A continuous-time particle filter for a nonlinear stochastic neural mass model
Sprache des Vortragstitels:
10th IMACS Seminar on Monte Carlo Methods
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 used for several applications, e.g. for detecting epileptic diseases or generating visual evoked potentials. In this talk, we first propose a stochastic version of the JR-NMM incorporating random input and we briefly discuss existence and uniqueness of the solution of this system of 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.