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Large Volume Electron Microscopy and Neural Microcircuit Analysis

One recent technical innovation in neuroscience is microcircuit analysis using three-dimensional reconstructions of neural elements with a large volume Electron microscopy (EM) data set. Large-scale data sets are acquired with newly-developed electron microscope systems such as automated tape-collec...

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Autores principales: Kubota, Yoshiyuki, Sohn, Jaerin, Kawaguchi, Yasuo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6240581/
https://www.ncbi.nlm.nih.gov/pubmed/30483066
http://dx.doi.org/10.3389/fncir.2018.00098
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author Kubota, Yoshiyuki
Sohn, Jaerin
Kawaguchi, Yasuo
author_facet Kubota, Yoshiyuki
Sohn, Jaerin
Kawaguchi, Yasuo
author_sort Kubota, Yoshiyuki
collection PubMed
description One recent technical innovation in neuroscience is microcircuit analysis using three-dimensional reconstructions of neural elements with a large volume Electron microscopy (EM) data set. Large-scale data sets are acquired with newly-developed electron microscope systems such as automated tape-collecting ultramicrotomy (ATUM) with scanning EM (SEM), serial block-face EM (SBEM) and focused ion beam-SEM (FIB-SEM). Currently, projects are also underway to develop computer applications for the registration and segmentation of the serially-captured electron micrographs that are suitable for analyzing large volume EM data sets thoroughly and efficiently. The analysis of large volume data sets can bring innovative research results. These recently available techniques promote our understanding of the functional architecture of the brain.
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spelling pubmed-62405812018-11-27 Large Volume Electron Microscopy and Neural Microcircuit Analysis Kubota, Yoshiyuki Sohn, Jaerin Kawaguchi, Yasuo Front Neural Circuits Neuroscience One recent technical innovation in neuroscience is microcircuit analysis using three-dimensional reconstructions of neural elements with a large volume Electron microscopy (EM) data set. Large-scale data sets are acquired with newly-developed electron microscope systems such as automated tape-collecting ultramicrotomy (ATUM) with scanning EM (SEM), serial block-face EM (SBEM) and focused ion beam-SEM (FIB-SEM). Currently, projects are also underway to develop computer applications for the registration and segmentation of the serially-captured electron micrographs that are suitable for analyzing large volume EM data sets thoroughly and efficiently. The analysis of large volume data sets can bring innovative research results. These recently available techniques promote our understanding of the functional architecture of the brain. Frontiers Media S.A. 2018-11-12 /pmc/articles/PMC6240581/ /pubmed/30483066 http://dx.doi.org/10.3389/fncir.2018.00098 Text en Copyright © 2018 Kubota, Sohn and Kawaguchi. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Kubota, Yoshiyuki
Sohn, Jaerin
Kawaguchi, Yasuo
Large Volume Electron Microscopy and Neural Microcircuit Analysis
title Large Volume Electron Microscopy and Neural Microcircuit Analysis
title_full Large Volume Electron Microscopy and Neural Microcircuit Analysis
title_fullStr Large Volume Electron Microscopy and Neural Microcircuit Analysis
title_full_unstemmed Large Volume Electron Microscopy and Neural Microcircuit Analysis
title_short Large Volume Electron Microscopy and Neural Microcircuit Analysis
title_sort large volume electron microscopy and neural microcircuit analysis
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6240581/
https://www.ncbi.nlm.nih.gov/pubmed/30483066
http://dx.doi.org/10.3389/fncir.2018.00098
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