My main interests revolve around the characterization of brain activity associated with high-level cognitive function in humans, with a focus on thinking, reasoning, executive functions, and abstract conceptual forms of learning. A main focus of my research is on the complex relationship between spontaneous and task-related neural activity. Other fields of interest include economic aspects of human behaviour and causality. More generally, I am captivated by complex systems lato se1465301_10152037377255255_1688040276_nnsu and their commonalities across fields including physiology, physics, economics, sociology, and ecology.

High-level cognitive function (viz. thinking, reasoning, and executive functions) is typically associated with neural activity unfolding at numerous intertwined temporal (from milliseconds to minutes), and spatial (from short-range local, to long-range whole-brain activity) scales, and often lacks well-defined temporal duration and stationary spatial topography and topology. Temporal non-stationarity, spatial extension, and multiscale variability imply that most standard methods of data analysis are not applicable. Thus, the very shape of the neural correlate of these activities is highly non-trivial.

The core of my work is devoted to finding ways to define neural correlates of complex cognitive tasks, i.e. particular functions of some aspects of the neural activity associated with the execution of these tasks.

Perhaps the most immediate way to convey the general flavour of one’s research is to show the main questions that it addresses. In my research, I address the following ones:

How can we characterize reasoning activity far away from a solution to a given problem?
What are the temporal and spatial characteristic scales of activities such as thinking or reasoning?
How do activities developing at various spatial and time scales interact with each other?
Can we predict the occurrence of insight far away from its occurrence?
How efficiently does a brain think, reason, learn?
Is there an internal organization that endows a neural system with the ability to learn a given task?
What makes a given material learnable?
Can we assess the potential for future learning?


To provide answers to these questions, I use methods and concepts from non linear analysis, complex network theory statistical mechanics, and advanced algebra, and describe observed behaviour and neural activity at many spatial and temporal scales as the product of a biophysical system, with given thermodynamical, statistical, topological and dynamical properties.


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