Wiki source code of Elephant Tutorials

Version 5.2 by denker on 2021/02/01 17:15
Hide last authors
denker 1.1 1 (% class="jumbotron" %)
2 (((
3 (% class="container" %)
4 (((
denker 4.2 5 = Elephant Tutorial Space =
denker 1.1 6
denker 4.2 7 Video tutorials on data analysis using Elephant
denker 1.1 8 )))
9 )))
10
11 (% class="row" %)
12 (((
13 (% class="col-xs-12 col-sm-8" %)
14 (((
denker 5.1 15 == A resource for kick-starting work with the Elephant library ==
denker 1.1 16
denker 5.2 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.
denker 1.1 18
denker 5.1 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.
denker 4.2 20
denker 5.2 21 == Access to the tutorials ==
denker 1.1 22
denker 5.1 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.
denker 5.2 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.
38
39
denker 1.1 40 )))
41
42
43 (% class="col-xs-12 col-sm-4" %)
44 (((
45 {{box title="**Contents**"}}
46 {{toc/}}
47 {{/box}}
48
49
50 )))
51 )))