Online variational inference for state-space models
with point process observations
We present a variational Bayesian 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. 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 is given. The methods are assessed on simulated data, and results are compared to expectation-maximisation 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.