Nonlinear filtering of a stochastic neural mass model
Sprache des Vortragstitels:
1st International Conference on Mathematical NeuroScience (ICMNS)
Sprache des Tagungstitel:
Neural mass models provide a useful framework for modelling mesoscopic neural dynamics. We briefly discuss the Jansen and Rit Neural Mass Model (JR-NMM) which has been introduced as a model in the context of electroencephalography (EEG) rhythms and evoked potentials and has been discussed for several times in the literature. In this poster, we first propose a stochastic version of the JR-NMM incorporating random input and we briefly discuss existence and uniqueness of the solution of these equations. Then, we apply the nonlinear filtering framework to the stochastic JR-NMM in order to solve the inverse problem, i.e. to compute certain parameters from EEG measurements. We determine an equation for the exact solution of the nonlinear filtering problem and solve it numerically by a continuous-time particle filter.