Changes for page Elephant Tutorials

Last modified by denker on 2025/04/09 07:02
From version 3.1
edited by denker
on 2021/02/01 08:51
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To version 7.2
edited by denker
on 2021/02/01 17:30
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Summary

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2 2  (((
3 3  (% class="container" %)
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5 -= My Collab's Extended Title =
5 += Elephant Tutorial Space[[image:https://elephant.readthedocs.io/en/latest/_static/elephant_logo_sidebar.png||alt="Elephant logo" style="float:right"]] =
6 6  
7 -My collab's subtitle
7 +Video tutorials on neuronal data analysis using Elephant
8 8  )))
9 9  )))
10 10  
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12 12  (((
13 13  (% class="col-xs-12 col-sm-8" %)
14 14  (((
15 -= What can I find here? =
15 +== A resource for kick-starting work with the Elephant library ==
16 16  
17 -* Notice how the table of contents on the right
18 -* is automatically updated
19 -* to hold this page's headers
17 +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.
20 20  
21 -= Who has access? =
19 +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.
22 22  
23 -Describe the audience of this collab.
21 +
22 +== Access to the tutorials ==
23 +
24 +To access the tutorials, check out the drive space of this Collab. Video are available for download in the (% style="color:#f39c12" %)videos(%%) section, whereas the corresponding Jupyter notebooks are available in the (% style="color:#f39c12" %)notebooks(%%) folder. Notebooks can either be run directly on the EBRAINS Collaboratory (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.
25 +
26 +=== Execution on the EBRAINS Collaboratory ===
27 +
28 +* Open the EBRAINS lab by selecting the corresponding (% style="color:#f39c12" %)Lab(%%) menu entry on the left.
29 +//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.//
30 +* In the lab, navigate to a particular notebook and open and execute it. To save changes you may want to make, please create a copy of the notebook in a Collab of your own (i.e., where you have write permissions).
31 +
32 +=== Local execution ===
33 +
34 +* Open the EBRAINS drive by selecting the corresponding (% style="color:#f39c12" %)Drive(%%) menu entry on the left.
35 +* Download a particular notebook and the (% style="color:#f39c12" %)requirements.txt(%%) to your computer.
36 +* Create a Python environment based on the (% style="color:#f39c12" %)requirements.txt(%%) file. The details will depend on your particular Python setup.
37 +
38 +== List of available tutorials ==
39 +
40 +
41 +(% style="margin-right:auto" %)
42 +|=(% style="width: 300px;" %)Tutorial|=(% style="width: 267px;" %)Hosts and Authors|=(% style="width: 626px;" %)Content
43 +|(% style="width:300px" %)LFP_analysis|(% style="width:267px" %)Robin Gutzen|(% style="width:626px" %)Apply basic LFP analysis techniques, such as power spectra.
44 +|(% style="width:300px" %)Spike_analysis|(% style="width:267px" %)(((
45 +Cristiano Köhler
46 +Alexander Kleinjohann
47 +)))|(% style="width:626px" %)Perform basic statistical analysis of spike trains from rate profiles to pair-wise correlations.
48 +|(% style="width:300px" %)Spatio-temporal_spike_patterns|(% style="width:267px" %)Regimatas Jurkus
49 +Alessandra Stella|(% style="width:626px" %)Highlights two methods for detecting hidden spatio-temporal patterns in spike data.
50 +|(% style="width:300px" %)GPFA|(% style="width:267px" %)Simon Essink|(% style="width:626px" %)Extract low-dimensional rate trajectories from the population spike activity.
51 +|(% 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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53 +
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