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

Online variational inference for state-space models with point-process observations
Online variational inference for state-space models with point-process observations
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.
1967-1999
Zammit Mangion, Anderw
d581879f-2569-4bd1-867f-215f5891c8d3
Yuan, Ke
ae93001b-9371-4d70-88a5-ea53b2c429a9
Kadirkamanathan, Visakan
f4332f52-32d4-45c2-9de7-2bb5623a39ff
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Sanguinetti, Guido
da9b015b-8a80-4f4c-a625-2276b3520f8c
Zammit Mangion, Anderw
d581879f-2569-4bd1-867f-215f5891c8d3
Yuan, Ke
ae93001b-9371-4d70-88a5-ea53b2c429a9
Kadirkamanathan, Visakan
f4332f52-32d4-45c2-9de7-2bb5623a39ff
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Sanguinetti, Guido
da9b015b-8a80-4f4c-a625-2276b3520f8c

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), 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. 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.

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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
ORCID for Mahesan Niranjan: ORCID iD orcid.org/0000-0001-7021-140X

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Date deposited: 17 Jun 2011 14:08
Last modified: 15 Mar 2024 03:29

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Contributors

Author: Anderw Zammit Mangion
Author: Ke Yuan
Author: Visakan Kadirkamanathan
Author: Mahesan Niranjan ORCID iD
Author: Guido Sanguinetti

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