Academic Journal
State and parameter estimation for a neural model of local field potentials.
| Τίτλος: | State and parameter estimation for a neural model of local field potentials. |
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| 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).) |
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| 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 |
| ISSN: | 1432-1416 |
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| DOI: | 10.1007/s00285-026-02451-w |