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Inferential framework for nonstationary dynamics. II, Application to a model of physiological signaling
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Duggento, Andrea, Luchinsky, Dmitri G., Smelyanskiy, Vadim N., Khovanov, I. A. and McClintock, P. V. E. (2008) Inferential framework for nonstationary dynamics. II, Application to a model of physiological signaling. Physical Review E (Statistical, Nonlinear, and Soft Matter Physics), Vol.77 (No.6). Article: 061106. doi:10.1103/PhysRevE.77.061106 ISSN 1539-3755.
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Official URL: http://dx.doi.org/10.1103/PhysRevE.77.061106
Abstract
The problem of how to reconstruct the parameters of a stochastic nonlinear dynamical system when these are time-varying is considered in the context of online decoding of physiological information from neuron signaling activity. To model the spiking of neurons, a set of FitzHugh-Nagumo (FHN) oscillators is used. It is assumed that only a fast dynamical variable can be detected for each neuron, and that the monitored signals are mixed by an unknown measurement matrix. The Bayesian framework introduced in Paper I (Phys. Rev. E 77, 06110500 (2008)) is applied both for reconstruction of the model parameters and elements of the measurement matrix, and for inference of the time-varying parameters in the non-stationary system. It is shown that the proposed approach is able to reconstruct unmeasured (hidden) slow variables of the FHN oscillators, to learn to model each individual neuron, and to track continuous, random and step-wise variations of the control parameter for each neuron in real time.
Item Type: | Journal Article | ||||
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Subjects: | Q Science > QA Mathematics T Technology > TA Engineering (General). Civil engineering (General) |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||
Library of Congress Subject Headings (LCSH): | Nonlinear theories, Stochastic systems, Neurons -- Mathematical models | ||||
Journal or Publication Title: | Physical Review E (Statistical, Nonlinear, and Soft Matter Physics) | ||||
Publisher: | American Physical Society | ||||
ISSN: | 1539-3755 | ||||
Official Date: | 4 June 2008 | ||||
Dates: |
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Volume: | Vol.77 | ||||
Number: | No.6 | ||||
Number of Pages: | 10 | ||||
Page Range: | Article: 061106 | ||||
DOI: | 10.1103/PhysRevE.77.061106 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access | ||||
Date of first compliant deposit: | 18 December 2015 | ||||
Date of first compliant Open Access: | 18 December 2015 | ||||
Funder: | Engineering and Physical Sciences Research Council (EPSRC), United States. National Aeronautics and Space Administration (NASA) |
Data sourced from Thomson Reuters' Web of Knowledge
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