SOCRATES: Sensor and actuator selection for learning, estimation and control of fractional dynamical networks

Neurological diseases and disorders significantly burden society, affecting millions of people and costing billions of euros annually.

Digital pathology shows great potential in facilitating accurate diagnosis, prognosis, and treatment of these conditions, but models characterizing neural dynamics must be both explainable and efficient while capturing the long-term memory properties that play a crucial role in many neurological diseases and disorders.

Fractional-order dynamical networks (FODNs) are a promising class of models, but the lack of necessary and sufficient conditions for learning them has hindered their adoption in practice.

The SOCRATES project aims to develop necessary and sufficient conditions for efficient learning of FODNs and identify the minimum sensor and actuator placement for learning, observability, and controllability.

The project will be carried out over four years and solved using concepts from dynamical and control systems theory, graph theory, and combinatorial optimization.

The SOCRATES project aims to achieve the aforementioned goals, which will develop a new set of tools to improve our understanding of fractional-order dynamical networks, particularly neural dynamics.

This enhanced understanding can lead to new therapies using neurostimulation devices for conditions such as epilepsy, chronic pain, and retinitis pigmentosa.