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Analysing and modelling neurobiological and behavioural data typically involves mathematical programming, which pertains to mathematical operations that are applied using a digital computing environment (e.g., solving systems of linear equations, eigen decomposition, the Fourier transform). In this course, we will use the Matlab computing environment, which is especially good for analysing and modelling signals (variables that vary as a function of time, space and frequency).
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This course has three parts:
- Introduction to Matlab
- Linear algebra (vectors & matrices, transformations, the general linear model, principal component analysis)
- The Fourier transform and convolution
The parts on linear algebra, the Fourier transform, and convolution are tightly linked to the corresponding lectures in Advanced Mathematics (DGCN09).
The teaching is based on tutorial exercises and feedback sessions. The tutorial exercises must be prepared prior to the feedback sessions, such that individual feedback can be provided.
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No preliminary knowledge is required, but the course will require less investment for students with experience in Matlab and the mathematical operations that are covered in this course. If you have never worked with Matlab, you will learn this in the first part of this course (through self-study exercises). The theory behind the mathematical operations is covered in the Advanced Mathematics course.
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