Winter Term 2020-21 / Neural Behav Sci



Course title

Essential Mathematics for Neuroscience

Arrenberg, Dehmelt
Course content / topics

Mathematical and statistical methods play an important role in neuroscience research. This course is intended to give a refresher on the mathematics needed in the everyday life of a neuroscientist. In particular, one aim of the course is to ensure that students have the necessary mathematical expertise to follow other courses in the program.

The course will consist of three parts, (i) Calculus and (ii) Linear Algebra and (iii) Advanced Topics. In the calculus section, we will cover differentiation, integration and Taylor approximations. Time permitting, multivariate calculus or basics of differential equations will be included. In the introduction to linear algebra, students will learn about vector and matrix manipulations, linear systems of equations as well as eigenvalues. These aspects of linear algebra provide a foundation for understanding statistics as well as for using software packages such as Matlab. The third part will cover principle component analysis (PCA) and the basics of Fourier analysis. Students are expected to understand the basic principles of these techniques, and be in a position to apply them to data analysis problems (Script_Essential Maths).

Course Schedule & Topics

The tutorial to this course is only open for students of the M.Sc. Neural & Behavioural Sciences!
A few additional places are available for students of the  M.Sc. Cellular and Molecular Neuroscience (or GTC doctoral students) - if you would like to join, please send an email to: 

Day, time & location

Tue, 10-12am, GTC Lecture Hall