|
|
@@ -7,6 +7,7 @@ Exercises for individual methods are contained in the `test-*` files. The second
|
|
|
|
|
|
To get started, create a Python environment using the `environment.yml` file provided.
|
|
|
|
|
|
+
|
|
|
## Andrea Brovelli: Neuronal Interactions
|
|
|
The folder `tutorials/notebooks` contains a series of 5 exercises based on the Frites software package, which are replicated from https://github.com/brainets/CookingFrites.
|
|
|
Part 0 explains the usage of `xarray`, a Python package used to represent data in Frites. Tutorials 1-4 then cover the various stages of a Frites workflow based on an example sEEG dataset.
|
|
|
@@ -14,6 +15,16 @@ Information on the various stages of the workflow, and on the dataset details ar
|
|
|
on the example datasets, and ends on a practical exercise to explore the dataset further.
|
|
|
|
|
|
|
|
|
+## Byron Yu: Dimensionality Reduction
|
|
|
+The notebook `tutorials/Exercise_PCA.ipynb` contains a the primary exercise centered around implementing a simple principle component analysis (PCA).
|
|
|
+Also, in `tutorials/Tutorial_GPFA.ipynb` you will find a tutorial guiding you through the application of the GPFA implementation of Elephant on an example dataset. For this tutorial, a video
|
|
|
+tutorial is available (see link in notebook).
|
|
|
+
|
|
|
+The notebook `tutorials/Exercise_PCA_to_FA.ipynb` contains a mathematically more advanced exercise covering both PCA and the transition to Factor Analysis (FA).
|
|
|
+
|
|
|
+To get started, create a Python environment using the `environment.yml` file provided.
|
|
|
+
|
|
|
+
|
|
|
## Martin Nawrot: Higher-order Correlations
|
|
|
The notebook `tutorials/trial_by_trial_variability.ipynb` contains an exercise centered around time-resolved Fano Factors. The first lines of the exercises contain code that help you
|
|
|
load the used datasets.
|
