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On a toy network of neurons interacting through their dendrites

Nicolas Fournier 1 Etienne Tanré 2 Romain Veltz 3 
2 TOSCA - TO Simulate and CAlibrate stochastic models
CRISAM - Inria Sophia Antipolis - Méditerranée , IECL - Institut Élie Cartan de Lorraine : UMR7502
3 MATHNEURO - Mathématiques pour les Neurosciences
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Consider a large number $n$ of neurons, each being connected to approximately $N$ other ones, chosen at random. When a neuron spikes, which occurs randomly at some rate depending on its electric potential, its potential is set to a minimum value $v_{min}$, and this initiates, after a small delay, two fronts on the (linear) dendrites of all the neurons to which it is connected. Fronts move at constant speed. When two fronts (on the dendrite of the same neuron) collide, they annihilate. When a front hits the soma of a neuron, its potential is increased by a small value $w_n$. Between jumps, the potentials of the neurons are assumed to drift in $[v_{min},\infty)$, according to some well-posed ODE. We prove the existence and uniqueness of a heuristically derived mean-field limit of the system when $n,N \to \infty$ with $w_n \simeq N^{-1/2}$. We make use of some recent versions of the results of Deuschel and Zeitouni \cite{dz} concerning the size of the longest increasing subsequence of an i.i.d. collection of points in the plan. We also study, in a very particular case, a slightly different model where the neurons spike when their potential reach some maximum value $v_{max}$, and find an explicit formula for the (heuristic) mean-field limit.
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Submitted on : Monday, April 29, 2019 - 3:33:46 PM
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Nicolas Fournier, Etienne Tanré, Romain Veltz. On a toy network of neurons interacting through their dendrites. Annales de l'Institut Henri Poincaré (B) Probabilités et Statistiques, 2020, 56 (2), pp.1041-1071. ⟨10.1214/19-AIHP993⟩. ⟨hal-01707663v2⟩



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