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Q&A: Understanding the composition of behavior
Understanding the brain requires understanding behavior. New machine vision and learning techniques are poised to revolutionize our ability to analyze behaviors exhibited by animals in the laboratory. Here we describe one such method, Motion Sequencing (MoSeq), which combines three-dimensional (3D)...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2019
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6542027/ https://www.ncbi.nlm.nih.gov/pubmed/31142307 http://dx.doi.org/10.1186/s12915-019-0663-3 |
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author | Datta, Sandeep Robert |
author_facet | Datta, Sandeep Robert |
author_sort | Datta, Sandeep Robert |
collection | PubMed |
description | Understanding the brain requires understanding behavior. New machine vision and learning techniques are poised to revolutionize our ability to analyze behaviors exhibited by animals in the laboratory. Here we describe one such method, Motion Sequencing (MoSeq), which combines three-dimensional (3D) imaging with unsupervised machine learning techniques to identify the syllables and grammar that comprise mouse body language. This Q&A situates MoSeq within the array of novel methods currently being developed for behavioral analysis, enumerates its relative strengths and weaknesses, and describes its future trajectory. |
format | Online Article Text |
id | pubmed-6542027 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-65420272019-06-03 Q&A: Understanding the composition of behavior Datta, Sandeep Robert BMC Biol Question and Answer Understanding the brain requires understanding behavior. New machine vision and learning techniques are poised to revolutionize our ability to analyze behaviors exhibited by animals in the laboratory. Here we describe one such method, Motion Sequencing (MoSeq), which combines three-dimensional (3D) imaging with unsupervised machine learning techniques to identify the syllables and grammar that comprise mouse body language. This Q&A situates MoSeq within the array of novel methods currently being developed for behavioral analysis, enumerates its relative strengths and weaknesses, and describes its future trajectory. BioMed Central 2019-05-29 /pmc/articles/PMC6542027/ /pubmed/31142307 http://dx.doi.org/10.1186/s12915-019-0663-3 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Question and Answer Datta, Sandeep Robert Q&A: Understanding the composition of behavior |
title | Q&A: Understanding the composition of behavior |
title_full | Q&A: Understanding the composition of behavior |
title_fullStr | Q&A: Understanding the composition of behavior |
title_full_unstemmed | Q&A: Understanding the composition of behavior |
title_short | Q&A: Understanding the composition of behavior |
title_sort | q&a: understanding the composition of behavior |
topic | Question and Answer |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6542027/ https://www.ncbi.nlm.nih.gov/pubmed/31142307 http://dx.doi.org/10.1186/s12915-019-0663-3 |
work_keys_str_mv | AT dattasandeeprobert qaunderstandingthecompositionofbehavior |