Teilnahme EITN Fall School 2022, Paris

Antragstellende: Myriam de Graaf
Fachbereich, Studienrichtung: FB 7, Promotion Sports/Movement Science

From September 21st until September 30th, I attended the EITN Fall school on computational Neuroscience in Paris in order to broaden my understanding of this topic. Currently, I am doing my PhD, where I aim to use artificial neural networks to better understand human motor control. With a background in human movement science, I have thus far had to learn the computational neuroscientific aspects of my project to a large extent by myself. While this has taught me a lot, I still found that I had a lot of knowledge gaps that I had been unable to fill. The EITN course has helped me fill some of these gaps, and given me the necessary tools to become better versed in the field of computational movement science.

The course was subdivided into four themes of two days, to reflect the various levels of neural modeling, namely:

1. Single Cell models and Brain Signals
2. Network Models
3. Mean-field and population models
4. Whole-Brain Modeling

Each day consisted of 1-3 lectures followed by a tutorial, e.g. on various modelling softwares. At the end of each day, there was time set aside to work on our group projects, which we presented on our final day.

I was especially happy to learn more about mean field models, as this is something that has come up often in my own research, but that I did not fully understand yet. The lectures and tutorials on this topic have provided me with the required tools and information to better master this subject.

The group projects all focused on one of the four sublevels. I joined the Theme 2 (Network models) project that was focused on learning the Nest software. Nest is a python-based simulation software for spiking neural network models. In our project, I focused on coupling two balanced neural networks and investigating what was needed to restore balance after coupling. As I had very limited experience with Python programming before this course, this helped me not only learn Nest but also gave me some much-needed Python experience.

Aside from the instructions, it was also very nice to come into closer contact with fellow PhDs working in the field of computational/theoretical neuroscience. These contacts will be very useful e.g. in spreading my research, attending conferences and maybe even future collaborations.

All in all, I experienced the EITN fall school as a very educative and intensive course, where I learned a lot about computational neuroscience and made some beneficial new contacts. I would recommend it to anyone wanting to intensify their knowledge of computational neuroscience.