This tutorial is part 1 of 2. It aims to provide viewers with an understanding of the fundamentals of R tool. Note: parts 1 and 2 of this tutorial are part of the same YouTube video; part 1 ends at 17:42. 

This lesson introduces the practical usage of The Virtual Brain (TVB) in its graphical user interface and via python scripts. In the graphical user interface, you are guided through its data repository, simulator, phase plane exploration tool, connectivity editor, stimulus generator, and the provided analyses. The implemented iPython notebooks of TVB are presented, and since they are public, can be used for further exploration of TVB. 

This tutorial covers the fundamentals of collaborating with Git and GitHub.

This lesson provides a comprehensive introduction to the command line and 50 popular Linux commands. This is a long introduction (nearly 5 hours), but well worth it if you are going to spend a good part of your career working from a terminal, which is likely if you are interested in flexibility, power, and reproducibility in neuroscience research. This lesson is courtesy of freeCodeCamp.

Overview of Day 2 of this course.

This talk compares various sensors and resolutions for in vivo neural recordings.

This hands-on tutorial explains how to run your own Minion session in the MetaCell cloud using jupityr notebooks.

In this hands-on analysis tutorial, users will mimic a kernel crash and learn the steps to restore inputs in such a case.

This lesson introduces various methods in MATLAB useful for dealing with data generated by calcium imaging. 

This lesson will go through how to extract cells from video that has been cleaned of background noise and motion.

This tutorial demonstrates how to use MATLAB to generate and visualize animations of calcium fluctuations over time. 

This final hands-on analysis tutorial walks users through the last visualization steps in the cellular data.

This tutorial instructs users how to use MATLAB to programmatically convert data from cells to a matrix.

In this tutorial, users will learn how to identify and remove background noise, or "blur", an important step in isolating cell bodies from image data. 

This lesson teaches users how MATLAB can be used to apply image processing techniques to identify cell bodies based on contiguity.

This tutorial demonstrates how to extract the time course of calcium activity from each clusters of neuron somata, and store the data in a MATLAB matrix.

This lesson demonstrates how to use MATLAB to implement a multivariate dimension reduction method, PCA, on time series data.