Introduction to Information Theory in Neuroscience
The Technical Doctoral School of IT and Design at Aalborg University
Welcome to Introduction to Information Theory in Neuroscience
Description:
In this course, we introduce key concepts from information theory and their applications in neuroscience.
The course provides a gentle introduction to the foundations of information: what it is and how it can be quantified from real-world data.
Core topics include:
- Entropi
- Mutual information
- Information divergence
- Transfer entropy
- Directed information,
- Partial information decomposition.
Our main emphasis will be on directed information measures, which are powerful tools for uncovering statistical relationships in time-series data such as EEG. We will also cover non-directed measures like phase synchrony to provide a broader perspective.You will explore advanced concepts such as redundant and synergistic information, connectivity matrices, and coupling strengths between time series. These approaches will be demonstrated on EEG data with applications to auditory attention decoding and epileptic seizure analysis. Through hands-on exercises, you will gain practical experience by applying the methods not only to provided datasets but also to your own physiological recordings.
For additional information, updates, and registration, please refer to AAU PhD Moodle via the link provided on the right side of this page.