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Bifurcation analysis of two coupled Jansen-Rit neural mass models
Author(s) -
Saeed Ahmadizadeh,
Philippa J. Karoly,
Dragan Nešić,
David B. Grayden,
Mark Cook,
Daniel Soudry,
Dean R. Freestone
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0192842
Subject(s) - bifurcation , artificial neural network , epilepsy , computer science , bifurcation theory , spike (software development) , neuroscience , biological neural network , electroencephalography , statistical physics , nerve net , physics , biological system , pattern recognition (psychology) , artificial intelligence , biology , nonlinear system , software engineering , quantum mechanics
We investigate how changes in network structure can lead to pathological oscillations similar to those observed in epileptic brain. Specifically, we conduct a bifurcation analysis of a network of two Jansen-Rit neural mass models, representing two cortical regions, to investigate different aspects of its behavior with respect to changes in the input and interconnection gains. The bifurcation diagrams, along with simulated EEG time series, exhibit diverse behaviors when varying the input, coupling strength, and network structure. We show that this simple network of neural mass models can generate various oscillatory activities, including delta wave activity, which has not been previously reported through analysis of a single Jansen-Rit neural mass model. Our analysis shows that spike-wave discharges can occur in a cortical region as a result of input changes in the other region, which may have important implications for epilepsy treatment. The bifurcation analysis is related to clinical data in two case studies.

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