Well-posedness and numerical analysis of an elapsed time model with strongly coupled neural networks

Mauricio Sepúlveda (1) , Nicolas Torres (2) , Luis Miguel Villada (3)

1 CI²MA - Centro de Investigación en Ingeniería Matemática [Concepción]
2 E-GRAN-AM SPAIN - Departamento de Matematica Aplicada [Granada]
3 Departamento de Matemática, Universidad del Bío-Bío

The elapsed time equation is an age-structured model that describes dynamics of interconnected spiking neurons through the elapsed time since the last discharge, leading to many interesting questions on the evolution of the system from a mathematical and biological point of view. In this work, we first deal with the case when transmission after a spike is instantaneous and the case when there exists a distributed delay that depends on previous history of the system, which is a more realistic assumption. Then we study the well-posedness and the numerical analysis of the elapsed time models. For existence and uniqueness we improve the previous works by relaxing some hypothesis on the nonlinearity, including the strongly excitatory case, while for the numerical analysis we prove that the approximation given by the explicit upwind scheme converges to the solution of the non-linear problem. We also show some numerical simulations to compare the behavior of the system in the case of instantaneous transmission with the case of distributed delay under different parameters, leading to solutions with different asymptotic profiles.

Mots clés: Structured equations; Mathematical neuroscience; Delay differential equations; Well-posedness; Numerical analysis; Periodic solutions

Domaines: Equations aux dérivées partielles [math.AP]; Analyse numérique [math.NA]

Mauricio Sepúlveda, Nicolas Torres, Luis Miguel Villada. Well-posedness and numerical analysis of an elapsed time model with strongly coupled neural networks. 2023. ⟨hal-04222675v2⟩