A stochastic system with infinite interacting components to model the time evolution of the membrane potentials of a population of neurons

Abstract

We consider a new class of interacting particle systems with a countable number of interacting components. The system represents the time evolution of the membrane potentials of an infinite set of interacting neurons. We prove the existence and uniqueness of the process, using a perfect simulation procedure. We show that this algorithm is successful, that is, we show that the number of steps of the algorithm is almost surely finite. We also construct a perfect simulation procedure for the coupling of a process with a finite number of neurons and the process with a infinite number of neurons. As a consequence, we obtain an upper bound for the error that we make when sampling from a finite set of neurons instead of the infinite set of neurons.

arXiv admin note: text overlap with arXiv:0904.1845 by other authors

References in corpus (1)

• A discrete time neural network model with spiking neurons II. Dynamics with noise

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