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Last modified by abonard on 2025/06/13 16:08

From version 33.1
edited by abonard
on 2025/06/13 16:08
Change comment: There is no comment for this version
To version 36.1
edited by abonard
on 2025/06/13 16:08
Change comment: There is no comment for this version

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23 23  **Level**: advanced(%%) **Type**: user documentation
24 24  
25 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"]] ===
26 26  
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.
31 +=== [[Time-domain Granger Causality>>https://elephant.readthedocs.io/en/latest/tutorials/granger_causality.html||rel=" noopener noreferrer" target="_blank"]] ===
32 +
33 +**Level**: advanced(%%) **Type**: user documentation
34 +
35 +The Granger causality is a method to determine functional connectivity between time-series using autoregressive modelling.
36 +=== [[Parallel>>https://elephant.readthedocs.io/en/latest/tutorials/parallel.html||rel=" noopener noreferrer" target="_blank"]] ===
37 +
38 +**Level**: advanced(%%) **Type**: user documentation
39 +
40 +elephant.parallel module provides a simple interface to parallelize multiple calls to any user-specified function. We showcase a typical use case in this tutorial which is calling a function many times with different parameters.
41 +