Adaptation and Fatigue Model for Neuron Networks and Large Time Asymptotics in a Nonlinear Fragmentation Equation

Khashayar Pakdaman (1) , Benoît Perthame (2, 3, 4) , Delphine Salort (1)

1 IJM (UMR_7592) - Institut Jacques Monod
2 MAMBA - Modelling and Analysis for Medical and Biological Applications
3 LJLL - Laboratoire Jacques-Louis Lions
4 IUF - Institut universitaire de France

Motivated by a model for neural networks with adaptation and fatigue, we study a conservative fragmentation equation that describes the density probability of neurons with an elapsed time s after its last discharge.
In the linear setting, we extend an argument by Laurençot and Perthame to prove exponential decay to the steady state. This extension allows us to handle coefficients that have a large variation rather than constant coefficients. In another extension of the argument, we treat a weakly nonlinear case and prove total desynchronization in the network. For greater nonlinearities, we present a numerical study of the impact of the fragmentation term on the appearance of synchronization of neurons in the network using two "extreme" cases.
Mathematics Subject Classification (2000)2010: 35B40, 35F20, 35R09, 92B20.

Mots clés: Desynchronization; Large time asymptotics; Neural networks; Fragmentation equation

Domaines: Neurosciences [q-bio.NC]; Sciences cognitives

Khashayar Pakdaman, Benoît Perthame, Delphine Salort. Adaptation and Fatigue Model for Neuron Networks and Large Time Asymptotics in a Nonlinear Fragmentation Equation. Journal of Mathematical Neuroscience, 2014, 4 (1), pp.14. ⟨10.1186/2190-8567-4-14⟩. ⟨hal-01054561⟩