Casey M Schneider-Mizell

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.

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 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 image or segmentation data directly. Instead, we leverage tools from geometry processing to create features capturing the local geometry of a neuron’s surface. We couple this core technique with computational and storage optimizations, enabling reliable deployment over hundreds of thousands of neurons for a few hundred dollars in cloud compute cost. We then show that a simple classifier trained on the MICrONS mouse visual cortex dataset can use these mesh-based features to accurately classify synapses as targeting somas, dendritic shafts, or spines (weighted F1 score 0.961). Using this pipeline, we create a map of the postsynaptic structures at over 207.3 million synapses in MICrONS. We present an overview of this census of postsynaptic targeting, finding expected patterns (e.g., excitatory neurons preferentially targeting excitatory spines) as well as unexpected exceptions (e.g., Layer 5 near-projecting and Layer 6 corticothalamic cells often connecting to excitatory neuron shafts). We also demonstrate that these tools can be used to detect spines receiving multiple synaptic inputs, revealing surprising variability in their frequency 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 cloud deployment. Beyond MICrONS, we find that the model generalizes well to the H01 connectome without retraining (weighted F1 score 0.949), indicating that these tools will be useful in future connectomics reconstructions. More generally, our work demonstrates that representations derived from neuronal meshes can be a scalable and generalizable primitive for describing morphologies.

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.

1 In 1949, Donald Hebb proposed that groups of neurons that activate stereotypically form the organizational 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 analyze this relation using a novel dataset that links in vivo optical physiology to connectivity using postmortem elec-tron microscopy (EM). From the fluorescence traces, we extract neural assemblies from higher-order correlations in neural activity. Physiologically, we show that these assemblies exhibit properties consistent with Hebb’s theory, including more reliable responses to repeated natural movie inputs than size-matched random ensembles and superior decoding of visual stimuli. Structurally, we find that neurons that participate in assemblies are significantly more integrated into the structural network than those that do not. Contrary to Hebb’s original prediction, we do not observe a marked increase in the strength of monosynaptic excitatory connections between cells participating in the same assembly. However, we find significantly stronger indirect feed-forward inhibitory connections targeting cells in other assemblies. These results show that assemblies can be useful components of perception, and, surprisingly, they are delineated by mutual inhibition.

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.

We are in the era of millimetre-scale electron microscopy volumes collected at nanometre resolution^1,2. Dense reconstruction of cellular compartments in these electron microscopy volumes has been enabled by recent advances in machine learning^3–6. Automated segmentation methods produce exceptionally accurate reconstructions of cells, but post hoc proofreading is still required to generate large connectomes that are free of merge and split errors. The elaborate 3D meshes of neurons in these volumes contain detailed morphological information at multiple scales, from the diameter, shape and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting these features can require substantial effort to piece together existing tools into custom workflows. Here, building on existing open source software for mesh manipulation, we present Neural Decomposition (NEURD), a software package that decomposes meshed neurons into compact and extensively annotated graph representations. With these feature-rich graphs, we automate a variety of tasks such as state-of-the-art automated proofreading of merge errors, cell classification, spine detection, axonal-dendritic proximities and other annotations. These features enable many downstream analyses of neural morphology and connectivity, making these massive and complex datasets more accessible to neuroscience researchers.

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.

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 morphology^2,3, electrophysiology⁴, transcriptomic expression^5,6, connectivity^7,8,9 or a combination of such modalities^10,11,12. The Patch-seq technique enables the characterization of morphology, electrophysiology and transcriptomic properties from individual cells^13,14,15. 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 type^18,19—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.