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

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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
Subjects:	Q Science > QA Mathematics T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Faculty of Science > 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:	Date Event 4 June 2008 Published
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
Funder:	Engineering and Physical Sciences Research Council (EPSRC), United States. National Aeronautics and Space Administration (NASA)

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