~~NOCACHE~~

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\\ ==== Prochaines séances ====
[[seminaires:SeminaireMAV:index|Séminaire Modélisation aléatoire du vivant]]\\
Mercredi 6 mai 2026, 11 heures, 16-26.209\\
**Alexandre Chaussard** (LPSM (MAV)) //Independent Component Discovery in Temporal Count Data//
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in data collection are producing growing volumes of temporal count observations, making adapted modeling increasingly necessary. In this work, we introduce a generative framework for independent component analysis of temporal count data, combining regime-adaptive dynamics with Poisson log-normal emissions. The model identifies disentangled components with regime-dependent contributions, enabling representation learning and perturbations analysis. Notably, we establish the identifiability of the model, supporting principled interpretation. To learn the parameters, we propose an efficient amortized variational inference procedure. Experiments on simulated data evaluate recovery of the mixing function and latent sources across diverse settings, while an in vivo longitudinal gut microbiome study reveals microbial co-variation patterns and regime shifts consistent with clinical perturbations.
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[[seminaires:SeminaireMAV:index|Séminaire Modélisation aléatoire du vivant]]\\
Mercredi 10 juin 2026, 11 heures, 16-26.209\\
**Aurélien Velleret**  //On the LAN and LAMN Properties for Mean-Field Model of Interacting Neurons//
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We study a mean-field system of interacting particles represented by stochastic differential equations (SDE's) with jumps, introduced in [1] as a model describing the time evolution of neuronal potentials in the human brain. Depending on the renormalization of the interaction : either averaging scaling (1/N) or diffusive scaling (1/\sqrt N)-it was shown in [1] and [2] that, as the number of particles tends to infinity, the system exhibit the propagation of chaos and conditional propagation of chaos, respectively. 
Assuming that the jump intensity depends on an unknown parameter, we establish the Local Asymptotic Normality (LAN) property in the averaging case and the Local Asymptotic Mixed Normality (LAMN) in the diffusive case. Some implications regarding the performance of the maximum likelihood estimator are also derived. 
The limiting Fisher information depends on the limit of the empirical measure of the system. It is deterministic in the averaging regime, random in diffusive regime. This phenomenon explains the dichotomy between LAN and LAMN. 
Based on joint works with Xavier Erny, Aline Duarte, Eva Löcherbach, Dasha Loukianova. 

[1] Fournier, N. and Löcherbach, E. (2016). On a toy model of interacting neurons. Ann.Inst. Henri Poincaré Probab. Stat. 52(4), 1844–1876. 
[2] Erny, X., Löcherbach, E. and Loukianova, D. (2021). Conditional propagation of chaos for mean field systems of interacting neurons. Electron. J. Probab. 26, 1–25.
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[[seminaires:SeminaireMAV:index|Séminaire Modélisation aléatoire du vivant]]\\
Mercredi 1 juillet 2026, 11 heures, 16-26.209\\
**Barbara Bricout** (LPSM (MAV)) //Non encore annoncé.//
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