Home > Research > Publications & Outputs > Inferential framework for nonstationary dynamic...

Research

Associated organisational unit

Electronic data

Links

Text available via DOI:

View graph of relations

Inferential framework for nonstationary dynamics. II. Application to a model of physiological signaling.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published

Standard

Inferential framework for nonstationary dynamics. II. Application to a model of physiological signaling. / Duggento, Andrea; Luchinsky, Dmitri G.; Smelyanskiy, Vadim N. et al.
In: Physical Review E, Vol. 77, No. 6, 04.06.2008, p. 1-10.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Duggento, A, Luchinsky, DG, Smelyanskiy, VN, Khovanov, I & McCkintock, PVE 2008, 'Inferential framework for nonstationary dynamics. II. Application to a model of physiological signaling.', Physical Review E, vol. 77, no. 6, pp. 1-10. https://doi.org/10.1103/PhysRevE.77.061106

APA

Duggento, A., Luchinsky, D. G., Smelyanskiy, V. N., Khovanov, I., & McCkintock, P. V. E. (2008). Inferential framework for nonstationary dynamics. II. Application to a model of physiological signaling. Physical Review E, 77(6), 1-10. https://doi.org/10.1103/PhysRevE.77.061106

Vancouver

Duggento A, Luchinsky DG, Smelyanskiy VN, Khovanov I, McCkintock PVE. Inferential framework for nonstationary dynamics. II. Application to a model of physiological signaling. Physical Review E. 2008 Jun 4;77(6):1-10. doi: 10.1103/PhysRevE.77.061106

Author

Duggento, Andrea ; Luchinsky, Dmitri G. ; Smelyanskiy, Vadim N. et al. / Inferential framework for nonstationary dynamics. II. Application to a model of physiological signaling. In: Physical Review E. 2008 ; Vol. 77, No. 6. pp. 1-10.

Bibtex

@article{284e4dde80b74e84a275456882a9c62e,
title = "Inferential framework for nonstationary dynamics. II. Application to a model of physiological signaling.",
abstract = "The problem of how to reconstruct the parameters of a stochastic nonlinear dynamical system when they 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 immediately preceding this paper 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 nonstationary 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 stepwise variations of the control parameter for each neuron in real time.",
author = "Andrea Duggento and Luchinsky, {Dmitri G.} and Smelyanskiy, {Vadim N.} and Igor Khovanov and McCkintock, {Peter V. E.}",
year = "2008",
month = jun,
day = "4",
doi = "10.1103/PhysRevE.77.061106",
language = "English",
volume = "77",
pages = "1--10",
journal = "Physical Review E",
issn = "1539-3755",
publisher = "American Physical Society",
number = "6",

}

RIS

TY - JOUR

T1 - Inferential framework for nonstationary dynamics. II. Application to a model of physiological signaling.

AU - Duggento, Andrea

AU - Luchinsky, Dmitri G.

AU - Smelyanskiy, Vadim N.

AU - Khovanov, Igor

AU - McCkintock, Peter V. E.

PY - 2008/6/4

Y1 - 2008/6/4

N2 - The problem of how to reconstruct the parameters of a stochastic nonlinear dynamical system when they 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 immediately preceding this paper 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 nonstationary 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 stepwise variations of the control parameter for each neuron in real time.

AB - The problem of how to reconstruct the parameters of a stochastic nonlinear dynamical system when they 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 immediately preceding this paper 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 nonstationary 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 stepwise variations of the control parameter for each neuron in real time.

U2 - 10.1103/PhysRevE.77.061106

DO - 10.1103/PhysRevE.77.061106

M3 - Journal article

VL - 77

SP - 1

EP - 10

JO - Physical Review E

JF - Physical Review E

SN - 1539-3755

IS - 6

ER -