Control properties of multiagent dynamical systems modelling brain neural networks

Abstract

The control of linear dynamical systems is a strategy that the brain uses to control its own intrinsic dynamics. The brain structure can be modelled as a networked system that is expressly interesting system to control because of the role of the underlying architecture, which predisposes some components to particular control motions. The concept of brain cognitive control defined by neuroscientists is related to the mathematical concept of control defined by physicists, mathematicians, and engineers, where the state of a complex system can be adjusted by a particular input. The in-depth study on the controllability and structural controllability character of linear dynamical systems, despite being very difficult, could help to regulate the brain cognitive function. Small advances in the study can favour the study and action against learning difficulties such as dyscalculia or other disturbances like the phenomena of forgetting. Between different aspects in which we can study the controllability we have the notion of structural controllability and exact controllability. In this talk, we revise these concepts for linear dynamical systems and multiagent neural networks