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Linear Algebra for Neuroscientists

Date: 15-19 August 2022            
Early bird fee: €495 or €413(deadline 1 April 2022)
Regular fee: €550
Application deadline: 1 July 2022

Download detailed programme here

Course Description

If you're interested in this course you might also want to participate in Analyzing Neural Time Series Data

Neuroscience is moving towards "big data," with new and improved brain measurement technologies that acquire an ever-increasing amount of data. Examples include multichannel LFP/tetrodes, high density MEEG, and optical imaging. Increases in the number of simultaneously recorded data allows new discoveries about spatiotemporal structure in the brain, but also presents new challenges for data analyses. Because data are stored in matrices, algorithms developed in matrix analysis will be extremely useful. On the other hand, linear algebra and matrix analysis are unfortunately rarely taught in neuroscience/biology/psychology courses.

The purpose of this course is to introduce you to matrix-based data analysis methods in neural time series data, with a focus on least-squares model fitting, multivariate dimensionality-reduction, and source-separation methods. The course is mathematically rigorous but is approachable to researchers with no formal mathematics background. MATLAB is the primary numerical processing engine but the material is easily portable to Python or any other language. The focus is on understanding methods and their implementation, rather than on using analysis toolboxes.

Each day will be a mix of lectures and hands-on labwork. In the labwork you will have the opportunity to implement in Matlab the concepts discussed in lecture. Labwork is done individually or in small groups of 4-5 other participants. There will be homework assignments to help you consolidate and develop your newly learned skills (homework is not graded, and solutions will be provided the following day). This will be an intensive course designed for learning, but there will be plenty of coffee and jokes to keep you motivated. You must bring a laptop with Matlab or Octave (a free Matlab-like software) installed.

Course Leader

Dr. Michael X Cohen, Associate professor
Donders Institute for Brain, Cognition and Behaviour
Radboud University Medical Center

Learning Outcomes

After this course you are able to:

  • 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.
  • Implement the least-squares algorithm to estimate general linear model parameters.
  • Understand eigendecomposition and its use in dimensionality reduction and source separation.
  • Simulate multivariate data to evaluate analysis methods and model overfitting.

Level of participant

  • PhD
  • Post-doc
  • Professional

The course is designed for

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.

Admission Requirements

Previous experience (beginner to moderate level) with Matlab programming is required. A strong background in mathematics is not required. The most important requirement is a positive and optimistic attitude! The application letter should include your motivation (including a statement "By the end of this course, I want to be able to ..."), and your previous experience with neuroscience data and programming languages (Matlab/python/other).

Admission Documents

  • Motivation letter
  • CV


15 - 19 August 2022

Application Deadline

1 July 2022

Course Fee


Early Bird Discounts

  • 10% early bird discount for all applicants
  • 25% discount early bird discount for:
    • students and PhD candidates from partner universities and Radboud University
    • alumni of Radboud Summer School and Radboud University

Mode of study

This course will be offered on campus.

ECTS credits


Participant Testimonials

“Refreshing, impactful, enjoyable, fulfilling, educational, beautiful. I recommend Mike X Cohen’s courses as he was an excellent teacher.”