Summary

• Page properties (1 modified, 0 added, 0 removed)

Details

Page properties

Content

...	...	@@ -7,10 +7,7 @@
7	7	(% style="color:#4e5f70" %)Interactive video tutorials on
8	8	neuronal data analysis using Elephant
9	9
10		-
11		-(% style="color:#e74c3c" %)Upcoming training event:
12		-
13		-(% style="color:#e74c3c" %)December 6, 2022 Intermediate data analysis in Python: Using Neo and Elephant for neural activity analysis
	10	+(% style="color:#e74c3c" %)**~-~- in beta  ~-~-**
14	14	)))
15	15	)))
16	16
...	...	@@ -18,18 +18,6 @@
18	18	(((
19	19	(% class="col-xs-12 col-sm-8" %)
20	20	(((
21		-== Upcoming training events ==
22		-
23		-{{info}}
24		-**Intermediate Data Analysis in Python (Hybrid)**
25		-**Session: Using Neo and Elephant for neural activity analysis**
26		-Date: Tuesday, December 6, 2022
27		-Time: tba
28		-Registration & Agenda: tba
29		-
30		-
31		-{{/info}}
32		-
33	33	== A resource for kick-starting work with the Elephant library ==
34	34
35	35	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.
...	...	@@ -36,9 +36,7 @@
36	36
37	37	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.
38	38
39		-In addition, tutorials presented at various workshops and schools are collected in this collab.
40	40
41		-
42	42	== Access to the tutorials ==
43	43
44	44	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.
...	...	@@ -73,15 +73,10 @@
73	73	|(% style="width:300px" %)GPFA|(% style="width:267px" %)Simon Essink|(% style="width:626px" %)Extract low-dimensional rate trajectories from the population spike activity.
74	74	|(% 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.
75	75
76		-== List of past events ==
77		-
78		-
79		-* (((
80		-November 10, 2022** Simulate with EBRAINS (Online)**
81		-Agenda: https:~/~/flagship.kip.uni-heidelberg.de/jss/HBPm?m=showAgenda&meetingID=242
	59	+
82	82	)))
83		-)))
84	84
	62	+
85	85	(% class="col-xs-12 col-sm-4" %)
86	86	(((
87	87	{{box title="**Contents**"}}