Information processing in the cortex depends on the integration of bottom-up and top-down signals through recurrent microcircuits spanning layers. Although the canonical microcircuit provides a framework for this integration, how these interactions are implemented at synapse resolution remains unclear. Here, we use large-volume electron microscopy reconstructions of mouse primary visual cortex to map the intralaminar and interlaminar connectivity of intratelencephalic (IT) neurons in layers 2/3 and 5. We find that layer 2/3 IT neurons formed a depth-dependent gradient of recurrent connectivity, with superficial (L2) and deeper (L3) neurons potentially forming two channels associated with top-down and bottom-up processing, respectively. These channels are preserved across layers via cell-type-specific pathways involving distinct L5 IT types, rather than collapsing into a single integrative pool. Moreover, each channel is regulated by a largely separate cohort of inhibitory interneurons, stabilizing recurrent excitation while limiting crosstalk. Together, these results reveal parallel, cell-type-specific processing streams embedded within the canonical circuit.

Just as genomes revolutionized molecular genetics, connectomes (maps of neurons and synapses) are transforming neuroscience. To date, the only organisms with complete connectomes are worms1-3, sea squirts4 and comb jellies5 (103-104 synapses). By contrast, the fruit fly is more complex (108 synaptic connections), with a brain that supports learning and spatial memory6,7 and an intricate ventral nerve cord analogous to the vertebrate spinal cord8-12. Here we report a densely reconstructed adult fly connectome that unites the brain and ventral nerve cord, and we leverage this resource to investigate principles of neural control. We show that effector neurons (motor neurons, endocrine cells and efferent neurons targeting the viscera) are primarily influenced by sensory neurons in the same body part, forming local feedback loops. These local loops are linked by long-range circuits that involve ascending and descending neurons organized into behaviour-centric modules. Single ascending and descending neurons are often positioned to influence the voluntary movements of multiple body parts, together with the endocrine cells or visceral organs that support those movements. Brain regions involved in learning and navigation supervise these circuits. These results reveal an architecture that is distributed, parallelized and embodied, reminiscent of distributed control architectures in engineered systems13,14.

The mossy fiber (MF) connections to pyramidal cells in hippocampal CA3 are thought to participate in pattern separation, pattern completion and memory encoding, yet no large-scale neuronal wiring diagram exists for these connections. We assembled a 3D electron microscopy volume (~1 × 1 × 0.1 mm3) 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 MF axons. 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 motif 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.ai , an online platform for hippocampal connectomics.

In 1949, Donald Hebb proposed that neuronal assemblies with temporally specific patterns of activity form the building blocks of perception, cognition, and behavior. Finding the structural underpinning of such assemblies has been technically challenging due to a lack of large-scale structure-activity maps. Here, we combine in vivo optical physiology with postmortem electron microscopy (EM) in the same tissue volume. Using higher-order correlations in fluorescence traces, we extract neuronal assemblies. Physiologically, we show that these assemblies respond more reliably to repeated natural movies than size-matched control ensembles and decode such stimuli more accurately. Structurally, we find that over a quarter of the pyramidal neurons do not participate in any assembly and are significantly less integrated into the connectome than those that do. We do not observe a marked increase in the strength of monosynaptic excitatory connections between neurons sharing assembly assignment, but instead find significantly stronger indirect inhibitory connections targeting cells in other assemblies. These results show that assemblies can serve as functional units of perception and suggest they may be structurally delineated by mutual inhibition.

Animals continuously monitor their body surfaces to detect and remove debris or parasites. Effective grooming requires that tactile inputs from specific body regions be converted into precisely targeted motor actions, but the neural circuits that support this sensorimotor transformation remain poorly understood. Here, we combine genetic tools and connectomics to elucidate a central somatotopic map of the Drosophila leg. We show that the axonal projections of leg touch receptors within the fly’s ventral nerve cord (VNC) are organized along the same cardinal axes as the developing leg. Somatotopically organized bristle axons target a specific class of developmentally related local interneurons, which imbricate the leg map with overlapping receptive fields of different shapes and sizes. These second-order interneurons target distinct pools of premotor interneurons, which in turn synapse directly onto motor neurons that control leg muscles. Optogenetic activation of second-order interneurons elicits spatially targeted grooming of specific leg regions, consistent with our spatial receptive field predictions based on the connectome. Together, our results suggest that this four-layer circuit processes spatial information from a somatotopic map of the fly leg to guide targeted grooming behavior.

Neurons display remarkable sub-cellular specificity in their synaptic targeting, which varies by cell type—for example, excitatory neurons prefer to target the spines of other excitatory cells. Modern dense neuroanatomy data, such as large volumetric electron microscopy connectomes, enable the study of this sub-cellular specificity and its context in a circuit at unprecedented scale and resolution. However, this scale has also made it challenging to create accurate and efficient methods for classifying and segmenting fine cell components (including spines) across entire volumes. Here, we present a cost-efficient computational pipeline for classifying postsynaptic targets and segmenting structures such as spines. Our method relies only on having a mesh representation of a neuron and avoids processing imaging data directly. Instead, we leverage tools from geometry processing to create features from the intrinsic geometry of a neuron’s surface. We couple this core technique with strategies for accelerating the computation and reducing the storage size of these features, creating a pipeline which can be deployed reliably over hundreds of thousands of neurons in the commercial cloud for a few hundred dollars. We then show how a simple but accurate classifier can use these mesh-based features to classify synapses as targeting somas, dendritic shafts, or spines (weighted F1 score 0.961). Using this pipeline, we create a publicly available map of the postsynaptic structures at over 208.6 million synapses in the MICrONS mouse visual cortex dataset. We present an overview of this census of postsynaptic targeting in MICrONS, finding expected patterns (e.g., excitatory neurons preferentially targeting excitatory spines) as well as less characterized exceptions (e.g., Layer 5 near-projecting and Layer 6 corticothalamic cells often connecting to excitatory neuron shafts). These tools also enable us to detect spines which receive multiple synaptic inputs—we find that the frequency of these multiply-innervated spines is unexpectedly variable across cells even within a cell type. We make our postsynaptic target predictions available for study, as well as the code for the computational pipeline and commercial cloud deployment. More generally, our work demonstrates how representations derived from neuronal meshes can be a powerful and scalable primitive for describing neural morphologies.

The mammalian basal ganglia (BG) orchestrate motor, cognitive, and affective functions, yet cell type-specific genetic access remains limited, especially beyond rodents. Key structures implicated in movement and psychiatric disorders, including pallidum, subthalamic nucleus, and dopaminergic midbrain, lack scalable tools for cross-species targeting. Here, we present a comprehensive enhancer-AAV library enabling selective labeling and manipulation of major BG neuronal populations: striatal projection neuron subtypes, pallidal and subthalamic neurons, and midbrain dopaminergic and GABAergic populations. Using an evolutionarily informed discovery pipeline, we identified enhancers targeting canonical, non-canonical, and disease-relevant cell types, with validation demonstrating robust cross-species conservation of specificity between mouse and macaque. Computational modeling revealed sequence features predictive of in vivo performance, including motif grammar, chromatin accessibility, and evolutionary conservation, and identified distinct regulatory architectures across glial, projection, and interneuron lineages. This work establishes a comprehensive cross-species viral toolkit for the BG, unlocking previously inaccessible cell types for circuit dissection.

The mammalian brain consists of diverse neuron types with various functions. Recent single-cell RNA sequencing approaches have led to a whole-brain taxonomy of transcriptomically defined cell types1. Patch-seq experiments augment these cell-type descriptions by linking transcriptomic profiles with local morphological and electrophysiological properties2-7. However, linking transcriptomic identities to long-range axonal projections remains a major unresolved challenge. Here, to address this, we collected two datasets from the mouse visual cortex consisting of: (1) 1,528 excitatory Patch-seq neurons, with local morphological, electrophysiological and transcriptomic data collected from each cell, and (2) 341 excitatory, whole-neuron morphologies. From the Patch-seq data, we defined 17 morphoelectric-transcriptomic types and built a multistep classifier to integrate cell-type assignments with whole-neuron morphology and interrogate cross-modality relationships. We find that transcriptomic variation within and across morphoelectric-transcriptomic types corresponds with morphological and electrophysiological phenotypes. In addition, these gene expression patterns, along with the anatomical location of the cell, can be used to predict projection targets of individual neurons. We observed novel multimodal cell-type signatures for layer 5 intratelencephalic and extratelencephalic neurons and shed new light on their axonal circuitry, including interhemispheric intratelencephalic projections. With this approach, we establish a comprehensive, integrated taxonomy of cortical, excitatory neuron types, and create a system for high-dimensional cell-type classification that can be extended to the whole brain and potentially across species.

We present an enhancer-AAV toolbox for accessing and perturbing striatal cell types and circuits. Best-in-class vectors were curated for accessing major striatal neuron populations including medium spiny neurons (MSNs), direct- and indirect-pathway MSNs, Sst-Chodl, Pvalb-Pthlh, and cholinergic interneurons. Specificity was evaluated by multiple modes of molecular validation, by three different routes of virus delivery, and with diverse transgene cargos. Importantly, we provide detailed information necessary to achieve reliable cell-type-specific labeling under different experimental contexts. We demonstrate direct pathway circuit-selective optogenetic perturbation of behavior and multiplex labeling of striatal interneuron types for targeted analysis of cellular features. Lastly, we show conserved in vivo activity for exemplary MSN enhancers in rats and macaques. This collection of striatal enhancer AAVs offers greater versatility compared to available transgenic lines and can readily be applied for cell type and circuit studies in diverse mammalian species beyond the mouse model.

Neural circuit function is shaped both by the cell types that comprise the circuit and the connections between them1. Neural cell types have previously been defined by morphology2,3, electrophysiology4, transcriptomic expression5,6, connectivity7-9 or a combination of such modalities10-12. The Patch-seq technique enables the characterization of morphology, electrophysiology and transcriptomic properties from individual cells13-15. These properties were integrated to define 28 inhibitory, morpho-electric-transcriptomic (MET) cell types in mouse visual cortex16, which do not include synaptic connectivity. Conversely, large-scale electron microscopy (EM) enables morphological reconstruction and a near-complete description of a neuron's local synaptic connectivity, but does not include transcriptomic or electrophysiological information. Here, we leveraged morphological information from Patch-seq to predict the transcriptomically defined cell subclass and/or MET-type of inhibitory neurons within a large-scale EM dataset. We further analysed Martinotti cells-a somatostatin (Sst)-positive17 morphological cell type18,19-which were classified successfully into Sst MET-types with distinct axon myelination and synaptic output connectivity patterns. We demonstrate that morphological features can be used to link cell types across experimental modalities, enabling further comparison of connectivity to gene expression and electrophysiology. We observe unique connectivity rules for predicted Sst cell types.

The complexity of neural circuits makes it challenging to decipher the brain's algorithms of intelligence. Recent breakthroughs in deep learning have produced models that accurately simulate brain activity, enhancing our understanding of the brain's computational objectives and neural coding. However, it is difficult for such models to generalize beyond their training distribution, limiting their utility. The emergence of foundation models1 trained on vast datasets has introduced a new artificial intelligence paradigm with remarkable generalization capabilities. Here we collected large amounts of neural activity from visual cortices of multiple mice and trained a foundation model to accurately predict neuronal responses to arbitrary natural videos. This model generalized to new mice with minimal training and successfully predicted responses across various new stimulus domains, such as coherent motion and noise patterns. Beyond neural response prediction, the model also accurately predicted anatomical cell types, dendritic features and neuronal connectivity within the MICrONS functional connectomics dataset2. Our work is a crucial step towards building foundation models of the brain. As neuroscience accumulates larger, multimodal datasets, foundation models will reveal statistical regularities, enable rapid adaptation to new tasks and accelerate research.

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 (~1×1×0.1mm3) 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.

Despite significant progress in characterizing neocortical cell types, a complete understanding of the synaptic connections of individual excitatory cells remains elusive. This study investigates the connectivity of mouse visual cortex thick tufted layer 5 pyramidal cells, also known as extratelencephalic neurons (L5-ETns), using a 1 mm3 publicly available electron microscopy dataset. The analysis reveals that, in their immediate vicinity, L5-ETns primarily establish connections with a group of inhibitory cell types, which, in turn, specifically target the L5-ETns back. The most common excitatory targets of L5-ETns are layer 5 intertelencephalic neurons (L5-ITns) and layer 6 (L6) pyramidal cells, whereas synapses with other L5-ETns are less common. When L5-ETns extend their axons to other cortical regions, they tend to connect more with excitatory cells. Our results highlight a circuit motif where a subclass of excitatory cells forms a subcircuit with specific inhibitory cell types. This is achieved using a publicly available, automated approach for synapse recognition and automated cell typing, offering a framework for exploring the connectivity of other neuron types.

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 CAVE's 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.

Mammalian cortex features a vast diversity of neuronal cell types, each with characteristic anatomical, molecular and functional properties1. Synaptic connectivity 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 millimetre-scale volumetric electron microscopy2 to investigate the connectivity of all inhibitory neurons across a densely segmented neuronal population of 1,352 cells spanning all layers of mouse visual cortex, producing a wiring diagram of inhibition with more than 70,000 synapses. Inspired by classical neuroanatomy, we classified inhibitory neurons based on targeting of dendritic compartments and developed an excitatory neuron classification based on dendritic reconstructions with whole-cell maps of synaptic input. Single-cell connectivity showed a class of disinhibitory specialist that targets basket cells. Analysis of inhibitory connectivity onto excitatory neurons found widespread specificity, with many interneurons exhibiting differential targeting of spatially intermingled subpopulations. 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 contemporary multimodal neuronal atlases with the cortical wiring diagram.

In recent years there has been a tremendous growth in new technologies that allow large-scale investigation of different characteristics of the nervous system at an unprecedented level of detail. There is a growing trend to use combinations of these new techniques to determine direct links between different modalities. In this Perspective, we focus on the mouse visual cortex, as this is one of the model systems in which much progress has been made in the integration of multimodal data to advance understanding. We review several approaches that allow integration of data regarding various properties of cortical cell types, connectivity at the level of brain areas, cell types and individual cells, and functional neural activity in vivo. The increasingly crucial contributions of computation and theory in analyzing and systematically modeling data are also highlighted. Together with open sharing of data, tools and models, integrative approaches are essential tools in modern neuroscience for improving our understanding of the brain architecture, mechanisms and function.

Just as genomes revolutionized molecular genetics, connectomes (maps of neurons and synapses) are transforming neuroscience. To date, the only species with complete connectomes are worms1-3 and sea squirts4 (103-104 synapses). By contrast, the fruit fly is more complex (108 synaptic connections), with a brain that supports learning and spatial memory5,6 and an intricate ventral nerve cord analogous to the vertebrate spinal cord7-11. Here we report the first adult fly connectome that unites the brain and ventral nerve cord, and we leverage this resource to investigate principles of neural control. We show that effector cells (motor neurons, endocrine cells and efferent neurons targeting the viscera) are primarily influenced by local sensory cells in the same body part, forming local feedback loops. These local loops are linked by long-range circuits involving ascending and descending neurons organized into behavior-centric modules. Single ascending and descending neurons are often positioned to influence the voluntary movements of multiple body parts, together with endocrine cells or visceral organs that support those movements. Brain regions involved in learning and navigation supervise these circuits. These results reveal an architecture that is distributed, parallelized and embodied (tightly connected to effectors), reminiscent of distributed control architectures in engineered systems12,13.

The mammalian cortex is comprised of cells classified into types according to shared properties. Defining the contribution of each cell type to the processes guided by the cortex is essential for understanding its function in health and disease. We use transcriptomic and epigenomic cortical cell-type taxonomies from mouse and human to define marker genes and putative enhancers and create a large toolkit of transgenic lines and enhancer adeno-associated viruses (AAVs) for selective targeting of cortical cell populations. We report creation and evaluation of fifteen transgenic driver lines, two reporter lines, and >1,000 different enhancer AAV vectors covering most subclasses of cortical cells. The tools reported here have been made publicly available, and along with the scaled process of tool creation, evaluation, and modification, they will enable diverse experimental strategies toward understanding mammalian cortex and brain function.