Changes for page Elephant Tutorials

Last modified by denker on 2025/04/09 07:02
From version 5.2
edited by denker
on 2021/02/01 17:15
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To version 1.1
edited by denker
on 2021/02/01 08:51
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2 2  (((
3 3  (% class="container" %)
4 4  (((
5 -= Elephant Tutorial Space =
5 += My Collab's Extended Title =
6 6  
7 -Video tutorials on data analysis using Elephant
7 +My collab's subtitle
8 8  )))
9 9  )))
10 10  
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12 12  (((
13 13  (% class="col-xs-12 col-sm-8" %)
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15 -== A resource for kick-starting work with the Elephant library ==
15 += What can I find here? =
16 16  
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.
17 +* Notice how the table of contents on the right
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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.
21 += Who has access? =
20 20  
21 -== Access to the tutorials ==
22 -
23 -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 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.
24 -
25 -== List of available tutorials ==
26 -
27 -(% style="margin-right:auto" %)
28 -|=(% style="width: 300px;" %)Tutorial|=(% style="width: 267px;" %)Hosts and Authors|=(% style="width: 626px;" %)Content
29 -|(% style="width:300px" %)Elephant_Tutorial_-_LFP_analysis|(% style="width:267px" %)Robin Gutzen|(% style="width:626px" %)Apply basic LFP analysis techniques, such as power spectra.
30 -|(% style="width:300px" %)Elephant_Tutorial_-_Spike_analysis|(% style="width:267px" %)(((
31 -Cristiano Köhler
32 -Alexander Kleinjohann
33 -)))|(% style="width:626px" %)Perform basic statistical analysis of spike trains from rate profiles to pair-wise correlations.
34 -|(% style="width:300px" %)Elephant_Tutorial_-_Spatio-temporal_spike_patterns|(% style="width:267px" %)Regimatas Jurkus
35 -Alessandra Stella|(% style="width:626px" %)Highlights two methods for detecting hidden spatio-temporal patterns in spike data.
36 -|(% style="width:300px" %)Elephant_Tutorial_-_GPFA|(% style="width:267px" %)Simon Essink|(% style="width:626px" %)Extract low-dimensional rate trajectories from the population spike activity.
37 -|(% style="width:300px" %)Elephant_Tutorial_-_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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39 -
23 +Describe the audience of this collab.
40 40  )))
41 41  
42 42  
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