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

Zammit Mangion, Anderw, Yuan, Ke, Kadirkamanathan, Visakan, Niranjan, Mahesan and Sanguinetti, Guido (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). (PMID:21521047).


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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. Themethods 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: Article
Digital Object Identifier (DOI): doi:10.1162/NECO_a_00156
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QP Physiology
Organisations: Southampton Wireless Group
ePrint ID: 272484
Date :
Date Event
14 June 2011e-pub ahead of print
August 2011Published
Date Deposited: 17 Jun 2011 14:08
Last Modified: 17 Apr 2017 17:44
Further Information:Google Scholar

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