Online variational inference for state-space models with point-process observations

Mangion, A. Z., Yuan, K. , Kadirkamanathan, V., Niranjan, M. and Sanguinetti, G. (2011) Online variational inference for state-space models with point-process observations. Neural Computation, 23(8), pp. 1967-1999. (doi: 10.1162/NECO_a_00156)

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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.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Yuan, Dr Ke
Authors: Mangion, A. Z., Yuan, K., Kadirkamanathan, V., Niranjan, M., and Sanguinetti, G.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Neural Computation
Publisher:Massachusetts Institute of Technology Press
ISSN (Online):1530-888X
Published Online:14 June 2011

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