Parameter and state estimation for a class of neural mass models

Romain Postoyan; Michelle Chong; Dragan Nesic; Levin Kuhlmann

doi:10.1109/CDC.2012.6427031

Parameter and state estimation for a class of neural mass models

Romain Postoyan, Michelle Chong, Dragan Nesic, Levin Kuhlmann

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

11 Citations (Scopus)

Abstract

We present an adaptive observer which asymptotically reconstructs the parameters and states of a model of interconnected cortical columns. Our study is motivated by the fact that the considered model is able to realistically reproduce patterns seen on (intracranial) electroencephalograms (EEG) by varying its parameters. Therefore, by estimating its parameters and states, we could gain a better understanding of the mechanisms underlying neurological phenomena such as seizures, which might lead to the prediction of the onsets of epileptic seizures. Simulations are performed to illustrate our results.

Original language	English
Title of host publication	51st IEEE Conference on Decision and Control, CDC 2012
Pages	2322-2327
Number of pages	6
DOIs	https://doi.org/10.1109/CDC.2012.6427031
Publication status	Published - 1 Dec 2012
Externally published	Yes
Event	IEEE Conference on Decision and Control 2012 - Grand Wailea, Maui, United States of America Duration: 10 Dec 2012 → 13 Dec 2012 Conference number: 51st https://ieeexplore.ieee.org/xpl/conhome/6416474/proceeding (Proceedings)

Conference

Conference	IEEE Conference on Decision and Control 2012
Abbreviated title	CDC 2012
Country/Territory	United States of America
City	Maui
Period	10/12/12 → 13/12/12
Internet address	https://ieeexplore.ieee.org/xpl/conhome/6416474/proceeding (Proceedings)

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10.1109/CDC.2012.6427031

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title = "Parameter and state estimation for a class of neural mass models",

abstract = "We present an adaptive observer which asymptotically reconstructs the parameters and states of a model of interconnected cortical columns. Our study is motivated by the fact that the considered model is able to realistically reproduce patterns seen on (intracranial) electroencephalograms (EEG) by varying its parameters. Therefore, by estimating its parameters and states, we could gain a better understanding of the mechanisms underlying neurological phenomena such as seizures, which might lead to the prediction of the onsets of epileptic seizures. Simulations are performed to illustrate our results.",

author = "Romain Postoyan and Michelle Chong and Dragan Nesic and Levin Kuhlmann",

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doi = "10.1109/CDC.2012.6427031",

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AU - Chong, Michelle

AU - Nesic, Dragan

AU - Kuhlmann, Levin

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N2 - We present an adaptive observer which asymptotically reconstructs the parameters and states of a model of interconnected cortical columns. Our study is motivated by the fact that the considered model is able to realistically reproduce patterns seen on (intracranial) electroencephalograms (EEG) by varying its parameters. Therefore, by estimating its parameters and states, we could gain a better understanding of the mechanisms underlying neurological phenomena such as seizures, which might lead to the prediction of the onsets of epileptic seizures. Simulations are performed to illustrate our results.

AB - We present an adaptive observer which asymptotically reconstructs the parameters and states of a model of interconnected cortical columns. Our study is motivated by the fact that the considered model is able to realistically reproduce patterns seen on (intracranial) electroencephalograms (EEG) by varying its parameters. Therefore, by estimating its parameters and states, we could gain a better understanding of the mechanisms underlying neurological phenomena such as seizures, which might lead to the prediction of the onsets of epileptic seizures. Simulations are performed to illustrate our results.

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DO - 10.1109/CDC.2012.6427031

M3 - Conference Paper

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BT - 51st IEEE Conference on Decision and Control, CDC 2012

T2 - IEEE Conference on Decision and Control 2012

Y2 - 10 December 2012 through 13 December 2012

ER -

Parameter and state estimation for a class of neural mass models

Abstract

Conference

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