ArticleJournal of mathematical neuroscience2017
A Stochastic Version of the Jansen and Rit Neural Mass Model: Analysis and Numerics.
Article in Journal of mathematical neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Artificial intelligence as a surrogate brain: bridging neural dynamical models and data.National science review · 2026Review
- Homeodynamic feedback inhibition control in whole-brain simulations.PLoS computational biology · 2024Article
- Comparing individual and group-level simulated neurophysiological brain connectivity using the Jansen and Rit neural mass model.Network neuroscience (Cambridge, Mass.) · 2023Article
- Fully Closed Loop Test Environment for Adaptive Implantable Neural Stimulators Using Computational Models.Journal of medical devices · 2022Article
- Phase synchronization and measure of criticality in a network of neural mass models.Scientific reports · 2022Article
- Spectral density-based and measure-preserving ABC for partially observed diffusion processes. An illustration on Hamiltonian SDEs.Statistics and computing · 2020Article
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3 authors.
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Abstract
Neural mass models provide a useful framework for modelling mesoscopic neural dynamics and in this article we consider the Jansen and Rit neural mass model (JR-NMM). We formulate a stochastic version of it which arises by incorporating random input and has the structure of a damped stochastic Hamiltonian system with nonlinear displacement. We then investigate path properties and moment bounds of the model. Moreover, we study the asymptotic behaviour of the model and provide long-time stability results by establishing the geometric ergodicity of the system, which means that the system-independently of the initial values-always converges to an invariant measure. In the last part, we simulate the stochastic JR-NMM by an efficient numerical scheme based on a splitting approach which preserves the qualitative behaviour of the solution.
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