Dynamics in Brain-inspired Neural Networks // 2-6 November 2026

The brain forms a large recurrent spiking neural network with extremely high connectivity making time-dependent mean-field approaches valuable tools to translate the microscopic level of single-neuron dynamics to the mesoscopic or macroscopic level. In recent years, experimental data as well as simulation studies have highlighted the importance of low-dimensional macroscopic dynamics in large recurrent spiking networks. On the theory side, the notion of low-rank connectivity has shifted into the focus of interest. 
This meeting will to bring together researchers in mathematical and theoretical neuroscience who address the above topics from different perspectives. 

Phase 1
Public lectures from Monday afternoon – Wednesday end of day

Presentations will alternate between speakers with a physics and those with a math background so as to foster links between the two communities.

Phase 2
Tutorials for PhD students on Thursday and Friday.