Year: 2025
Type: conference-paper
Abstract: We address the challenge of parameter identification in networks of neural mass models. We consider a system of n interconnected populations, structured in successive layers, where measurements are available from only one. Traditional offline methods struggle with parameter ambiguity and time-dependent variability. We propose an online approach that exploits the system’s nonlinear characteristics, particularly saturation functions, to enable effective parameter recovery. By designing specific control inputs applied to targeted nodes, it becomes possible to isolate and retrieve parameters effectively. Crucially, we show that this process must be conducted online to achieve full identification, as control inputs must adapt to previously unknown states and parameters.
Authors: A. M. Annabi, J.-B. Pomet, D. Prandi, L. Sacchelli
Institutions: Centre National de la Recherche Scientifique, Institut national de recherche en sciences et technologies du numérique, Université Côte d'Azur, Laboratoire Jean-Alexandre Dieudonné, MCTAO: Mathématiques pour le Contrôle, le Transport et leurs Applications, Université Paris-Saclay, Laboratoire des signaux et systèmes
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Funder: Agence Nationale de la Recherche
OpenAlex ID: W4415142273
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