| Autor: |
Venus N. Sherathiya, Michael D. Schaid, Jillian L. Seiler, Gabriela C. Lopez, Talia N. Lerner |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Scientific Reports, Vol 11, Iss 1, Pp 1-9 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2045-2322 |
| DOI: |
10.1038/s41598-021-03626-9 |
| Popis: |
Abstract Fiber photometry (FP) is an adaptable method for recording in vivo neural activity in freely behaving animals. It has become a popular tool in neuroscience due to its ease of use, low cost, the ability to combine FP with freely moving behavior, among other advantages. However, analysis of FP data can be challenging for new users, especially those with a limited programming background. Here, we present Guided Photometry Analysis in Python (GuPPy), a free and open-source FP analysis tool. GuPPy is designed to operate across computing platforms and can accept data from a variety of FP data acquisition systems. The program presents users with a set of graphic user interfaces (GUIs) to load data and provide input parameters. Graphs are produced that can be easily exported for integration into scientific figures. As an open-source tool, GuPPy can be modified by users with knowledge of Python to fit their specific needs. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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