This unit develops computational models and analytical pipelines for the study of neuronal and brain activity, tightly coupled to experimental and clinical data. Core activities include the analysis of electrophysiological signals from in vitro neuronal networks, in vivo preclinical recordings, and human EEG and high-density/stereo-EEG data, as well as the integration of features derived from brain imaging.

By combining mechanistic modeling, machine learning, and multimodal data integration, this unit aims to extract quantitative biomarkers, investigate connectivity–dynamics relationships, and link cellular and network-level mechanisms to brain-scale signals. A central focus is the development of digital network and brain models, including digital twin approaches, to support interpretation, prediction, and hypothesis testing across in vitro, in vivo, and clinical contexts.