Do you have large-scale neuroscience datasets and lots of ideas, but need a better understanding of how to work with your data? Then this course is for you! You will learn key concepts in multichannel neuroscience analyses, including dimension-reduction, source separation, and least-squares modeling, and how to implement them in Matlab.
This course is designed for PhD students, postdocs, and senior researchers who have experience with data analysis and want a deeper understanding of advanced data analysis methods. Some experience with Matlab is necessary. Masters students are welcomed if they have had some experience with neuroscience data analysis. The course focuses on analog electrophysiology signals (LFP/EEG/MEG), but the methods are applicable to imaging (fMRI or calcium/wide-field imaging) as well.
After this course you are able to:
1. Understand the key concepts in linear algebra including matrix multiplication, inverse, and projections, as well as know geometric and algebraic ways of representing data and analyses.
2. Implement the least-squares algorithm to estimate general linear model.
3. Understand eigendecomposition and its use in dimensionality reduction and source separation.
4. Simulate multivariate data to evaluate analysis methods and model overfitting.
EUR 550: The fee includes the registration fees, course materials, access to library and IT facilities, coffee/tea, lunch, and a number of social activities.
For more information click "LINK TO ORIGINAL" below.