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State and parameter estimation for a neural model of local field potentials.

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Τίτλος: State and parameter estimation for a neural model of local field potentials.
Συγγραφείς: Avitabile D; Amsterdam Center for Dynamics and Computation, VU Amsterdam, Amsterdam, The Netherlands.; Inria Math Neuro Team, Inria Branch at the University of Montpellier, Montpellier, France., Lord GJ; Department of Mathematics, IMAPP, Radboud University, Nijmegen, The Netherlands., Meddouni K; Department of Mathematics, IMAPP, Radboud University, Nijmegen, The Netherlands. khadija.meddouni@ru.nl.
Πηγή: Journal of mathematical biology [J Math Biol] 2026 Oct 03; Vol. 93 (4). Date of Electronic Publication: 2026 Oct 03.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Springer Verlag Country of Publication: Germany NLM ID: 7502105 Publication Model: Electronic Cited Medium: Internet ISSN: 1432-1416 (Electronic) Linking ISSN: 03036812 NLM ISO Abbreviation: J Math Biol Subsets: MEDLINE
Imprint Name(s): Publication: Berlin : Springer Verlag
Original Publication: Wien, New York, Springer-Verlag.
Ιατρικοί όροι (MeSH): Models, Neurological*, Local Field Potential Measurement/statistics & numerical data ; Sleep/physiology ; Neurons/physiology ; Cerebral Cortex/physiology ; Animals ; Bayes Theorem ; Mice ; Mathematical Concepts ; Computer Simulation
Περίληψη: The study of cortical dynamics during different states such as decision making, sleep and movement, is an important topic in Neuroscience. Modelling efforts aim to relate the neural rhythms present in cortical recordings to the underlying dynamics responsible for their emergence. We present an effort to capture certain features of the neural activity from the cortex of a mouse during natural sleep, obtained through local field potential measurements. Our approach relies on using a discretized Wilson-Cowan Amari neural field model for neural activity, along with a data assimilation method that allows the Bayesian joint estimation of the state and parameters. We demonstrate the feasibility of our approach on synthetic measurements before applying it to a dataset available in literature. Our findings suggest the potential of our approach to characterize the parameters of the external forcing in the neural activity model, while simultaneously inferring a state that aligns with the observed signal.
(© 2026. The Author(s).)
References: J Neural Eng. 2008 Mar;5(1):1-8. (PMID: 18310806)
J Math Neurosci. 2018 Feb 5;8(1):3. (PMID: 29399710)
Biol Cybern. 1977 Aug 3;27(2):77-87. (PMID: 911931)
Biol Cybern. 2005 Aug;93(2):91-108. (PMID: 16059785)
Biol Cybern. 1979 Oct;34(3):137-50. (PMID: 486593)
Kybernetik. 1973 Sep;13(2):55-80. (PMID: 4767470)
Proc Natl Acad Sci U S A. 2022 Nov;119(44):e2204959119. (PMID: 36279469)
Cereb Cortex. 2021 Jan 1;31(1):324-340. (PMID: 32995860)
Phys Rev E Stat Nonlin Soft Matter Phys. 2009 May;79(5 Pt 1):051909. (PMID: 19518482)
J Neurosci Methods. 2002 Oct 30;120(2):203-9. (PMID: 12385770)
Contributed Indexing: Keywords: Bayesian inference; Local field potential; Parameter estimation; Particle filters; Wilson–Cowan–Amari model
Entry Date(s): Date Created: 20261003 Date Completed: 20261003 Latest Revision: 20261006
Update Code: 20261006
PubMed Central ID: PMC13634030
DOI: 10.1007/s00285-026-02451-w
PMID: 42828681
Βάση Δεδομένων: MEDLINE
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  – Url: https://dx.doi.org/doi:10.1007/s00285-026-02451-w
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  Data: State and parameter estimation for a neural model of local field potentials.
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  Data: <searchLink fieldCode="AU" term="%22Avitabile+D%22">Avitabile D</searchLink>; Amsterdam Center for Dynamics and Computation, VU Amsterdam, Amsterdam, The Netherlands.; Inria Math Neuro Team, Inria Branch at the University of Montpellier, Montpellier, France.<br /><searchLink fieldCode="AU" term="%22Lord+GJ%22">Lord GJ</searchLink>; Department of Mathematics, IMAPP, Radboud University, Nijmegen, The Netherlands.<br /><searchLink fieldCode="AU" term="%22Meddouni+K%22">Meddouni K</searchLink>; Department of Mathematics, IMAPP, Radboud University, Nijmegen, The Netherlands. khadija.meddouni@ru.nl.
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  Data: <searchLink fieldCode="MM" term="%22Models%2C+Neurological%22">Models, Neurological*</searchLink><br /><searchLink fieldCode="MH" term="%22Local+Field+Potential+Measurement%22">Local Field Potential Measurement</searchLink>/<searchLink fieldCode="MH" term="%22Local+Field+Potential+Measurement+statistics+%26+numerical+data%22">statistics & numerical data</searchLink> ; <searchLink fieldCode="MH" term="%22Sleep%22">Sleep</searchLink>/<searchLink fieldCode="MH" term="%22Sleep+physiology%22">physiology</searchLink> ; <searchLink fieldCode="MH" term="%22Neurons%22">Neurons</searchLink>/<searchLink fieldCode="MH" term="%22Neurons+physiology%22">physiology</searchLink> ; <searchLink fieldCode="MH" term="%22Cerebral+Cortex%22">Cerebral Cortex</searchLink>/<searchLink fieldCode="MH" term="%22Cerebral+Cortex+physiology%22">physiology</searchLink> ; <searchLink fieldCode="MH" term="%22Animals%22">Animals</searchLink> ; <searchLink fieldCode="MH" term="%22Bayes+Theorem%22">Bayes Theorem</searchLink> ; <searchLink fieldCode="MH" term="%22Mice%22">Mice</searchLink> ; <searchLink fieldCode="MH" term="%22Mathematical+Concepts%22">Mathematical Concepts</searchLink> ; <searchLink fieldCode="MH" term="%22Computer+Simulation%22">Computer Simulation</searchLink>
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  Data: The study of cortical dynamics during different states such as decision making, sleep and movement, is an important topic in Neuroscience. Modelling efforts aim to relate the neural rhythms present in cortical recordings to the underlying dynamics responsible for their emergence. We present an effort to capture certain features of the neural activity from the cortex of a mouse during natural sleep, obtained through local field potential measurements. Our approach relies on using a discretized Wilson-Cowan Amari neural field model for neural activity, along with a data assimilation method that allows the Bayesian joint estimation of the state and parameters. We demonstrate the feasibility of our approach on synthetic measurements before applying it to a dataset available in literature. Our findings suggest the potential of our approach to characterize the parameters of the external forcing in the neural activity model, while simultaneously inferring a state that aligns with the observed signal.<br /> (© 2026. The Author(s).)
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  Data: J Neural Eng. 2008 Mar;5(1):1-8. (PMID: <searchLink fieldCode="PM" term="%2218310806%22">18310806)</searchLink><br />J Math Neurosci. 2018 Feb 5;8(1):3. (PMID: <searchLink fieldCode="PM" term="%2229399710%22">29399710)</searchLink><br />Biol Cybern. 1977 Aug 3;27(2):77-87. (PMID: <searchLink fieldCode="PM" term="%22911931%22">911931)</searchLink><br />Biol Cybern. 2005 Aug;93(2):91-108. (PMID: <searchLink fieldCode="PM" term="%2216059785%22">16059785)</searchLink><br />Biol Cybern. 1979 Oct;34(3):137-50. (PMID: <searchLink fieldCode="PM" term="%22486593%22">486593)</searchLink><br />Kybernetik. 1973 Sep;13(2):55-80. (PMID: <searchLink fieldCode="PM" term="%224767470%22">4767470)</searchLink><br />Proc Natl Acad Sci U S A. 2022 Nov;119(44):e2204959119. (PMID: <searchLink fieldCode="PM" term="%2236279469%22">36279469)</searchLink><br />Cereb Cortex. 2021 Jan 1;31(1):324-340. (PMID: <searchLink fieldCode="PM" term="%2232995860%22">32995860)</searchLink><br />Phys Rev E Stat Nonlin Soft Matter Phys. 2009 May;79(5 Pt 1):051909. (PMID: <searchLink fieldCode="PM" term="%2219518482%22">19518482)</searchLink><br />J Neurosci Methods. 2002 Oct 30;120(2):203-9. (PMID: <searchLink fieldCode="PM" term="%2212385770%22">12385770)</searchLink>
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      – SubjectFull: Local Field Potential Measurement statistics & numerical data
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