Wiki source code of Elephant Tutorials
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| 5 | = (% style="color:#f39c12" %)Elephant Tutorial Space[[image:https://elephant.readthedocs.io/en/latest/_static/elephant_logo_sidebar.png||alt="Elephant logo" style="float:right"]](%%) = | ||
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| 7 | (% style="color:#4e5f70" %)Interactive video tutorials on | ||
| 8 | neuronal data analysis using Elephant | ||
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| 10 | (% style="color:#e74c3c" %)**~-~- in beta ~-~-** | ||
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| 18 | == A resource for kick-starting work with the Elephant library == | ||
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| 20 | The Python library [[Electrophysiology Analysis Toolkit (Elephant)>>https://python-elephant.org||rel="noopener noreferrer" target="_blank"]] provides tools for the analysis of neuronal activity data, such as spike trains, local field potentials and intracellular data. In addition to providing a platform for sharing analysis codes from different laboratories, Elephant provides a consistent and homogeneous framework for data analysis, built on a modular foundation. The underlying data model is the Neo library, a framework which easily captures a wide range of neuronal data types and methods, including dozens of file formats and network simulation tools. A common data description, as provided by the Neo library, is essential for developing interoperable analysis workflows. | ||
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| 22 | In this collaborative space, we provide hands on video tutorials based on Jupyter notebooks that showcase various types of data analysis, from simple to advanced. Most notebooks are based on a common dataset published at [[https:~~/~~/gin.g-node.org/INT/multielectrode_grasp>>https://gin.g-node.org/INT/multielectrode_grasp]] (for details cf. Brochier et al (2018) Scientific Data 5, 180055. [[https:~~/~~/doi.org/10.1038/sdata.2018.55>>url:https://doi.org/10.1038/sdata.2018.55]]). All video tutorials are approximately 30 minutes in length. | ||
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| 25 | == Access to the tutorials == | ||
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| 27 | To access the tutorials, check out the drive space of this collab. The Jupyter notebooks are available in the (% style="color:#f39c12" %)notebooks(%%) folder, and links to the (% style="color:#f39c12" %)videos(%%) are embedded within each notebook. Notebooks can either be run directly on the EBRAINS Collaboratory's JupyterLab service (currently limited to HBP-affiliated members), or downloaded and run locally. For local execution, please use the provided (% style="color:#f39c12" %)requirements.txt(%%) file to generate an appropriate Python environment. | ||
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| 29 | === Execution on the EBRAINS Collaboratory === | ||
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| 31 | * Open the EBRAINS lab by selecting the corresponding (% style="color:#f39c12" %)Lab(%%) menu entry on the left. | ||
| 32 | //Please note: JupyterLab functionality is currently in beta and not yet available to non-HBP-affiliated Collaboratory users. Please check back in the near future.// | ||
| 33 | * In the lab, navigate to a particular notebook and open and execute it. | ||
| 34 | ** Please note that in some instances, you may need to restart the kernel for the notebooks to run (e.g., when new packages are installed by the notebook, or in case of low memory). | ||
| 35 | ** To save changes you may want to make to a notebook, please create a copy of the notebook in a collab of your own (i.e., a collab where you have write permissions). | ||
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| 37 | === Local execution === | ||
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| 39 | * Open the EBRAINS drive by selecting the corresponding (% style="color:#f39c12" %)Drive(%%) menu entry on the left. | ||
| 40 | * Download a particular notebook, the datasets, and the (% style="color:#f39c12" %)requirements.txt(%%) to your computer. | ||
| 41 | * Create a Python environment based on the (% style="color:#f39c12" %)requirements.txt(%%) file. The details will depend on your particular Python setup. | ||
| 42 | * Likely, path names to data files must be adjusted accordingly. | ||
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| 44 | == List of available tutorials == | ||
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| 48 | |=(% style="width: 300px;" %)Tutorial|=(% style="width: 267px;" %)Hosts and Authors|=(% style="width: 626px;" %)Content | ||
| 49 | |(% style="width:300px" %)LFP_analysis|(% style="width:267px" %)Robin Gutzen|(% style="width:626px" %)Apply basic LFP analysis techniques, such as power spectra. | ||
| 50 | |(% style="width:300px" %)Spike_analysis|(% style="width:267px" %)((( | ||
| 51 | Cristiano Köhler | ||
| 52 | Alexander Kleinjohann | ||
| 53 | )))|(% style="width:626px" %)Perform basic statistical analysis of spike trains from rate profiles to pair-wise correlations. | ||
| 54 | |(% style="width:300px" %)Spatio-temporal_spike_patterns|(% style="width:267px" %)Regimatas Jurkus | ||
| 55 | Alessandra Stella|(% style="width:626px" %)Highlights two methods for detecting hidden spatio-temporal patterns in spike data. | ||
| 56 | |(% style="width:300px" %)GPFA|(% style="width:267px" %)Simon Essink|(% style="width:626px" %)Extract low-dimensional rate trajectories from the population spike activity. | ||
| 57 | |(% style="width:300px" %)Surrogate_techniques|(% style="width:267px" %)Peter Bouss|(% style="width:626px" %)Learn how to use different surrogate methods for spike trains to assist in formulating statistical null hypotheses in the presence of non-stationarity. | ||
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| 65 | {{box title="**Contents**"}} | ||
| 66 | {{toc/}} | ||
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