Developing Mathematical and Physical Tools for Multiscale Dynamical Systems: Applications to Neurophysiological Data.
When studying nature, and particularly living systems, we observe multiple levels of organization and interaction, ranging from microscopic scales—such as DNA sequences or protein networks—to macroscopic ones, including ecological interactions between species or social networks. Across these levels, different measurement techniques allow us to access information to understand their temporal dynamics, the interactions among components, and how such behaviors influence both higher (bottom-up) and lower (top-down) levels.
Given the vast diversity of data that can arise at each scale, it is crucial to develop analytical tools capable of extracting as much information as possible. In this context, physics and mathematics provide advanced frameworks for the study of complex systems.
In this talk, I will present different methodologies structured around four main axes: (i) dynamical analysis of uni- and multidimensional signals, (ii) characterization of interactions through graph theory and graph dynamics, (iii) higher-order interactions (HOI) explored via hypergraphs and Topological Data Analysis (TDA), and (iv) explainable machine learning and deep learning models (XAI).
These approaches will be illustrated with applications to neuroscience, from the study of different states of consciousness (wakefulness, sleep, anesthesia, drug-induced states) to neurological disorders such as epilepsy, Parkinson’s disease, and Alzheimer’s disease.
This IFISC Seminar will be broadcasted in the following zoom link:
Coffee and cookies will be served 15 minutes before the start of the seminar
Detalles de contacto:
Claudio Mirasso Contact form