Rapid technological advances have recently opened up new possibilities in understanding how the brain works. In particular the number of neurons that can be simultaneously recorded has increased considerably to hundreds (and soon thousands!) of neurons. However, this has led to a big challenge on how to actually process and analyze the resulting big data sets. Solutions for these challenges are part of the new exciting research field of 'Neural Data Science'.

In this module you will learn how methods and approaches from data science and machine learning can be applied to study brain signals and the related cognitive functions. In the first part of the module we will focus on so-called spike trains, how they can be analyzed, visualized, and decoded. In the second part of the module we will look at continuous signals, in particular at neural oscillations. Finally, we will learn about and apply some advanced methods from machine learning, such as dimensionality reduction approaches, reinforcement learning, clustering, and computational statistics. In the lectures I will provide the relevant neurobiological background and explain the computational approaches, which will then be applied in the computer exercises using real neural data sets.

Prof. Dr. Robert Schmidt Lecturer

(+49) 234-32-27300 robert.schmidt@rub.de NB 3/68

Course type

Lectures

Credits

6

Term

Winter Term 2024/2025

E-Learning

moodle course available

Lecture

Takes place every week on Friday from 08:30 to 10:00 in room GABF 04/516.
First appointment is on 11.10.2024
Last appointment is on 31.01.2025

Exercise

Takes place every week on Friday from 10:15 to 11:45 in room MB 2/90.
First appointment is on 11.10.2024
Last appointment is on 31.01.2025

Basic knowledge of calculus and linear algebra, programming in Python

Literature: Nylen, E. L., & Wallisch, P. (2017). Neural Data Science: A Primer with MATLAB® and PythonTM. Academic Press.