Barak Nehoran

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.

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.

Understanding the relationship between circuit connectivity and function is crucial for uncovering how the brain computes. In mouse primary visual cortex, excitatory neurons with similar response properties are more likely to be synaptically connected^1–8; however, broader connectivity rules remain unknown. Here we leverage the millimetre-scale MICrONS dataset to analyse synaptic connectivity and functional properties of neurons across cortical layers and areas. Our results reveal that neurons with similar response properties are preferentially connected within and across layers and areas—including feedback connections—supporting the universality of ‘like-to-like’ connectivity across the visual hierarchy. Using a validated digital twin model, we separated neuronal tuning into feature (what neurons respond to) and spatial (receptive field location) components. We found that only the feature component predicts fine-scale synaptic connections beyond what could be explained by the proximity of axons and dendrites. We also discovered a higher-order rule whereby postsynaptic neuron cohorts downstream of presynaptic cells show greater functional similarity than predicted by a pairwise like-to-like rule. Recurrent neural networks trained on a simple classification task develop connectivity patterns that mirror both pairwise and higher-order rules, with magnitudes similar to those in MICrONS data. Ablation studies in these recurrent neural networks reveal that disrupting like-to-like connections impairs performance more than disrupting random connections. These findings suggest that these connectivity principles may have a functional role in sensory processing and learning, highlighting shared principles between biological and artificial systems.

Understanding the brain requires understanding neurons’ functional responses to the circuit architecture shaping them. Here we introduce the MICrONS functional connectomics dataset with dense calcium imaging of around 75,000 neurons in primary visual cortex (VISp) and higher visual areas (VISrl, VISal and VISlm) in an awake mouse that is viewing natural and synthetic stimuli. These data are co-registered with an electron microscopy reconstruction containing more than 200,000 cells and 0.5 billion synapses. Proofreading of a subset of neurons yielded reconstructions that include complete dendritic trees as well the local and inter-areal axonal projections that map up to thousands of cell-to-cell connections per neuron. Released as an open-access resource, this dataset includes the tools for data retrieval and analysis^1,2. Accompanying studies describe its use for comprehensive characterization of cell types^3–6, a synaptic level connectivity diagram of a cortical column⁴, and uncovering cell-type-specific inhibitory connectivity that can be linked to gene expression data^4,7. Functionally, we identify new computational principles of how information is integrated across visual space⁸, characterize novel types of neuronal invariances⁹ and bring structure and function together to uncover a general principle for connectivity between excitatory neurons within and across areas^10,11.