Max Planck Institute for Human Cognitive and Brain Sciences
Dynamic Properties of Neural Mass Models
Mathematical modelling of networks of neural populations. Each population is represented by a neural mass, described by its relationship between the mean input and the mean output.
Characterization of the dynamic properties using bifurcation analysis, especially under considerations of the specific input and synaptic parameters. We find harmonic oscillations, irregular seizure-like oscillations, quasiperiodic behavior and chaos.
Modelling of transmission delays between brain areas.
Application of these models for the explanation of EEG phenomena and human behavior.
Implementation of learning rules and investigation of the self organization of neural networks.
Inverse estimation of network parameters from measured data.
Validation in animal and human experiments.
Andreas Spiegler (left the group)
Thomas Knösche (responsible)
Manh Nguyen Trong
MPI for Mathematics in Natural Sciences (Fatihcan Atay)
Technical University of Ilmenau (Jens Haueisen)
Publications (peer reviewed)
A. Spiegler, S. Kiebel, F. Atay , T.R. Knösche: Bifurcation Analysis of Neural Mass Models: Impact of Extrinsic Inputs and Dendritic Time Constants. NeuroImage 52(3), 1041-1058 (2010)
A. Spiegler, T.R. Knösche, K. Schwab, J. Haueisen, F.M. Atay: Modeling Brain Resonance Phenomena Using a Neural Mass Model, PLoS Computational Biology, 7(12), (2011)