Online Variational Inference for State-Space Models with Point-Process Observations

Andrew Zammit-Mangion, Ke Yuan, Visakan Kadirkamanathan, Mahesan Niranjan, Guido Sanguinetti

Research output: Contribution to journalArticle (Academic Journal)peer-review

13 Citations (Scopus)

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. 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.

Original languageEnglish
Pages (from-to)1967-1999
Number of pages33
JournalNeural Computation
Volume23
Issue number8
Publication statusPublished - Aug 2011

Keywords

  • MONTE-CARLO METHODS
  • SOLITARY TRACT
  • VARIABILITY
  • NUCLEUS
  • SYSTEMS
  • FILTERS
  • NOISE
  • RAT

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