Functional connectomics spanning multiple areas of mouse visual cortex

Nature, Год журнала: 2025, Номер 640(8058), С. 435 - 447

Опубликована: Апрель 9, 2025

Abstract Understanding the brain requires understanding neurons’ functional responses to circuit architecture shaping them. Here we introduce MICrONS connectomics dataset with dense calcium imaging of around 75,000 neurons in primary visual cortex (VISp) and higher areas (VISrl, VISal VISlm) an awake mouse that is viewing natural synthetic stimuli. These data are co-registered electron microscopy reconstruction containing more than 200,000 cells 0.5 billion synapses. Proofreading a subset yielded reconstructions include complete dendritic trees as well local inter-areal axonal projections map up thousands cell-to-cell connections per neuron. Released open-access resource, this includes tools for retrieval analysis 1,2 . Accompanying studies describe its use comprehensive characterization cell types 3–6 , synaptic level connectivity diagram cortical column 4 uncovering cell-type-specific inhibitory can be linked gene expression 4,7 Functionally, identify new computational principles how information integrated across space 8 characterize novel neuronal invariances 9 bring structure function together uncover general principle between excitatory within 10,11

Язык: Английский

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Functional connectomics reveals general wiring rule in mouse visual cortex

Nature, Год журнала: 2025, Номер 640(8058), С. 459 - 469

Опубликована: Апрель 9, 2025

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

Язык: Английский

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NEURD offers automated proofreading and feature extraction for connectomics

Nature, Год журнала: 2025, Номер 640(8058), С. 487 - 496

Опубликована: Апрель 9, 2025

We are in the era of millimetre-scale electron microscopy volumes collected at nanometre resolution1,2. Dense reconstruction cellular compartments these has been enabled by recent advances machine learning3-6. Automated segmentation methods produce exceptionally accurate reconstructions cells, but post hoc proofreading is still required to generate large connectomes that free merge and split errors. The elaborate 3D meshes neurons contain detailed morphological information multiple scales, from diameter, shape branching patterns axons dendrites, down fine-scale structure dendritic spines. However, extracting features can require substantial effort piece together existing tools into custom workflows. Here, building on open source software for mesh manipulation, we present Neural Decomposition (NEURD), a package decomposes meshed compact extensively annotated graph representations. With feature-rich graphs, automate variety tasks such as state-of-the-art automated errors, cell classification, spine detection, axonal-dendritic proximities other annotations. These enable many downstream analyses neural morphology connectivity, making massive complex datasets more accessible neuroscience researchers.

Язык: Английский

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A map of neural signals and circuits traces the logic of brain computation

Nature, Год журнала: 2025, Номер 640(8058), С. 319 - 321

Опубликована: Апрель 9, 2025

Язык: Английский

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