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adavison 1.1 1
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abonard 3.1 3 * ((( ==== **[[Beginner >>||anchor = "HBeginner-1"]]** ==== )))
jessicamitchell 2.1 4
abonard 32.1 5 * ((( ==== **[[Advanced >>||anchor = "HAdvanced-1"]]** ==== )))
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abonard 3.1 7 === **Beginner** ===
jessicamitchell 2.1 8
abonard 3.1 9 === [[Statistics of spike trains>>https://elephant.readthedocs.io/en/latest/tutorials/statistics.html||rel=" noopener noreferrer" target="_blank"]] ===
jessicamitchell 2.1 10
abonard 3.1 11 **Level**: beginner(%%) **Type**: user documentation
jessicamitchell 2.1 12
abonard 3.1 13 This notebook provides an overview of the functions provided by the elephant statistics module.
abonard 32.1 14 === **Advanced** ===
jessicamitchell 2.1 15
abonard 32.1 16 === [[Spike Pattern Detection and Evaluation (SPADE)>>https://elephant.readthedocs.io/en/latest/tutorials/spade.html||rel=" noopener noreferrer" target="_blank"]] ===
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18 **Level**: advanced(%%) **Type**: user documentation
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20 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.
abonard 33.1 21 === [[The Unitary Events Analysis>>https://elephant.readthedocs.io/en/latest/tutorials/unitary_event_analysis.html||rel=" noopener noreferrer" target="_blank"]] ===
abonard 32.1 22
abonard 33.1 23 **Level**: advanced(%%) **Type**: user documentation
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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.
abonard 34.1 26 === [[Gaussian Process Factor Analysis (GPFA)>>https://elephant.readthedocs.io/en/latest/tutorials/gpfa.html||rel=" noopener noreferrer" target="_blank"]] ===
abonard 33.1 27
abonard 34.1 28 **Level**: advanced(%%) **Type**: user documentation
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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.
abonard 35.1 31 === [[Time-domain Granger Causality>>https://elephant.readthedocs.io/en/latest/tutorials/granger_causality.html||rel=" noopener noreferrer" target="_blank"]] ===
abonard 34.1 32
abonard 35.1 33 **Level**: advanced(%%) **Type**: user documentation
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35 The Granger causality is a method to determine functional connectivity between time-series using autoregressive modelling.
abonard 36.1 36 === [[Parallel>>https://elephant.readthedocs.io/en/latest/tutorials/parallel.html||rel=" noopener noreferrer" target="_blank"]] ===
abonard 35.1 37
abonard 36.1 38 **Level**: advanced(%%) **Type**: user documentation
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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.
abonard 37.1 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"]] ===
abonard 36.1 42
abonard 37.1 43 **Level**: advanced(%%) **Type**: user documentation
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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.
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