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28	28	**Level**: advanced(%%) **Type**: user documentation
29	29
30	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"]] ===
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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		-=== [[Analysis of Sequences of Synchronous Events in Massively Parallel Spike Trains (ASSET)>>https://elephant.readthedocs.io/en/latest/tutorials/asset.html||rel=" noopener noreferrer" target="_blank"]] ===
42		-
43		-**Level**: advanced(%%) **Type**: user documentation
44		-
45		-The tutorial demonstrates a method of finding patterns of synchronous spike times (synfire chains) which cannot be revealed by measuring neuronal firing rates only.
46		-