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Author

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1		-XWiki.jessicamitchell
	1	+XWiki.abonard

Content

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1		-Available tutorials:
2	2
3		-=== [[Statistics of spike trains>>https://elephant.readthedocs.io/en/latest/tutorials/statistics.html||rel=" noopener noreferrer" target="_blank"]] ===
4	4
5		-//Level: beginner//
	3	+* ((( ==== **[[Beginner >>||anchor = "HBeginner-1"]]** ==== )))
6	6
7		-This notebook provides an overview of the functions provided by the elephant statistics module.
8		-=== [[ASSET: Analysis of Sequences of Synchronous Events in Massively Parallel Spike Trains>>https://elephant.readthedocs.io/en/latest/tutorials/asset.html||rel=" noopener noreferrer" target="_blank"]] ===
	5	+* ((( ==== **[[Advanced >>||anchor = "HAdvanced-1"]]** ==== )))
9	9
10		-//Level: advanced//
	7	+=== **Beginner** ===
11	11
12		-The tutorial demonstrates a method of finding patterns of synchronous spike times (synfire chains) which cannot be revealed by measuring neuronal firing rates only.
13		-=== [[Gaussian Process Factor Analysis>>https://elephant.readthedocs.io/en/latest/tutorials/gpfa.html||rel=" noopener noreferrer" target="_blank"]] ===
	9	+=== [[Statistics of spike trains>>https://elephant.readthedocs.io/en/latest/tutorials/statistics.html||rel=" noopener noreferrer" target="_blank"]] ===
14	14
15		-//Level: advanced//
	11	+**Level**: beginner(%%) **Type**: user documentation
16	16
17		-This tutorial illustrates the usage of the gpfa.GPFA() class implemented in elephant, through its applications to synthetic spike train data, of which the ground truth low-dimensional structure is known.
18		-=== [[Parallel>>https://elephant.readthedocs.io/en/latest/tutorials/parallel.html||rel=" noopener noreferrer" target="_blank"]] ===
	13	+This notebook provides an overview of the functions provided by the elephant statistics module.
	14	+=== **Advanced** ===
19	19
20		-//Level: advanced//
21		-
22		-elephant.parallel module provides a simple interface to parallelize multiple calls to any user-specified function.
23	23	=== [[Spike Pattern Detection and Evaluation (SPADE)>>https://elephant.readthedocs.io/en/latest/tutorials/spade.html||rel=" noopener noreferrer" target="_blank"]] ===
24	24
25		-//Level: advanced//
	18	+**Level**: advanced(%%) **Type**: user documentation
26	26
27	27	SPADE is a method to detect repeated spatio-temporal activity patterns in parallel spike train data that occur in excess to chance expectation. In this tutorial, we will use SPADE to detect the simplest type of such patterns, synchronous events that are found across a subset of the neurons considered.
28	28	=== [[The Unitary Events Analysis>>https://elephant.readthedocs.io/en/latest/tutorials/unitary_event_analysis.html||rel=" noopener noreferrer" target="_blank"]] ===
29	29
30		-//Level: advanced//
	23	+**Level**: advanced(%%) **Type**: user documentation
31	31
32		-The analysis detects coordinated spiking activity that occurs significantly more often than predicted by the firing rates of neurons alone. It’s superior to simple statistics.
	25	+The Unitary Event analysis detects coordinated spiking activity that occurs significantly more often than predicted by the firing rates of neurons alone. It’s therefore superior to simple statistics. This tutorial will show you how to use this analysis in Elephant.
	26	+=== [[Gaussian Process Factor Analysis (GPFA)>>https://elephant.readthedocs.io/en/latest/tutorials/gpfa.html||rel=" noopener noreferrer" target="_blank"]] ===
	27	+
	28	+**Level**: advanced(%%) **Type**: user documentation
	29	+
	30	+This tutorial illustrates the usage of the gpfa.GPFA() class implemented in elephant, through its applications to synthetic spike train data, of which the ground truth low-dimensional structure is known.
33	33	=== [[Time-domain Granger Causality>>https://elephant.readthedocs.io/en/latest/tutorials/granger_causality.html||rel=" noopener noreferrer" target="_blank"]] ===
34	34
35		-//Level: advanced//
	33	+**Level**: advanced(%%) **Type**: user documentation
36	36
37	37	The Granger causality is a method to determine functional connectivity between time-series using autoregressive modelling.
38	38