5 August 2016
Math and Matlab for Neuroscientists
The purpose of this course is to learn some of the fundamental mathematical and signal-processing theorems that underlie most of the advanced data analysis techniques used in the field of neuroscience and cognitive neuroscience. The focus of this course is on the theory, math and programming implementations (not how to use particular toolboxes). The course is designed for people who have a background in biology or psychology and would like to learn some of the maths.
Each day will be a mix of lectures and hands-on lab work. During the lab work you will have the opportunity to implement the concepts discussed in lecture in Matlab. Most lab work is done in small groups of two to three students. There will be several short quizzes each day to make sure you are learning the material. There will also be occasional homework assignments to help you consolidate and develop your newly learned skills. Quizzes and homework assignments are not graded and solutions will be provided the following day.
The major topics include: (1) Fourier transform, (2) convolution, (3) wavelets and time-frequency analyses, (4) matrix algebra including least-squares estimation and eigenvalue decomposition (principle components analysis).
This will be an intensive course designed for learning but there will be plenty of coffee and chocolates to keep you motivated. This material has been taught by Dr Cohen for seven years in several different countries, and is the basis of the book Analyzing Neural Time Series Data (MIT Press, 2014).
During the week-long course, you will have assignments and quizzes, and you may want to work after the lectures (this will not interfere with the social programme).
You must bring a laptop with Matlab or Octave (a free Matlab-like software) installed. Desktop computers will not be available.
Dr. M.X. Cohen
Donders Center for Neuroscience
This course is designed for PhD students and post-docs who have experience with data analysis but who feel that they need more training in the fundamentals. Beginner-level experience with Matlab programming is necessary. The course focuses heavily on analog signals (LFP/EEG/MEG).
- Understand the mechanics of the Fourier transform and how to implement it in Matlab.
- Understand convolution and how to use it to perform time-frequency analyses in Matlab.
- Understand the basics of matrix algebra and how to perform least-squares and eigenvalue decomposition in Matlab.
EUR 490: The course fee includes the registration fee, course materials, access to library and IT facilities, coffee/tea, lunch, and a number of social activities.
10% discount for early bird applicants. The early bird deadline is 1 April 2016.
15% discount for students and PhD candidates from Radboud University and partner universities.
EUR 195: Housing (optional)