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

Journal 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. 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.

Authors


  •   Zammit-Mangion, Andrew
  •   Yuan, K (external author)
  •   Kadirkamanathan, V (external author)
  •   Sanguinetti, Guido (external author)

Publication Date


  • 2011

Citation


  • Mangion, A. Zammit., Yuan, K., Kadirkamanathan, V. & Sanguinetti, G. (2011). Online variational inference for state-space models with point-process observations. Neural Computation, 23 (8), 1967-1999.

Scopus Eid


  • 2-s2.0-79959658819

Has Global Citation Frequency


Number Of Pages


  • 32

Start Page


  • 1967

End Page


  • 1999

Volume


  • 23

Issue


  • 8

Place Of Publication


  • United States

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.

Authors


  •   Zammit-Mangion, Andrew
  •   Yuan, K (external author)
  •   Kadirkamanathan, V (external author)
  •   Sanguinetti, Guido (external author)

Publication Date


  • 2011

Citation


  • Mangion, A. Zammit., Yuan, K., Kadirkamanathan, V. & Sanguinetti, G. (2011). Online variational inference for state-space models with point-process observations. Neural Computation, 23 (8), 1967-1999.

Scopus Eid


  • 2-s2.0-79959658819

Has Global Citation Frequency


Number Of Pages


  • 32

Start Page


  • 1967

End Page


  • 1999

Volume


  • 23

Issue


  • 8

Place Of Publication


  • United States