Miguel Aguilera

Ikerbasque Research Fellow

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Information of interest

My research line is guided by the ambition of using methods from different branches of complex systems research and related areas (statistical mechanics, information theory, machine learning and nonlinear dynamics) to study the closed-loop ongoing interaction of intelligent agents with their environments. I work with methods from nonequilibrium physics to study neural systems in interaction with their environment as open, nonequilibrium systems, which often challenge mathematical and modelling methods assuming linearity or asymptotic equilibrium. My goal is to apply such methods to address open problems and theoretical challenges related to leading theories in neuroscience and as well as  broader research perspectives on life and mind.

Code and data reproducing the paper "A unifying framework for mean field theories of asymmetric kinetic Ising systems"

Authors: Miguel Aguilera

License: GPLv3.0