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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2017-09-01
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| Series: | eLife |
| Subjects: | |
| Online Access: | https://elifesciences.org/articles/27702 |
| _version_ | 1811253211040841728 |
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| author | Ashesh K Dhawale Rajesh Poddar Steffen BE Wolff Valentin A Normand Evi Kopelowitz Bence P Ölveczky |
| author_facet | Ashesh K Dhawale Rajesh Poddar Steffen BE Wolff Valentin A Normand Evi Kopelowitz Bence P Ölveczky |
| author_sort | Ashesh K Dhawale |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-12T16:46:29Z |
| format | Article |
| id | doaj.art-d41b7bbc3b724c3c9a2f29afbd3a8e21 |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-04-12T16:46:29Z |
| publishDate | 2017-09-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-d41b7bbc3b724c3c9a2f29afbd3a8e212022-12-22T03:24:33ZengeLife Sciences Publications LtdeLife2050-084X2017-09-01610.7554/eLife.27702Automated long-term recording and analysis of neural activity in behaving animalsAshesh K Dhawale0https://orcid.org/0000-0001-7438-1115Rajesh Poddar1Steffen BE Wolff2Valentin A Normand3Evi Kopelowitz4Bence P Ölveczky5https://orcid.org/0000-0003-2499-2705Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States; Center for Brain Science, Harvard University, Cambridge, United StatesDepartment of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States; Center for Brain Science, Harvard University, Cambridge, United StatesDepartment of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States; Center for Brain Science, Harvard University, Cambridge, United StatesDepartment of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States; Center for Brain Science, Harvard University, Cambridge, United StatesDepartment of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States; Center for Brain Science, Harvard University, Cambridge, United StatesDepartment of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States; Center for Brain Science, Harvard University, Cambridge, United StatesAddressing 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.https://elifesciences.org/articles/27702neural recordingssystems neurosciencebehavior |
| spellingShingle | Ashesh K Dhawale Rajesh Poddar Steffen BE Wolff Valentin A Normand Evi Kopelowitz Bence P Ölveczky Automated long-term recording and analysis of neural activity in behaving animals eLife neural recordings systems neuroscience behavior |
| 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 | neural recordings systems neuroscience behavior |
| url | https://elifesciences.org/articles/27702 |
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