The mean-field equation of a leaky integrate-and-fire neural network: measure solutions and steady states

Grégory Dumont (1) , Pierre Gabriel (2)

1 Group for Neural Theory [Paris]
2 LMV - Laboratoire de Mathématiques de Versailles

Neural network dynamics emerge from the interaction of spiking cells. One way to formulate the problem is through a theoretical framework inspired by ideas coming from statistical physics, the so-called mean-field theory. In this document, we investigate different issues related to the mean-field description of an excitatory network made up of leaky integrate-and-fire neurons. The description is written in the form a nonlinear partial differential equation which is known to blow up in finite time when the network is strongly connected. We prove that in a moderate coupling regime the equation is globally well-posed in the space of measures, and that there exist stationary solutions. In the case of weak connectivity we also demonstrate the uniqueness of the steady state and its global exponential stability. The method to show those mathematical results relies on a contraction argument of Doeblin's type in the linear case, which corresponds to a population of non-interacting units.

Mots clés: Doeblin's condition; measure solution; piecewise deterministic Markov process; neural network; leaky integrate-and-fire; relaxation to steady state

Domaines: Equations aux dérivées partielles [math.AP]

Grégory Dumont, Pierre Gabriel. The mean-field equation of a leaky integrate-and-fire neural network: measure solutions and steady states. 2018. ⟨hal-01617088v3⟩