Research

Linking network connectivity to resulting dynamics

The patterns of connections in a network play a central role in shaping its dynamics, but a rigorous mathematical description of this link remains challenging. We focus on oscillator networks, where we have developed a precise mathematical link between connectivity and dynamics. We are particularly interested in predicting emergent dynamics from network connectivity, and in developing methods to control them — uncovering the network mechanisms behind pattern formation.

Emergence of spatiotemporal dynamics in brain networks 

What drives the emergence of organized neural activity in the brain? We study how structural connectivity and the dynamics of neural interactions shape neural activity and support brain function, combining theoretical and computational modelling with the analysis of neural data across multiple modalities. Our ultimate goal is to understand how the brain's structural connections support behaviour and function, and to build personalized brain models with potential applications to clinical studies and neurological health.

Neural computations in artificial networks 

We are interested in the spatiotemporal dynamics of neural networks and how they support computation. In particular, we focus on simplified network models where computations can be fully interpreted and understood — in some cases expressed through simple mathematical equations. This approach could help reduce the cost of training these systems while offering a more general perspective on computation in artificial and biological neural networks.