People
Our Lab Team
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.
Hadas Benisty
BSc in Electrical Engineering BA in Physics PhD in Electrical Engineering
hadasbe@technion.ac.il
Alumni
Yoav Harris
Geometric framework for robust order detection in delay-coordinates Dynamic Mode Decomposition
2026
Yonatan Kleerekoper
Title: Geometric Insights into Neural Learning: A Riemannian Approach to Dynamics of Functional Connectivity
2026