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.

People involved

  • Andreas Spiegler (left the group)
  • Thomas Knösche (responsible)
  • Manh Nguyen Trong
  • Peng Wang


  • 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)
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