Perceptions, memories, emotions, and everything that makes us human, demand the flexible integration of information represented and computed in a distributed manner. The human brain is structured in a large number of areas in which information and computation are highly segregated. Normal brain functions require the integration of functionally specialized but widely distributed brain areas. Furthermore, human behavior entails a flexible task-dependent interplay between different subsets of these brain areas in order to integrate them according to the corresponding goal-directed requirements. Nevertheless, the neuronal and cortical mechanisms governing the interactions and entrainment of different specialized brain areas for reaching that integration remain poorly understood. Hence, the understanding of the mechanisms underlying the integration of distributed segregated representations is a central question for the entire field of cognition and brain function.
We contend that the functional and encoding roles of diverse neuronal populations across areas are subject to intra- and inter-cortical dynamics. The main aim of the current research is to elucidate precisely the interplay and mutual entrainment between local brain area dynamics and global network dynamics. We wish to understand how segregated distributed information and processing are integrated in a flexible and context-dependent way, as required for goal-directed behavior. More concretely, we hypothesize that coherent oscillations within frequency-specific large-scale networks and coherent structuring of the underlying fluctuations are crucial computational mechanisms for the flexible integration of distributed processing and interaction of representations. Using computational modeling and theoretical tools, we study how local dynamics shapes and determines global dynamics in a self-organizing manner, and how much of the global dynamics information is embedded in the local dynamics. This will help us understand the mechanisms underlying brain functions by complementing structural and activation based analyses with dynamics. In particular, we expect to better comprehend the generation and interpretation of global and local spatio-temporal patterns of activity revealed at many levels of observations (fMRI, EEG, MEG) in humans, and under task and resting (i.e. no stimulation and no task) conditions. We act as a methodological hub by integrating different levels (microscopic, mesoscopic and macroscopic) of experimental characterization of human (and non-human animals) cognition through computational modeling and theoretical analyses.
Better understading of neuropsychiatric diseases
Towards a global model of brain activity:
Lessons from the human connectome
Unrevealing Brain Networks