Dynamic Properties of Neural Mass Models
Keypoints
- 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.
People involved
- Andreas Spiegler (left the group)
- Thomas Knösche (responsible)
- Manh Nguyen Trong
- Peng Wang
Cooperations
- 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)