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Real-Time Selective Markerless Tracking of Forepaws of Head Fixed Mice Using Deep Neural Networks
Here, we describe a system capable of tracking specific mouse paw movements at high frame rates (70.17 Hz) with a high level of accuracy (mean = 0.95, SD < 0.01). Short-latency markerless tracking of specific body parts opens up the possibility of manipulating motor feedback. We present a softwar...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Society for Neuroscience
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7307631/ https://www.ncbi.nlm.nih.gov/pubmed/32409507 http://dx.doi.org/10.1523/ENEURO.0096-20.2020 |
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author | Forys, Brandon J. Xiao, Dongsheng Gupta, Pankaj Murphy, Timothy H. |
author_facet | Forys, Brandon J. Xiao, Dongsheng Gupta, Pankaj Murphy, Timothy H. |
author_sort | Forys, Brandon J. |
collection | PubMed |
description | Here, we describe a system capable of tracking specific mouse paw movements at high frame rates (70.17 Hz) with a high level of accuracy (mean = 0.95, SD < 0.01). Short-latency markerless tracking of specific body parts opens up the possibility of manipulating motor feedback. We present a software and hardware scheme built on DeepLabCut—a robust movement-tracking deep neural network framework—which enables real-time estimation of paw and digit movements of mice. Using this approach, we demonstrate movement-generated feedback by triggering a USB-GPIO (general-purpose input/output)-controlled LED when the movement of one paw, but not the other, selectively exceeds a preset threshold. The mean time delay between paw movement initiation and LED flash was 44.41 ms (SD = 36.39 ms), a latency sufficient for applying behaviorally triggered feedback. We adapt DeepLabCut for real-time tracking as an open-source package we term DeepCut2RealTime. The ability of the package to rapidly assess animal behavior was demonstrated by reinforcing specific movements within water-restricted, head-fixed mice. This system could inform future work on a behaviorally triggered “closed loop” brain–machine interface that could reinforce behaviors or deliver feedback to brain regions based on prespecified body movements. |
format | Online Article Text |
id | pubmed-7307631 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Society for Neuroscience |
record_format | MEDLINE/PubMed |
spelling | pubmed-73076312020-06-23 Real-Time Selective Markerless Tracking of Forepaws of Head Fixed Mice Using Deep Neural Networks Forys, Brandon J. Xiao, Dongsheng Gupta, Pankaj Murphy, Timothy H. eNeuro Research Article: Methods/New Tools Here, we describe a system capable of tracking specific mouse paw movements at high frame rates (70.17 Hz) with a high level of accuracy (mean = 0.95, SD < 0.01). Short-latency markerless tracking of specific body parts opens up the possibility of manipulating motor feedback. We present a software and hardware scheme built on DeepLabCut—a robust movement-tracking deep neural network framework—which enables real-time estimation of paw and digit movements of mice. Using this approach, we demonstrate movement-generated feedback by triggering a USB-GPIO (general-purpose input/output)-controlled LED when the movement of one paw, but not the other, selectively exceeds a preset threshold. The mean time delay between paw movement initiation and LED flash was 44.41 ms (SD = 36.39 ms), a latency sufficient for applying behaviorally triggered feedback. We adapt DeepLabCut for real-time tracking as an open-source package we term DeepCut2RealTime. The ability of the package to rapidly assess animal behavior was demonstrated by reinforcing specific movements within water-restricted, head-fixed mice. This system could inform future work on a behaviorally triggered “closed loop” brain–machine interface that could reinforce behaviors or deliver feedback to brain regions based on prespecified body movements. Society for Neuroscience 2020-06-05 /pmc/articles/PMC7307631/ /pubmed/32409507 http://dx.doi.org/10.1523/ENEURO.0096-20.2020 Text en Copyright © 2020 Forys et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article 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 that the original work is properly attributed. |
spellingShingle | Research Article: Methods/New Tools Forys, Brandon J. Xiao, Dongsheng Gupta, Pankaj Murphy, Timothy H. Real-Time Selective Markerless Tracking of Forepaws of Head Fixed Mice Using Deep Neural Networks |
title | Real-Time Selective Markerless Tracking of Forepaws of Head Fixed Mice Using Deep Neural Networks |
title_full | Real-Time Selective Markerless Tracking of Forepaws of Head Fixed Mice Using Deep Neural Networks |
title_fullStr | Real-Time Selective Markerless Tracking of Forepaws of Head Fixed Mice Using Deep Neural Networks |
title_full_unstemmed | Real-Time Selective Markerless Tracking of Forepaws of Head Fixed Mice Using Deep Neural Networks |
title_short | Real-Time Selective Markerless Tracking of Forepaws of Head Fixed Mice Using Deep Neural Networks |
title_sort | real-time selective markerless tracking of forepaws of head fixed mice using deep neural networks |
topic | Research Article: Methods/New Tools |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7307631/ https://www.ncbi.nlm.nih.gov/pubmed/32409507 http://dx.doi.org/10.1523/ENEURO.0096-20.2020 |
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