Summary

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

Details

Page properties

Content

...	...	@@ -7,11 +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 Sessions:
12		-
13		-(% style="color:#e74c3c" %)December 6, 2022 Intermediate data analysis in Python: Using Neo and Elephant for
14		-neural activity analysis
	10	+(% style="color:#e74c3c" %)**Upcoming: CNS 2022**
15	15	)))
16	16	)))
17	17
...	...	@@ -19,21 +19,6 @@
19	19	(((
20	20	(% class="col-xs-12 col-sm-8" %)
21	21	(((
22		-{{info}}
23		-(% class="wikigeneratedid wikigeneratedheader" id="HIntermediateDataAnalysisinPython28Hybrid29" %)
24		-**Intermediate Data Analysis in Python (Hybrid)**
25		-
26		-**Session: Using Neo and Elephant for neural activity analysis**
27		-
28		-Date: Tuesday, December 6, 2022
29		-
30		-Time: tba
31		-
32		-Registration & Agenda: tba
33		-
34		-
35		-{{/info}}
36		-
37	37	== A resource for kick-starting work with the Elephant library ==
38	38
39	39	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.
...	...	@@ -77,17 +77,10 @@
77	77	|(% style="width:300px" %)GPFA|(% style="width:267px" %)Simon Essink|(% style="width:626px" %)Extract low-dimensional rate trajectories from the population spike activity.
78	78	|(% 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.
79	79
80		-== List of past events ==
81		-
82		-
83		-* (((
84		-November 10, 2022** Simulate with EBRAINS (Online)**
85		-Agenda: https:~/~/flagship.kip.uni-heidelberg.de/jss/HBPm?m=showAgenda&meetingID=242
	61	+
86	86	)))
87		-)))
88	88
89	89
90		-
91	91	(% class="col-xs-12 col-sm-4" %)
92	92	(((
93	93	{{box title="**Contents**"}}