in the brain arise from differences in cell number, morphology, and connectivity, and underlie sex-specific behaviors. In Drosophila, these differences are largely governed by the transcription factors Fruitless (Fru) and Doublesex (Dsx), expressed in neurons implicated in reproductive and social behaviors. However, how these neurons are integrated into the connectome to drive different behaviors in different sexes remains unclear. Here, we identify 98 putative Fru- or Dsx-expressing cell types ([~]1700 neurons) in the female whole-brain connectome by matching electron microscopy reconstructions to light microscopy images. We then analyze their connectivity within the whole-brain network. Fru/Dsx neurons are strongly interconnected, but are not part of a private network. We measure distances to the sensory periphery to examine multisensory interactions, and map connections to descending neurons that drive behavior. We further cluster Fru/Dsx neurons based on whole-brain connectivity, and uncover functional groupings. We analyze a subset of strongly connected Fru/Dsx neurons to make functional predictions, and test some of these with experiments. Together, this work provides a brain-wide framework for understanding the organization of sexually dimorphic circuits and establishes a resource for dissecting the neural basis of social and sexual behaviors.

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bioRxiv2025

Multi-output computation by single neuron biophysics in a visual system

As long anticipated (Sandberg and Bostrom 2008; Seung 2012; Szigeti et al. 2014), connectomics is providing a new foundation for brain simulation by replacing theoretical assumptions about network connectivity with solid empirical facts. Connectomics also yields detailed information about neuronal morphology, which is useful for simulating the biophysics of single neurons (Yang et al. 2016; Meier and Borst 2019; T. X. Liu et al. 2022). Here I introduce a formalism for simulating a brain as a network of synapses interacting via an effective resistance matrix. By computing this matrix for fly visual interneurons, I find evidence that some neurons may be true multi-output devices, neither well approximated as "point neurons" (Lappalainen et al. 2024; Shiu et al. 2024), nor as collections of functionally independent compartments (Meier and Borst 2019). Within a linear approximation, such a neuron is instead equivalent to a hierarchy of virtual neurons that spatially pool over multiple length scales. The computational powers of multi-output neurons may support highly sophisticated normalizations in the fly visual system (Seung 2024a).

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bioRxiv2024

Interneuron diversity and normalization specificity in a visual system

Normalization is a fundamental operation in image processing. Convolutional nets have evolved to include a large number of normalizations (Ioffe and Szegedy 2015; Ulyanov, Vedaldi, and Lempitsky 2016; Wu and He 2018), and this architectural shift has proved essential for robust computer vision (He et al. 2015; Bjorck et al. 2018; Santurkar, Tsipras, and Ilyas 2018). Studies of biological vision, in contrast, have invoked just one or a few normalizations to model psychophysical (Mach 1868; Furman 1965; Sperling 1970) and physiological (Carandini and Heeger 2011; Shin and Adesnik 2024) observations that have accumulated for over a century. Here connectomic information (Matsliah et al. 2023) is used to argue that interneurons of the fly visual system support a large number of normalizations with unprecedented specificity. Ten interneuron types in the distal medulla (Dm) of the fly optic lobe, for example, appear to support chiefly spatial normalizations, each of which is specific to a single cell type and length scale. Another Dm type supports normalization over features as well as space. Two outlier types do not appear to support normalization at all. Interneuron types likely to be normalizers are identified not only in Dm but also in all other interneuron families of the optic lobe. For fly vision, the diversity of interneurons appears to be an inevitable consequence of the specificity of normalizations.

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bioRxiv2023

Insights into vision from interpretation of a neuronal wiring diagram

What insects can see has been probed by over a century of behavioral experiments. Motion and color vision have also been studied through neurophysiology in insect brains. Here I study form vision by interpreting a neuronal wiring diagram of the Drosophila optic lobe. The Dm3 "line amacrine" cells are shown to divide into three cell types with oriented dendrites, and to be connected with three TmY cell types, also with oriented dendrites. All six cell types are predicted to respond selectively to oriented visual stimuli, with preferred orientation defined by dendrite orientation. Their receptive fields are predicted by mapping input from other cell types that chiefly convey information from single facets of the compound eye. Dm3 to Dm3 and TmY connectivity is approximated by cross-orientation inhibition and TmY to TmY connectivity by iso-orientation excitation. Both connectivity motifs were previously hypothesized for mammalian visual cortex. Two of the TmY types target a novel type of LC10 cell, which leads by multiple pathways to brain regions that support learning of visual form. Based on the spatial organization of TmY to TmY and LC10 connectivity, I conjecture that flies may see illusory contours and corners.

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bioRxiv2023

Neuronal "parts list" and wiring diagram for a visual system

A catalog of neuronal cell types has often been called a "parts list" of the brain, and regarded as a prerequisite for understanding brain function. In the optic lobe of Drosophila, rules of connectivity between cell types have already proven essential for understanding fly vision. Here we analyze the fly connectome to complete the list of cell types intrinsic to the optic lobe, as well as the rules governing their connectivity. We more than double the list of known types. Most new cell types contain between 10 and 100 cells, and integrate information over medium distances in the visual field. Some existing type families (Tm, Li, and LPi) at least double in number of types. We introduce a new Sm interneuron family, which contains more types than any other, and three new families of cross-neuropil types. Self-consistency of cell types is demonstrated through automatic assignment of cells to types by distance in high-dimensional feature space, and further validation is provided by algorithms that select small subsets of discriminative features. Cell types with similar connectivity patterns divide into clusters that are interpretable in terms of motion, object, and color vision. Our work showcases the advantages of connectomic cell typing: complete and unbiased sampling, a rich array of features based on connectivity, and reduction of the connectome to a drastically simpler wiring diagram of cell types, with immediate relevance for brain function and development.

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bioRxiv2023

CAVE: Connectome Annotation Versioning Engine

Advances in Electron Microscopy, image segmentation and computational infrastructure have given rise to large-scale and richly annotated connectomic datasets which are increasingly shared across communities. To enable collaboration, users need to be able to concurrently create new annotations and correct errors in the automated segmentation by proofreading. In large datasets, every proofreading edit relabels cell identities of millions of voxels and thousands of annotations like synapses. For analysis, users require immediate and reproducible access to this constantly changing and expanding data landscape. Here, we present the Connectome Annotation Versioning Engine (CAVE), a computational infrastructure for immediate and reproducible connectome analysis in up-to petascale datasets ([~]1mm3) while proofreading and annotating is ongoing. For segmentation, CAVE provides a distributed proofreading infrastructure for continuous versioning of large reconstructions. Annotations in CAVE are defined by locations such that they can be quickly assigned to the underlying segment which enables fast analysis queries of CAVEs data for arbitrary time points. CAVE supports schematized, extensible annotations, so that researchers can readily design novel annotation types. CAVE is already used for many connectomics datasets, including the largest datasets available to date.

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bioRxiv2023

Neuronal wiring diagram of an adult brain

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 5x107 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.

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bioRxiv2023

A consensus cell type atlas from multiple connectomes reveals principles of circuit stereotypy and variation

The fruit fly Drosophila melanogaster combines surprisingly sophisticated behaviour with a highly tractable nervous system. A large part of the flys success as a model organism in modern neuroscience stems from the concentration of collaboratively generated molecular genetic and digital resources. As presented in our FlyWire companion paper1, this now includes the first full brain connectome of an adult animal. Here we report the systematic and hierarchical annotation of this [~]130,000-neuron connectome including neuronal classes, cell types and developmental units (hemilineages). This enables any researcher to navigate this huge dataset and find systems and neurons of interest, linked to the literature through the Virtual Fly Brain database2. Crucially, this resource includes 4,552 cell types. 3,094 are rigorous consensus validations of cell types previously proposed in the "hemibrain" connectome3. In addition, we propose 1,458 new cell types, arising mostly from the fact that the FlyWire connectome spans the whole brain, whereas the hemibrain derives from a subvolume. Comparison of FlyWire and the hemibrain showed that cell type counts and strong connections were largely stable, but connection weights were surprisingly variable within and across animals. Further analysis defined simple heuristics for connectome interpretation: connections stronger than 10 unitary synapses or providing >1% of the input to a target cell are highly conserved. Some cell types showed increased variability across connectomes: the most common cell type in the mushroom body, required for learning and memory, is almost twice as numerous in FlyWire as the hemibrain. We find evidence for functional homeostasis through adjustments of the absolute amount of excitatory input while maintaining the excitation-inhibition ratio. Finally, and surprisingly, about one third of the cell types proposed in the hemibrain connectome could not yet be reliably identified in the FlyWire connectome. We therefore suggest that cell types should be defined to be robust to inter-individual variation, namely as groups of cells that are quantitatively more similar to cells in a different brain than to any other cell in the same brain. Joint analysis of the FlyWire and hemibrain connectomes demonstrates the viability and utility of this new definition. Our work defines a consensus cell type atlas for the fly brain and provides both an intellectual framework and open source toolchain for brain-scale comparative connectomics.

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bioRxiv2023

Synaptic architecture of leg and wing motor control networks in Drosophila

Animal movement is controlled by motor neurons (MNs), which project out of the central nervous system to activate muscles. MN activity is coordinated by complex premotor networks that allow individual muscles to contribute to many different behaviors. Here, we use connectomics to analyze the wiring logic of premotor circuits controlling the Drosophila leg and wing. We find that both premotor networks cluster into modules that link MNs innervating muscles with related functions. Within most leg motor modules, the synaptic weights of each premotor neuron are proportional to the size of their target MNs, establishing a circuit basis for hierarchical MN recruitment. In contrast, wing premotor networks lack proportional synaptic connectivity, which may allow wing steering muscles to be recruited with different relative timing. By comparing the architecture of distinct limb motor control systems within the same animal, we identify common principles of premotor network organization and specializations that reflect the unique biomechanical constraints and evolutionary origins of leg and wing motor control.

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bioRxiv2023

Integrating EM and Patch-seq data: Synaptic connectivity and target specificity of predicted Sst transcriptomic types

Neural circuit function is shaped both by the cell types that comprise the circuit and the connections between those cell types1. Neural cell types have previously been defined by morphology 2, 3, electrophysiology 4, 5, transcriptomic expression 6-8, connectivity 9-13, or even a combination of such modalities 14-16. More recently, the Patch-seq technique has enabled the characterization of morphology (M), electrophysiology (E), and transcriptomic (T) properties from individual cells 17-20. Using this technique, these properties were integrated to define 28, inhibitory multimodal, MET-types in mouse primary visual cortex 21. It is unknown how these MET-types connect within the broader cortical circuitry however. Here we show that we can predict the MET-type identity of inhibitory cells within a large-scale electron microscopy (EM) dataset and these MET-types have distinct ultrastructural features and synapse connectivity patterns. We found that EM Martinotti cells, a well defined morphological cell type 22, 23 known to be Somatostatin positive (Sst+) 24, 25, were successfully predicted to belong to Sst+ MET-types. Each identified MET-type had distinct axon myelination patterns and synapsed onto specific excitatory targets. Our results demonstrate that morphological features can be used to link cell type identities across imaging modalities, which enables further comparison of connectivity in relation to transcriptomic or electrophysiological properties. Furthermore, our results show that MET-types have distinct connectivity patterns, supporting the use of MET-types and connectivity to meaningfully define cell types.

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bioRxiv2023

Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex

Mammalian cortex features a vast diversity of neuronal cell types, each with characteristic anatomical, molecular and functional properties. Synaptic connectivity powerfully shapes how each cell type participates in the cortical circuit, but mapping connectivity rules at the resolution of distinct cell types remains difficult. Here, we used millimeter-scale volumetric electron microscopy1 to investigate the connectivity of all inhibitory neurons across a densely-segmented neuronal population of 1352 cells spanning all layers of mouse visual cortex, producing a wiring diagram of inhibitory connections with more than 70,000 synapses. Taking a data-driven approach inspired by classical neuroanatomy, we classified inhibitory neurons based on the relative targeting of dendritic compartments and other inhibitory cells and developed a novel classification of excitatory neurons based on the morphological and synaptic input properties. The synaptic connectivity between inhibitory cells revealed a novel class of disinhibitory specialist targeting basket cells, in addition to familiar subclasses. Analysis of the inhibitory connectivity onto excitatory neurons found widespread specificity, with many interneurons exhibiting differential targeting of certain subpopulations spatially intermingled with other potential targets. Inhibitory targeting was organized into "motif groups," diverse sets of cells that collectively target both perisomatic and dendritic compartments of the same excitatory targets. Collectively, our analysis identified new organizing principles for cortical inhibition and will serve as a foundation for linking modern multimodal neuronal atlases with the cortical wiring diagram.

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bioRxiv2022

Large-scale unsupervised discovery of excitatory morphological cell types in mouse visual cortex

Neurons in the neocortex exhibit astonishing morphological diversity which is critical for properly wiring neural circuits and giving neurons their functional properties. However, the organizational principles underlying this morphological diversity remain an open question. Here, we took a data-driven approach using graph-based machine learning methods to obtain a low-dimensional morphological "bar code" describing more than 30,000 excitatory neurons in mouse visual areas V1, AL and RL that were reconstructed from the millimeter scale MICrONS serial-section electron microscopy volume. Contrary to previous classifications into discrete morphological types (m-types), our data-driven approach suggests that the morphological landscape of cortical excitatory neurons is better described as a continuum, with a few notable exceptions in layers 5 and 6. Dendritic morphologies in layers 2-3 exhibited a trend towards a decreasing width of the dendritic arbor and a smaller tuft with increasing cortical depth. Inter-area differences were most evident in layer 4, where V1 contained more atufted neurons than higher visual areas. Moreover, we discovered neurons in V1 on the border to layer 5 which avoided deeper layers with their dendrites. In summary, we suggest that excitatory neurons morphological diversity is better understood by considering axes of variation than using distinct m-types.

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bioRxiv2022

Tools for comprehensive reconstruction and analysis of Drosophila motor circuits

Like the vertebrate spinal cord, the insect ventral nerve cord (VNC) mediates limb sensation and motor control. Here, we apply automated tools for electron microscopy volume alignment, neuron segmentation, and synapse prediction toward creating a connectome of an adult female Drosophila VNC. To interpret a 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 nano-tomography of the fly leg and wing. Knowing the outputs of the connectome allowed us to identify neural circuits that coordinate the wings and legs during escape takeoff. We provide the reconstruction of VNC circuits and motor neuron atlas, along with tools for programmatic and interactive access, as community resources to support experimental and theoretical studies of how the fly nervous system controls behavior.

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bioRxiv2022

Fast imaging of millimeter-scale areas with beam deflection transmission electron microscopy

Serial section transmission electron microscopy (TEM) has proven to be one of the leading methods for millimeter-scale 3D imaging of brain tissues at nanoscale resolution. It is important to further improve imaging efficiency to acquire larger and more brain volumes. We report here a three fold increase in the speed of TEM by using a beam deflecting mechanism to enable highly efficient acquisition of multiple image tiles (nine) for each motion of the mechanical stage. For millimeter-scale areas, the duty cycle of imaging doubles to more than 30%, yielding a net average imaging rate of 0.3 gigapixels per second. If fully utilized, an array of beam deflection TEMs should be capable of imaging a dataset of cubic millimeter scale in several weeks.

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