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| Hauptseite > Publikationsdatenbank > Designing reproducible analysis workflows for experimental and simulated activity using Elephant |
| Hauptseite > Publikationsdatenbank > Designing reproducible analysis workflows for experimental and simulated activity using Elephant |
| Poster (Invited) | FZJ-2020-01105 |
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2020
Please use a persistent id in citations: http://hdl.handle.net/2128/24387
Abstract: Neuroscientists have a diversified and constantly growing repertoire of methods to analyze neuronal activity data. Moreover, the growing availability of open data sets containing neuronal activity data puts modelers in a position to perform a more in-depth validation of their models (e.g., [1]) based on the statistical descriptions of the activity observed in experiments. However, the increased possibilities also come at the cost of higher complexity of such analysis and validation processes. Here, we showcase the state of HBP-enabled, tool-based workflow solutions that implement rigorous and well-defined data handling and analysis, as well as model validation schemes, for activity data such as spike trains or local field potentials. We demonstrate methods for data and metadata representation, and its analysis using multiple emerging open-source software tools (e.g., [2-4]). Analysis is performed using the Electrophysiology Analysis Toolkit (Elephant, http://neuralensemble.org/elephant/) as a community-centered analysis framework for parallel, multi-scale activity data developed within the HBP, while validation is carried out using the HBP validation framework, and in particular the NetworkUnit library [5-7]. The interplay between the tools is showcased by integrating them into a robust workflow solution. Concrete examples on how to utilize these tools for scientific discovery in conjunction with the Collaboratory and Knowledgegraph HBP infrastructure components, as well as with the snakemake workflow tool, are given in the context of the use cases of SP3 [8,9].References1. van Albada, S.J. et al. (2018). Front Neuroinf 12, 291.2. Garcia, S. et al. (2014). Front Neuroinf 8, 10.3. Zehl, L. et al. (2016). Front Neuroinf 10, 26.4. Grewe, J. et al. (2011). Front Neuroinf 5, 16.5. Gutzen, R. et al. (2018) Front Neuroinf 12, 90.6. Omar, C. et al. (2014). ICSE Companion 2014, 524–527.7. Sarma, G. P. et al. (2016). F1000 Research, 5:1946.8. Pastorelli, E. et al. (2019) Front. Syst. Neurosci 13, 339. De Bonis, G. et al. (2019) Front. Syst. Neurosci. doi: 10.3389/fnsys.2019.00070 (in press)
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