Efficent numerical integration and nonlinear filtering of a stochastic Jansen and Rit model

Description

Neural mass models provide a useful framework for modelling mesoscopic neural dynamics and in this poster we consider the Jansen and Rit Neural Mass Model (JR-NMM). This system of ODEs has been introduced as a model in the context of electroencephalography (EEG) rhythms and evoked potentials and has been proposed as an underlying model in various application settings. We use a stochastic version of the JR-NMM which has the structure of a stochastic Hamiltonian with a nonlinear displacement and has been shown to have a number of structural properties, such as moment bounds and ergodicity. We discuss the quality of simulations based on an e

Period	01 Jun 2017
Event title	International Conference on Mathematical Neuroscience
Event type	Conference
Location	United StatesShow on map