christophe-pouzat/LASCON_2018: Lecture notes of C Pouzat's LASCON 2018 lectures
| Main Author: | Christophe Pouzat |
|---|---|
| Format: | info software eJournal |
| Terbitan: |
, 2019
|
| Online Access: |
https://zenodo.org/record/3240236 |
Daftar Isi:
- Course material for LASCON 2018 This repository contains data, slides, code, everything needed to "reproduce" my LASCON 2018 lectures and practical work. The course material per se is located in the Lectures folder. The outline: Lecture 1 (2018-01-19): Making one's work reproducible. The slides and "supplementary material" are located in the Lectures/ReproducibleResearch folder. The source file of the slides is Pouzat_LASCON2018_RR_slides.org, while the slides themselves are in file Pouzat_LASCON2018_RR_slides.pdf. The source file contains additional references and notes (and can be visualized directly with GitHub (just click on the file name). The tutorial in available in org as well as in HTML formats. Lecture 2 (2018-01-22): A glimpse of the Statistician's toolbox. For the slides and more, look at Lectures/Statistics. The slides source file, Pouzat_LASCON2018_Statistics_slides.org contains notes and references that do not appear in the slides themselves Pouzat_LASCON2018_Statistics_slides.pdf. The tutorial is available in org and in HTML formats. The Fatt and Katz data set used in the tutorial, nerve.dat, is also available in the Lectures/Statistics/data/ folder. Lecture 3 (2018-01-23): Spike Sorting. For the slides and more, look at Lectures/SpikeSorting/. The slides are available in their source format org as well as in PDF. The tutorial is available in org, HTML and PDF. Lecture 4 (2018-01-24): Stochastic neuronal discharge models and their inference. For the slides and more, look at Lectures/SpikeTrainAnalysis. The slides are available in their source format org as well as in PDF. The tutorial can be found in its org source format as well as in PDF, HTML and MarkDown formats. A copy of the data used are also contained in the Lectures/SpikeTrainAnalysis folder. Invited Lecture (2018-01-26): Peri-Stimulus Time Histograms Estimation Through Poisson Regression Without Generalized Linear Models. For the slides look at Lectures/PSTH. The slides are available as an org source file as well as in PDF. The precise justification of the material presented in this lecture can be found in a manuscript Homogeneity and identity tests for unidimensional Poisson processes with an application to neurophysiological peri-stimulus time histograms. The code, its description and the way the code was applied to the data to generate the complete analysis is available in R and in Python.