Dynamic modeling of neuronal responses in fMRI using cubature Kalman filtering

Keyphrases

• Functional Magnetic Resonance Imaging 100%
• Neuronal Responses 100%
• Cubature Kalman Filtering 100%
• Dynamic Modeling 100%
• Adaptation 33%
• Systems-based 33%
• Varying Parameters 33%
• Computational Efficiency 33%
• Hemodynamic Model 33%
• Functional Architecture 33%
• Posterior Estimates 33%
• Model Fitting 33%
• Electrophysiological Study 33%
• Neuronal Network 33%
• Auditory Brainstem Response 33%
• Hidden States 33%
• Generative Models 33%
• Ordinary Differential Equations 33%
• Spontaneous Fluctuations 33%
• Continuous-time 33%
• Discrete Observations 33%
• Bayesian Filtering 33%
• Stochastic Differential Equations 33%
• Hidden Parameters 33%
• Observation Process 33%
• Biological Time Series 33%
• Coupled Dynamical Systems 33%
• Unknown Exogenous Inputs 33%
• Dynamic Expectation Maximization 33%
• Bayesian Filter 33%
• Dynamic Causal Model 33%

Computer Science

• Generative Model 100%
• Computational Efficiency 100%
• Underlying System 100%
• Discrete Observation 100%
• Continuous Time 100%
• Comparative Evaluation 100%
• Observation Process 100%
• Stochastic Differential 100%
• Functional Architecture 100%
• Dynamical System 100%

Mathematics

• Bayesian 100%
• Kalman Filtering 100%
• Dynamical Modelling 100%
• Dynamical System 50%
• Ordinary Differential Equation 50%
• Hidden State 50%
• Wide Variety 50%
• Continuous Time 50%
• Neuronal System 50%
• Causal Model 50%
• Stochastic Differential Equation 50%
• Time-Varying Parameter 50%
• Observation Process 50%
• Generative Model 50%