Literature DB >> 17052158

Bifurcation analysis of Jansen's neural mass model.

François Grimbert1, Olivier Faugeras.   

Abstract

We present a mathematical model of a neural mass developed by a number of people, including Lopes da Silva and Jansen. This model features three interacting populations of cortical neurons and is described by a six-dimensional nonlinear dynamical system. We address some aspects of its behavior through a bifurcation analysis with respect to the input parameter of the system. This leads to a compact description of the oscillatory behaviors observed in Jansen and Rit (1995) (alpha activity) and Wendling, Bellanger, Bartolomei, and Chauvel (2000) (spike-like epileptic activity). In the case of small or slow variation of the input, the model can even be described as a binary unit. Again using the bifurcation framework, we discuss the influence of other parameters of the system on the behavior of the neural mass model.

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Year:  2006        PMID: 17052158     DOI: 10.1162/neco.2006.18.12.3052

Source DB:  PubMed          Journal:  Neural Comput        ISSN: 0899-7667            Impact factor:   2.026


  44 in total

1.  Energy-based stochastic control of neural mass models suggests time-varying effective connectivity in the resting state.

Authors:  Roberto C Sotero; Amir Shmuel
Journal:  J Comput Neurosci       Date:  2011-11-01       Impact factor: 1.621

2.  Coupling relationship between the central pattern generator and the cerebral cortex with time delay.

Authors:  Qiang Lu
Journal:  Cogn Neurodyn       Date:  2015-03-10       Impact factor: 5.082

3.  Probing scale interaction in brain dynamics through synchronization.

Authors:  Alessandro Barardi; Daniel Malagarriga; Belén Sancristobal; Jordi Garcia-Ojalvo; Antonio J Pons
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2014-10-05       Impact factor: 6.237

4.  Analytically determining frequency and amplitude of spontaneous alpha oscillation in Jansen's neural mass model using the describing function method.

Authors:  Yao Xu; Chun-Hui Zhang; Ernst Niebur; Jun-Song Wang
Journal:  Chin Phys B       Date:  2018-04       Impact factor: 1.494

5.  Neural mass models as a tool to investigate neural dynamics during seizures.

Authors:  Tatiana Kameneva; Tianlin Ying; Ben Guo; Dean R Freestone
Journal:  J Comput Neurosci       Date:  2017-01-19       Impact factor: 1.621

6.  Homeostasis of brain dynamics in epilepsy: a feedback control systems perspective of seizures.

Authors:  Niranjan Chakravarthy; Kostas Tsakalis; Shivkumar Sabesan; Leon Iasemidis
Journal:  Ann Biomed Eng       Date:  2009-01-06       Impact factor: 3.934

7.  A constructive mean-field analysis of multi-population neural networks with random synaptic weights and stochastic inputs.

Authors:  Olivier Faugeras; Jonathan Touboul; Bruno Cessac
Journal:  Front Comput Neurosci       Date:  2009-02-18       Impact factor: 2.380

8.  Dynamic causal modelling for EEG and MEG.

Authors:  Stefan J Kiebel; Marta I Garrido; Rosalyn J Moran; Karl J Friston
Journal:  Cogn Neurodyn       Date:  2008-04-23       Impact factor: 5.082

9.  Dynamic Causal Models for phase coupling.

Authors:  W D Penny; V Litvak; L Fuentemilla; E Duzel; K Friston
Journal:  J Neurosci Methods       Date:  2009-07-02       Impact factor: 2.390

10.  Critical fluctuations in cortical models near instability.

Authors:  Matthew J Aburn; C A Holmes; James A Roberts; Tjeerd W Boonstra; Michael Breakspear
Journal:  Front Physiol       Date:  2012-08-20       Impact factor: 4.566

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