On analysis of inputs triggering large nonlinear neural responses: Slow-fast dynamics in the Wendling neural mass model

Jurgen Hebbink*, Stephan A. van Gils, Hil G.E. Meijer

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)
80 Downloads (Pure)


Many applications in neuroscience, such as electrical and magnetic stimulation, can be modelled as short transient input to non-linear dynamical systems. In excitable systems, small input yields more or less linear responses, while for increasing stimulation strength large non-linear responses may show up suddenly. A challenging task is to determine the transition between the two different response types.

In this work we consider such a transition between normal and pathological responses in a model of coupled Wendling neural masses as we encountered in a previous study. First, the different timescales of inhibition in this model allow a slow-fast analysis. This reveals two different dynamical regimes for the systems’ response. Second, the two response types are separated by a high-dimensional stable manifold of a saddle slow manifold. Large pathological responses appear if the fast subsystem escapes from this manifold to another attractor. The typical fast oscillations seen during the pathological responses are explained by the bifurcation diagram of the fast subsystem. Under normal conditions these oscillations are suppressed by slow inhibition. External stimulation temporarily releases the fast subsystem from this slow inhibition. The critical response can be formulated as a boundary value problem with one free parameter and can be used to study the dependency of the transition between the two response types upon the system parameters.
Original languageEnglish
Article number105103
JournalCommunications in Nonlinear Science and Numerical Simulation
Early online date9 Nov 2019
Publication statusPublished - 1 Apr 2020


  • Non-linear response
  • Slow-fast analysis
  • Cortical stimulation
  • Neural mass model


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