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23	23	**Level**: advanced(%%) **Type**: user documentation
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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"]] ===
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	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	+=== [[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	+