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Abstract

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.

02 /Specification & Classification

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Claude Opus 5.5 · xhigh effort · 8 Nov 2026 · from the full text of version 3 (6 Jan 2024) · CC BY 4.0

Electron microscopy of 1352 neurons in a mouse visual cortex column reveals widespread cell-type-specific inhibition, a basket-cell-targeting disinhibitory class, and interneuron motif groups sharing excitatory targets.

Knowledge of cortical inhibitory wiring rests mostly on coarse molecular subclasses, and whether interneurons inhibit nearby excitatory cells indiscriminately or pick out particular subpopulations is debated. Unlike earlier cortical electron microscopy (EM) volumes, this cubic-millimeter one holds complete dendritic arbors, so the outputs of all interneurons in a pia-to-white-matter column could be mapped onto a densely reconstructed population, a scale physiological and viral methods struggle to reach.

Key results

  1. ShownInterneurons specialised for inhibiting other interneurons (InhTCs) form two groups. Most target dendrite-targeting (somatostatin-like) cells, as expected. A second group, which the authors report as new, selectively targets perisomatic (basket-like) cells, with larger synapses and more synapses per connection.

    Of 29 InhTCs, 21 sent most inhibitory-directed output to distal-dendrite-targeting cells (DistTCs; mean 74%) and 8 to perisomatic-targeting cells (PeriTCs; mean 82%). The 8 contacted 56/58 PeriTCs (mean 10.5 synapses per target). Their PeriTC synapses were 69% larger (median) than those onto other subclasses, with 3.1 vs 1.5 synapses per connection (p = 1.1 × 10^-5, t-test). · Inhibition of inhibitory neurons; B. Basket cell disinhibition

  2. ShownClustering excitatory neurons on synaptic input features as well as dendritic shape gave morphological types (M-types). These separate upper from lower layer 2/3 and distinguish visually similar cells, such as two layer 2 types that differ markedly in the number and density of synaptic inputs.

    Consensus clustering of 29 features gave 17 M-types; 142/143 (99%) expert-labelled layer 6 cortico-thalamic (CT) cells fell into L6tall types. L2a vs L2b: 4758 vs 2649 synaptic inputs, 1.04 vs 0.72 syn/µm median density, 4532 vs 3510 µm dendritic length. Layer 5 near-projecting (L5NP) cells had the fewest dendritic inputs and the lowest input density. · Dendritic synaptic input properties define…; Excitatory feature extraction and clustering

  3. ShownInhibition is spread unevenly across M-types, and much of the difference tracks overall synaptic input. Somatic and dendritic inputs rise together, cells that receive more perisomatic-targeting input tend to receive more dendrite-targeting input, and intermingled L2a and L2b cells get very different amounts.

    Of 70,884 inhibitory-to-excitatory column synapses, PeriTCs and DistTCs averaged 581 and 596 per cell. Somatic vs dendritic input across M-types: r = 0.96, p = 5 × 10^-10. PeriTC and DistTC input correlated positively within 12/18 M-types. L2b cells received 37.7 ± 0.27 intracolumnar inhibitory synapses vs 94.8 ± 0.58 for L2a. · Coordination of inhibition across excitatory…; Figure legends

  4. SuggestedClustering interneurons by how their output divides among excitatory M-types gave 18 motif groups. Most groups mix subclasses, so perisomatic and dendrite-targeting cells jointly inhibit the same target types, often with sublaminar precision.

    15/18 groups (156/163 cells) held at least two subclasses. Within-column output budgets correlated with dataset-wide budgets based on predicted M-types (Pearson r = 0.90), although interneurons made more than 4 times as many synapses outside the column. In layers 2–4 each M-type received strong inhibition from 2-3 groups; most layer 5 groups targeted a single M-type. · Cellular contributions of inhibition

  5. SuggestedSpecific targeting comes from both precise axon placement and synaptic selectivity, weighted differently by subclass. A layer-2-specific group is largely explained by where its axons overlap targets, whereas dendrite-targeting cells need selectivity beyond a depth- and compartment-matched null model.

    In 17/18 motif groups the Selectivity Index (observed vs shuffled synapses) was consistently above or below 1 for some M-types, yet layer-2-specific Group 1 showed no consistent positive selectivity. PeriTCs had the lowest Kullback-Leibler divergence from the null model; perisomatic inputs were more spatially segregated across M-types than distal/apical ones (p = 3.0 × 10^-19, Mann-Whitney U). · Synaptic selectivity; E. Peters’ rule and White’s exceptions; Figure legends

Methods, data and reproducibility

In the MICrONs transmission electron microscopy (EM) volume of mouse visual cortex, 1352 neurons in a 100 × 100 µm pia-to-white-matter column were proofread. Interneurons were classed by targeted compartments with a linear discriminant classifier and their outputs tested against a depth- and compartment-matched null model; excitatory cells were consensus-clustered on morphology and synapse features.

  • MICrONs serial-section transmission EM volume of visual cortex (larger subvolume covering parts of VISp, AL and RL), analysed at materialization version 795 · mouse (single male, SLC17a7-Cre; Ai162, GCaMP6s in excitatory neurons) · n = single male mouse
  • Proofread 100 × 100 µm pia-to-white-matter column: complete dendrites, partial inhibitory axons, synapse tables, skeletons, cell-type and connectivity-group labels · mouse · n = 1352 neurons (1886 cells with nucleus in column); 163 interneurons analysed · 10.5281/zenodo.7641780
  • Inhibitory wiring diagram within the column (interneuron-to-interneuron synapses span 3,569 connections) · mouse · n = 70,884 synapses onto excitatory cells; 9,235 synapses between interneurons
  • Expert manual labels of inhibitory subclasses used to train the linear discriminant classifier · mouse · n = 150 labelled cells; 13 uncertain
Methods & tools
Serial-section transmission EM (autoTEM, modified JEOL 1200EXII), ATUMtome, render-modules, PyChunkedGraph, Neuroglancer, CAVEclient, CloudVolume, MeshParty, Guidebook, TEASAR skeletonization, phenograph (Leiden clustering), scikit-learn
Software & environment
Python 3.9 with custom code using CAVEclient, CloudVolume, MeshParty, Matplotlib, Numpy, Pandas, Scikit-learn, Scipy, stats-models and VTK; data analysed at materialization version 795. Package versions and requirement files are not stated.
Experimental conditions
Hardware not stated. More than 46,000 manual proofreading edits; dendrite proofreading took about ten minutes per cell and inhibitory axon cleaning and extension 3–10 hours per neuron; skeletons recompute in about 10 seconds.
Reproducibility gaps
Analysis code is described as forthcoming on an Allen Institute GitHub repository (not released in this version); MICrONs-Explorer serves an older segmentation, to be updated closer to publication.
Limitations (6)
  • All data come from one male mouse, at a site toward the edge of VISp, with only a handful of examples of each cell type; variability across cells, animals and cortical locations remains to be measured. · stated by the authors
  • Connectivity was measured only onto targets inside a 100 × 100 µm column, so distance-dependent wiring or axons partly outside it could bias distributions; deep-layer cells may receive inhibition from more distant interneurons, and no chandelier cells fell in the column. · stated by the authors
  • Inhibitory axons were not traced to completion (an estimated 50-75% of synaptic output captured), so axonal measures are lower bounds; the unsegmented top 10 µm of layer 1 truncates apical tufts and degrades layer 1 interneuron reconstructions. · stated by the authors
  • Inhibitory subclasses are defined from EM targeting features rather than molecular markers and match molecular subclasses only approximately; which molecular subclass the basket-targeting disinhibitory cells belong to is unknown. · stated by the authors
  • Only classical synapses were analysed (no gap junctions, non-synaptic transmitter release or neuromodulators), and automated synapse detection does not separate excitatory from inhibitory inputs, so null-model baselines pool both. · stated by the authors
  • Some reported numbers disagree between sections (17 vs 18 excitatory M-types; 1,000 vs 10,000 shuffles per interneuron; 33 vs 23 added apical-classifier training cells), so check details against the released tables and code. · noted in this summary

Who should read:Read this if you study cortical interneuron types or inhibitory circuits and want EM-based, cell-type-resolved wiring rules, or a proofread column of mouse visual cortex with synapse tables to reanalyse.

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