Introduction to Neural Data Science

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.



Course type
Winter Term 2023/2024
moodle course available


Takes place every week on Thursday from 10:00 to 12:00 in room IC 03/449.
First appointment is on 12.10.2023
Last appointment is on 01.02.2024
Takes place every week on Thursday from 12:00 to 14:00 in room ID 03/121 (CIP-Pool 2).
First appointment is on 12.10.2023
Last appointment is on 01.02.2024


Basic knowledge of calculus and linear algebra, programming in Python

Any semester Bachelor (typically 5th), successful completion of Introduction to Artificial Intelligence

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

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210