How does brain connectivity evolve through learning? This is the biological motivation for this project.
We explore the dynamics of correlations between neuronal activity of cells /brain regions and how they evolve to create new representations and improve performance during learning. Diverging from the commonly used approaches relying on statistics, we use a geometric point of view of correlations as points on a Riemannian manifold. We will develop a unique approach to model the temporal evolution of correlation matrices throughout the learning process. This interpretable approach would allow us to identify the intrinsic time scales of the data as well as the main components driving this process.
The contribution of this project will be twofold: a mathematical framework for modeling temporal dynamics of SPD matrices, and biological – promoting a deeper understanding of functional connectivity in the brain.
