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The authors use Patch-seq and electron microscopy datasets to relate synaptic connectivity to the transcriptomic cell type of different types of inhibitory neuron.

Excitatory neurons in the neocortex exhibit considerable morphological diversity, yet their organizational principles remain a subject of ongoing research. Here, the authors use unsupervised learning to show that most excitatory neuron morphologies in the mouse visual cortex form a continuum, with notable exceptions in deeper layers.

This study maps the connections of layer 5 pyramidal neurons in the mouse cortex, revealing distinct local and intercortical wiring patterns, and provides an open framework for exploring the connectivity of cell types.

A foundation model trained on neural activity of visual cortex from multiple mice accurately predicts responses to video stimuli and cell types, dendritic features and connectivity within the MICrONS functional connectomics dataset.

Eschbach, Fushiki et al. combine synaptic-resolution circuit mapping, functional analyses and modeling to reveal circuit motifs that regulate dopaminergic neuron activity and may increase associative learning task performance and flexibility.

An analysis demonstrates that quantitative measurements of perisomatic ultrastructure features of neurons can be used to categorize them into cell types.

Dense calcium imaging combined with co-registered high-resolution electron microscopy reconstruction of the brain of the same mouse provide a functional connectomics map of tens of thousands of neurons of a region of the primary cortex and higher visual areas.

The Connectome Annotation Versioning Engine (CAVE) is a platform for proofreading, annotating and analyzing datasets reaching the petascale. Currently, CAVE is used for electron microscopy datasets, but it can potentially be used for other large-scale datasets.

Combining neural manipulation in freely behaving animals, physiological studies and electron microscopy reconstruction in the Drosophila larva identifies a complex multilsensory circuit involved in the selection of larval escape modes that exhibits a multilevel multimodal convergence architecture.

Neural Decomposition (NEURD) is a software package that decomposes neuronal data from high-resolution electron microscopy volumes into feature-rich graph representations to facilitate analysis for neuroscience research.

A consensus cell type atlas for the fly brain provides both an intellectual framework and open-source toolchains for brain-scale comparative connectomics.

Using volumetric electron microscopy, the authors map and analyze the structure of cortical inhibition with synaptic resolution across a column of visual cortex.

The MICrONS mouse visual cortex dataset shows that neurons with similar response properties preferentially connect, a pattern that emerges within and across brain areas and layers, and independently emerges in artificial neural networks where these ‘like-to-like’ connections prove important for task performance.

Automated reconstruction of dense neural networks in the ventral nerve cord of the fruit fly provides a resource for investigating the neural control of movement.

FlyWire presents a neuronal wiring diagram of the whole fly brain with annotations for cell types, classes, nerves, hemilineages and predicted neurotransmitters, with data products and an open ecosystem to facilitate exploration and browsing.

We use connectomics to compare the wiring logic of premotor circuits controlling the Drosophila leg and wing, finding that both premotor networks cluster into modules that link motor neurons innervating muscles with related functions.

SegCLR automatically annotates segmented electron microscopy datasets of the brain with information such as cellular subcompartments and cell types, using a self-supervised contrastive learning approach.

FlyWire is an online community and a platform for proofreading electron microscopy-based connectome data of the Drosophila brain.

The complete, synapse-resolution connectome of the Drosophila larval mushroom body.