MICrONS Explorer: A virtual observatory of the cortex
The Machine Intelligence from Cortical Networks (MICrONS) program seeks to revolutionize machine learning by reverse-engineering the algorithms of the brain. It is an ambitious program to map the function and connectivity of cortical circuits, using high throughput imaging technologies, with the goal of providing insights into the computational principles that underlie cortical function in order to advance the next generation of machine learning algorithms.
This website serves as a data portal to release connectivity and functional imaging data collected by a consortium of laboratories led by groups at the Allen Institute for Brain Science, Princeton University, and Baylor College of Medicine, with support from a broad array of teams, coordinated and funded by the IARPA MICrONS program. These data include large scale electron microscopy based reconstructions of cortical circuitry from mouse visual cortex, with corresponding functional imaging data from those same neurons.
Have a Scientific Request? Check out the Virtual Observatory of the Cortex (VORTEX) project, a BRAIN Initiative funded program to bring the MICrONS dataset to the research community. Access proofreading resources to answer your scientific questions.
Publications from the MICrONS Datasets
Primary Publications
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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.
DOI 10.1038/s41586-025-08790-w
The MICrONS Consortium
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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.
DOI 10.1038/s41593-025-02004-2
Agnes L. Bodor, Casey M. Schneider-Mizell, Chi Zhang … Nuno Maçarico da Costa
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Using volumetric electron microscopy, the authors map and analyze the structure of cortical inhibition with synaptic resolution across a column of visual cortex.
DOI 10.1038/s41586-024-07780-8
Casey M. Schneider-Mizell, Agnes L. Bodor … Nuno Maçarico da Costa
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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.
DOI 10.1038/s41586-025-08840-3
Zhuokun Ding, Paul G. Fahey …Andreas S. Tolias
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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.
DOI 10.1038/s41586-025-08829-y
Eric Y. Wang, Paul G. Fahey …Andreas S. Tolias
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An analysis demonstrates that quantitative measurements of perisomatic ultrastructure features of neurons can be used to categorize them into cell types.
DOI 10.1038/s41586-024-07765-7
Leila Elabbady, Sharmishtaa Seshamani, Forrest Collman
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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.
DOI 10.1038/s41586-025-08805-6
Clare R. Gamlin, Casey M. Schneider-Mizell … Staci A. Sorensen
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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.
Marissa A. Weis, Stelios Papadopoulos … Alexander S. Ecker
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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.
DOI 10.1038/s41586-025-08660-5
Brendan Celii, Stelios Papadopoulos … Jacob Reimer
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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.
DOI 10.1038/s41592-024-02426-z
Sven DorkenwaldCasey M. Schneider-MizellForrest Collman
Scientists present the largest, most detailed, functional wiring diagram of a mammalian brain to date—a milestone for neuroscience achieved by a global consortium of scientists. Understanding how the brain works – its parts, the way it is organized, how neurons are connected – scientists can better understand what happens when things go wrong in disease. The Machine Intelligence from Cortical Networks (MICrONS) Project took seven years and is considered one of the most challenging neuroscience experiment ever attempted. The findings are presented in a suite of ten papers published in the Nature family of journals
This video was made by Tyler Sloan, Ph.D., at @quorumetrix