Modelling and classification of neuronal dynamics through Generalised Linear Models
Dardenne, Denis
Promoteur(s) : Sacré, Pierre ; Drion, Guillaume
Date de soutenance : 24-jui-2024/25-jui-2024 • URL permanente : http://hdl.handle.net/2268.2/20447
Détails
Titre : | Modelling and classification of neuronal dynamics through Generalised Linear Models |
Titre traduit : | [fr] Modélisation et classification de dynamiques neuronales grâce aux Generalised Linear Models |
Auteur : | Dardenne, Denis |
Date de soutenance : | 24-jui-2024/25-jui-2024 |
Promoteur(s) : | Sacré, Pierre
Drion, Guillaume |
Membre(s) du jury : | Vandewalle, Gilles
Franci, Alessio |
Langue : | Anglais |
Nombre de pages : | 94 |
Mots-clés : | [en] GLM [en] Neuronal dynamics [en] Classification [en] Generalised linear model |
Discipline(s) : | Ingénierie, informatique & technologie > Multidisciplinaire, généralités & autres |
Public cible : | Chercheurs Professionnels du domaine Etudiants Grand public |
URL complémentaire : | https://github.com/DenisDardenne/TFE_neuron-GLM |
Institution(s) : | Université de Liège, Liège, Belgique |
Diplôme : | Master en ingénieur civil biomédical, à finalité spécialisée |
Faculté : | Mémoires de la Faculté des Sciences appliquées |
Résumé
[en] The generalised linear models, so-called GLM, are data-driven models able to capture a wide
variety nonlinear behaviours which can be difficult to simulate in classical mechanistic models. Consequently, GLMs occasionally find applications in neuron modelling, providing a
flexible solution to address the complexities of neuronal dynamics. Here, this work focus on
the main behaviours studied in the neuroscience research field to design relevant GLMs.
Initially, the performance of the GLM is meticulously evaluated based on factors such
as the length of the sequence being captured, the number of its basis functions and their
designs. The parameters that remain untested are transparently highlighted. While key
characteristics of those fitted filters are also discussed.
In a second time, deeper investigations are conducted into the feedback filter of the fitted
GLM. Although the GLMs differ according the training sequences, it exists notable similitude
between them when the training sequences belongs to the same family, e.g. spiking, bursting.
The extraction of features is possible thanks to the elements of the GLM. Therefore, the
classification of the original sequences according the features of the GLM is addressed at the
end of this these, it contributes to a comprehensive understanding of the intricate dynamics
underlying neuronal behaviour.
Through analysis and interpretation of GLM performance, this study offers valuable insights into the potential applications and limitations of these models in capturing and reproducing complex neuronal dynamics. By shedding light on the role of model parameters,
training sequences, and extracted features, this thesis helps in the design and interpretation
of GLM within the framework of neuronal representation.
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