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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).

Record type: Article

Abstract

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.

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More information

e-pub ahead of print date: 14 June 2011
Published date: August 2011
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 272484
URI: http://eprints.soton.ac.uk/id/eprint/272484
PURE UUID: 68c2e820-5999-4ec7-8890-0feba06da30c

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Date deposited: 17 Jun 2011 14:08
Last modified: 18 Jul 2017 06:24

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Contributors

Author: Anderw Zammit Mangion
Author: Ke Yuan
Author: Visakan Kadirkamanathan
Author: Guido Sanguinetti

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