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Showing 1–7 of 7 results

Advanced filters: Author: Sharmishtaa Seshamani Clear advanced filters

The synaptic architecture of layer 5 thick tufted excitatory neurons in mouse visual cortex

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.

Agnes L. Bodor
Casey M. Schneider-Mizell
Nuno Maçarico da Costa
ResearchOpen Access28 Jul 2025
Nature Neuroscience

Volume: 28, P: 1704-1715
Author Correction: Inhibitory specificity from a connectomic census of mouse visual cortex

Casey M. Schneider-Mizell
Agnes L. Bodor
Nuno Maçarico da Costa
Amendments and CorrectionsOpen Access19 May 2025
Nature

Volume: 642, P: E9
Inhibitory specificity from a connectomic census of mouse visual cortex

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

Casey M. Schneider-Mizell
Agnes L. Bodor
Nuno Maçarico da Costa
ResearchOpen Access09 Apr 2025
Nature

Volume: 640, P: 448-458
Connectomics of predicted Sst transcriptomic types in mouse visual cortex

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

Clare R. Gamlin
Casey M. Schneider-Mizell
Staci A. Sorensen
ResearchOpen Access09 Apr 2025
Nature

Volume: 640, P: 497-505
Perisomatic ultrastructure efficiently classifies cells in mouse cortex

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

Leila Elabbady
Sharmishtaa Seshamani
Forrest Collman
ResearchOpen Access09 Apr 2025
Nature

Volume: 640, P: 478-486
A community-developed open-source computational ecosystem for big neuro data

Joshua T. Vogelstein
Eric Perlman
Randal Burns
Correspondence30 Oct 2018
Nature Methods

Volume: 15, P: 846-847
Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy

Convolutional neural networks enable prediction of fluorescently labeled structures from three-dimensional time-lapse transmitted-light images. Applications include multiplexed long time-lapse imaging and prediction of fluorescence in electron micrographs.

Chawin Ounkomol
Sharmishtaa Seshamani
Gregory R. Johnson
Research17 Sept 2018
Nature Methods

Volume: 15, P: 917-920