Hawkes processes with variable length memory and an infinite number of components

In this paper, we build a model for biological neural nets where the activity of the network is described by Hawkes processes having a variable length memory. The particularity of this paper is to deal with an infinite number of components. We propose a graphical construction of the process and we build, by means of a perfect simulation algorithm, a stationary version of the process. To carry out this algorithm, we make use of a Kalikow-type decomposition technique. Two models are described in this paper. In the first model, we associate to each edge of the interaction graph a saturation threshold that controls the influence of a neuron on another. In the second model, we impose a structure on the interaction graph leading to a cascade of spike trains. Such structures, where neurons are divided into layers can be found in retina

Probabilistic overall reconstruction of membrane-associated molecular dynamics from partial observations in rod-shaped bacteria

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An inverse problem approach to the probabilistic reconstruction of particle tracks on a censored and closed surface

Investigation of dynamics processes in cell biology very often relies on the observation of sampled regions without considering re-entrance events. In the case of image-based observations of bacteria cell wall processes, a large amount of the cylinder-shaped wall is not observed. It follows that biomolecules may disappear for a period of time in a region of interest, and then reappear later. Assuming Brownian motion with drift, we address the mathematical problem of the connection of particle trajectories on a cylindrical surface. A subregion of the cylinder is typically observed during the observation period, and biomolecules may appear or disappear in any place of the 3D surface. The performance of the method is mainly demonstrated on simulation data that mimic MreB dynamics observed in 2D time-lapse fluorescence microscopy

Probabilistic reconstruction of truncated particle trajectories on a closed surface

International audienceInvestigation of dynamic processes in cell biology very often relies on the observation in two dimensions of 3D biological processes. Consequently, the data are partial and statistical methods and models are required to recover the parameters describing the dynamical processes. In the case of molecules moving over the 3D surface, such as proteins on walls of bacteria cell, a large portion of the 3D surface is not observed in 2D-time microscopy. It follows that biomolecules may disappear for a period of time in a region of interest, and then reappear later. Assuming Brownian motion with drift, we address the mathematical problem of the reconstruction of biomolecules trajectories on a cylindrical surface. A subregion of the cylinder is typically recorded during the observation period, and biomolecules may appear or disappear in any place of the 3D surface. The performance of the method is demonstrated on simulated particle trajectories that mimic MreB protein dynamics observed in 2D time-lapse fluorescence microscopy in rod-shaped bacteria

Interacting particles system with a variable length memory

On étudie un système de particules en interactions. Deux types de processus sont utilisés pour modéliser le système. Tout d'abord des processus de Hawkes. On propose deux modèles pour lesquels on obtient l'existence et l'unicité d'une version stationnaire, ainsi qu'une construction graphique de la mesure stationnaire à l'aide d'une décomposition de type Kalikow et d'un algorithme de simulation parfaite.Le deuxième type de processus utilisés est un processus de Markov déterministe par morceaux (PDMP). On montre l'ergodicité de ce processus et propose un estimateur à noyau pour la fonction de taux de saut possédant une vitesse de convergence optimale dans L².We work on interacting particles systems. Two different types of processes are studied. A first model using Hawkes processes, for which we state existence and uniqueness of a stationnary version. We also propose a graphical construction of the stationnary measure by the mean of a Kalikow-type decomposition and a perfect simulation algorithm.The second model deals with Piecewise deterministic Markov processes (PDMP). We state ergodicity and propose a Kernel estimator for the jump rate function having an optimal speed of convergence in L²

Concentración y desigualdades de tipo Poincaré para un proceso de Markov puro con salto degenerado

We study Talagrand concentration and Poincaré type inequalities for unbounded pure jump Markov processes. In particular we focus on processes with degenerate jumps that depend on the past of the whole system, based on the model introduced by Galves and Löcherbach, in order to describe the activity of a biological neural network. As a result we obtain concentration properties.Estudiamos la concentración de Talagrand y las desigualdades de tipo Poincaré para procesos de Markov de salto puro no acotado. En particular, nos centramos en los procesos con saltos degenerados que dependen del pasado de todo el sistema, basado en el modelo introducido por Galves y Löcherbach, para describir la actividad de una red neuronal biológica. Como resultado obtenemos algunas propiedades de concentración. &nbsp

Exponential inequality for chaos based on sampling without replacement

International audienceWe are interested in the behavior of particular functionals, in a framework where the only source of randomness is a sampling without replacement. More precisely the aim of this short note is to prove an exponential concentration inequality for special U-statistics of order 2, that can be seen as chaos. (C) 2018 Published by Elsevier B.V