Changes for page Elephant Tutorials

Last modified by denker on 2025/04/09 07:02

From version 31.1
edited by denker
on 2022/06/29 11:31
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To version 42.1
edited by denker
on 2022/11/11 12:18
Change comment: There is no comment for this version

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7 7  (% style="color:#4e5f70" %)Interactive video tutorials on
8 8  neuronal data analysis using Elephant
9 9  
10 -(% style="color:#e74c3c" %)**Upcoming: CNS 2022**
10 +
11 +(% style="color:#e74c3c" %)Upcoming Sessions:
12 +
13 +(% style="color:#e74c3c" %)December 6, 2022 Intermediate data analysis in Python: Using Neo and Elephant for
14 +neural activity analysis
11 11  )))
12 12  )))
13 13  
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15 15  (((
16 16  (% class="col-xs-12 col-sm-8" %)
17 17  (((
22 +{{info}}
23 +== Intermediate Data Analysis in Python (Hybrid) ==
24 +
25 +**Using Neo and Elephant for neural activity analysis**
26 +
27 +=== Information ===
28 +
29 +Date: Tuesday, December 6, 2022
30 +
31 +Time: tba
32 +
33 +Registration & Agenda: tba
34 +
35 +
36 +{{/info}}
37 +
18 18  == A resource for kick-starting work with the Elephant library ==
19 19  
20 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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58 58  |(% style="width:300px" %)GPFA|(% style="width:267px" %)Simon Essink|(% style="width:626px" %)Extract low-dimensional rate trajectories from the population spike activity.
59 59  |(% 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.
60 60  
61 -
81 +== List of past events ==
82 +
83 +== ==
84 +
85 +* (((
86 +November 10, 2022** Simulate with EBRAINS (Online)**
87 +Agenda: https:~/~/flagship.kip.uni-heidelberg.de/jss/HBPm?m=showAgenda&meetingID=242
62 62  )))
89 +)))
63 63  
64 64  
92 +
65 65  (% class="col-xs-12 col-sm-4" %)
66 66  (((
67 67  {{box title="**Contents**"}}