FG 868: Computational Modeling of Behavioral, Cognitive and Neural Dynamics - Teilprojekt A 3: Dynamical complex network approaches for the analysis and modeling of large scale brain activities during cognitive processes

The topic of this proposal is studying the neuroscientific foundations of word recognition by analysis and modeling of the corresponding large-scale event-related brain response (ERP) using the principles of nonlinear dynamical complex networks. With a close collaboration among the expertise from statistical physics/nonlinear dynamics, biological psychology and experimental psychology we will (i) obtain high-resolution ERP data from a well-controlled psychological experiment with well-defined cognitive processes which are highly relevant for reading, (ii) develop advanced tools based on the concept of hierarchical complex networks for the analysis of large-scale data of brain activity recorded during cognition, which can be used to detect and monitor the successive sub-processes and distinguish different cognitive tasks under various physiological and psychological conditions, and (iii) develop biophysical network models that can account for the ERP, using neural mass models to represent the activity of the brain regions involved into the processes. The coupling parameters of such models allow us to identify the causal connectivity in the processing stream.

Projektleitung
Sommer, Werner Prof. Dr. rer. soc. habil. (Details) (Forschergruppen)

Mittelgeber
DFG: Forschergruppen

Laufzeit
Projektstart: 11/2007
Projektende: 12/2010

Forschungsfelder
Cognition, Cognitive Neuroscience, EEG, Kognition, Kognitive Neurowissenschaft, Language, Neural Networks, Neuronale Netzwerke, nichtlineare Dynamik, nonlinear Dynamics, Word Recognition, Wortverarbeitung

Publikationen
Schinkel, S., Zamora-Lopez, G., Dimigen, O., Sommer, W., & Kurths, J. (2011). Functional network analysis reveals differences in the semantic priming task. Journal of Neuroscience Methods, 197, 333–339.

Zuletzt aktualisiert 2020-09-03 um 17:08