What can I find here?

This collab contains the materials which will be used during the hands-on session on 24 November 2025, during EITN Autumn School 2025, which takes place in Marseille: https://www.ebrains.eu/news-and-events/eitn-ebrains-fall-school-2025.

The objective of this hands-on session is to create a familiarity with mean-field concepts for the participant by performing simulations related to different brain states. We will see the AdEx mean-field model and its building blocks. We will see example simulations of this mean-field framework in relation to neural population dynamics in different brain states. Finally, we will show a generalization of this framework to the whole-brain scale via TVB.

Requirements

Access to the notebooks and materials requires to have an EBRAINS account.

Participants are also suggested to download the materials in case of connection issues. The material and notebooks can be downloaded by clicking on "Drive" on the left hand side (you should be logged in via your EBRAINS account to have access to Drive). Once downloaded, the participants are also suggested to run the cells with the title "Initialization" in "mainAdEx.ipynb" (Part I) and in "Human_TVB_AdEx.ipynb" (Part II) to see and install the necessary packages/dependencies. If the notebooks are run locally, then "%matplotlib widget" should be disabled by commenting it in the relevant cells.

TVB installation can be done via the following link: https://www.thevirtualbrain.org/tvb/zwei/brainsimulator-software . Once it is installed, it can be used for a variety of simulations found in EBRAINS Collab.

References

• Baspinar, E., Cecchini, G., DePass, M., Andujar, M., Pani, P., Ferraina, S., Moreno-Bote, R., Cos, I., Destexhe, A. (2023). A biologically plausible decision-making model based on interacting neural populations. bioRxiv, 2025.
• Di Volo, M., Romagnoni, A., Capone, C., Destexhe, A. (2019).  Biologically realistic mean-field models of conductance-based networks of spiking neurons with adaptation. Neural Computation, 31(4), 653-680.
• Goldman, J. S., Kusch, L., Aquilue, D., Yalçınkaya, B. H., Depannemaecker, D., Ancourt, K., Nghiem, T. E., Jirsa, V., Destexhe, A. (2023). A comprehensive neural simulation of slow-wave sleep and highly responsive wakefulness dynamics. Frontiers in Computational Neuroscience, 16, 1058957.
• Sacha, M., Goldman, J. S., Kusch, L., Destexhe, A. (2024). Asynchronous and slow-wave oscillatory states in connectome-based models of mouse, monkey and human cerebral cortex. Applied Sciences, 14(3), 1063.
• Sanz-Leon P., Knock S. A., Spiegler A., Jirsa V. K. (2015). Mathematical framework for large-scale brain network modeling in The Virtual Brain. NeuroImage, 111, 385-430.