A unifying review of linear Gaussian models
Neural Computation
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Estimating a state-space model from point process observations
Neural Computation
Dynamic analysis of neural encoding by point process adaptive filtering
Neural Computation
Online Model Selection Based on the Variational Bayes
Neural Computation
Sequential Monte Carlo Methods to Train Neural Network Models
Neural Computation
Factorial Switching Linear Dynamical Systems Applied to Physiological Condition Monitoring
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inferring parameters and structure of latent variable models by variational bayes
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Particle filters for state-space models with the presence ofunknown static parameters
IEEE Transactions on Signal Processing
Computational Statistics & Data Analysis
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We present a variational Bayesian (VB) approach for the state and parameter inference of a state-space model with point-process observations, a physiologically plausible model for signal processing of spike data. We also give the derivation of a variational smoother, as well as an efficient online filtering algorithm, which can also be used to track changes in physiological parameters. The methods are assessed on simulated data, and results are compared to expectation-maximization, as well as Monte Carlo estimation techniques, in order to evaluate the accuracy of the proposed approach. The VB filter is further assessed on a data set of taste-response neural cells, showing that the proposed approach can effectively capture dynamical changes in neural responses in real time.