How does intelligence emerge from a system of simple components? In which way do neural networks combine memory and computation? How does the constraint of locality affect the structure of these systems?

In my research I combine several information theoretic measures to analyze both developing structure and flow of information in these systems. These tools help in shedding light on the fundamental interplay between neural structure and computation. Recently, I have been working on novel information-theoretic learning goals that allow neurons to self-organize and learn locally. Ultimately, insights gained in this way might allow us to understand the fundamental working principles of neural information processing.

While this research is interesting in itself, it becomes vital for a safe application of neural networks in the real world. From the moment we understand neural networks to a degree where hidden biases, adversarial attacks and unintuitive results can be explained and overcome we can exploit the huge potential of artificial intelligence. The recent rapid improvement in capabilities of these systems only increase the need for more intensive, targeted research in this area.

Research groups:

• Dr. Viola Priesemann, Physics, Complex Systems & Neural Networks, MPI for Dynamics and Self-Organization, Göttingen, Germany. (Master's, PhD)
• Prof. Dr. Anna Köhler, Organic and Anorganic Semiconductors, University of Bayreuth, Germany. (Bachelor's)

To date, I have given tutorials on:

• Living Neural Networks (Co-Organization), Summer 2026
• Deep Learning for Computer Vision, Winter 2022/23
• Machine Learning, Summer 2022
• Introduction to Physics of Complex Systems, Winter 2019/20
• Physics preparatory course for agricultural sciences, biology, forestry & earth sciences, Fall 2019
• Scientific computing, Summer 2019