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Automated long-term recording and analysis of neural activity in behaving animals
Addressing how neural circuits underlie behavior is routinely done by measuring electrical activity from single neurons in experimental sessions. While such recordings yield snapshots of neural dynamics during specified tasks, they are ill-suited for tracking single-unit activity over longer timesca...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5619984/ https://www.ncbi.nlm.nih.gov/pubmed/28885141 http://dx.doi.org/10.7554/eLife.27702 |
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author | Dhawale, Ashesh K Poddar, Rajesh Wolff, Steffen BE Normand, Valentin A Kopelowitz, Evi Ölveczky, Bence P |
author_facet | Dhawale, Ashesh K Poddar, Rajesh Wolff, Steffen BE Normand, Valentin A Kopelowitz, Evi Ölveczky, Bence P |
author_sort | Dhawale, Ashesh K |
collection | PubMed |
description | Addressing how neural circuits underlie behavior is routinely done by measuring electrical activity from single neurons in experimental sessions. While such recordings yield snapshots of neural dynamics during specified tasks, they are ill-suited for tracking single-unit activity over longer timescales relevant for most developmental and learning processes, or for capturing neural dynamics across different behavioral states. Here we describe an automated platform for continuous long-term recordings of neural activity and behavior in freely moving rodents. An unsupervised algorithm identifies and tracks the activity of single units over weeks of recording, dramatically simplifying the analysis of large datasets. Months-long recordings from motor cortex and striatum made and analyzed with our system revealed remarkable stability in basic neuronal properties, such as firing rates and inter-spike interval distributions. Interneuronal correlations and the representation of different movements and behaviors were similarly stable. This establishes the feasibility of high-throughput long-term extracellular recordings in behaving animals. |
format | Online Article Text |
id | pubmed-5619984 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-56199842017-09-29 Automated long-term recording and analysis of neural activity in behaving animals Dhawale, Ashesh K Poddar, Rajesh Wolff, Steffen BE Normand, Valentin A Kopelowitz, Evi Ölveczky, Bence P eLife Neuroscience Addressing how neural circuits underlie behavior is routinely done by measuring electrical activity from single neurons in experimental sessions. While such recordings yield snapshots of neural dynamics during specified tasks, they are ill-suited for tracking single-unit activity over longer timescales relevant for most developmental and learning processes, or for capturing neural dynamics across different behavioral states. Here we describe an automated platform for continuous long-term recordings of neural activity and behavior in freely moving rodents. An unsupervised algorithm identifies and tracks the activity of single units over weeks of recording, dramatically simplifying the analysis of large datasets. Months-long recordings from motor cortex and striatum made and analyzed with our system revealed remarkable stability in basic neuronal properties, such as firing rates and inter-spike interval distributions. Interneuronal correlations and the representation of different movements and behaviors were similarly stable. This establishes the feasibility of high-throughput long-term extracellular recordings in behaving animals. eLife Sciences Publications, Ltd 2017-09-08 /pmc/articles/PMC5619984/ /pubmed/28885141 http://dx.doi.org/10.7554/eLife.27702 Text en © 2017, Dhawale et al http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Neuroscience Dhawale, Ashesh K Poddar, Rajesh Wolff, Steffen BE Normand, Valentin A Kopelowitz, Evi Ölveczky, Bence P Automated long-term recording and analysis of neural activity in behaving animals |
title | Automated long-term recording and analysis of neural activity in behaving animals |
title_full | Automated long-term recording and analysis of neural activity in behaving animals |
title_fullStr | Automated long-term recording and analysis of neural activity in behaving animals |
title_full_unstemmed | Automated long-term recording and analysis of neural activity in behaving animals |
title_short | Automated long-term recording and analysis of neural activity in behaving animals |
title_sort | automated long-term recording and analysis of neural activity in behaving animals |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5619984/ https://www.ncbi.nlm.nih.gov/pubmed/28885141 http://dx.doi.org/10.7554/eLife.27702 |
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