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J Math Neurosci. 2014 Jul 24;4:14. doi: 10.1186/2190-8567-4-14. eCollection 2014.

Adaptation and fatigue model for neuron networks and large time asymptotics in a nonlinear fragmentation equation.

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Institut Jacques Monod, UMR 7592, Université Paris Diderot, 75205, Paris, France.
Laboratoire Jacques-Louis Lions, UMR 7598, UPMC Université Paris 06, Sorbonne Universités, 75005, Paris, France ; INRIA-Paris-Rocquencourt EPI BANG, Paris, France ; Institut Universitaire de France, Paris, 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.


Desynchronization; Fragmentation equation; Large time asymptotics; Neural networks

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