2013/11/18 by Olivier Faugeras, Faugeras, Olivier, James MacLaurin +1 · 1 citation
Mathematics · Neuroscience · Physics and Astronomy · #28C20 #34F05 #34K50 #37L55 #60B11 #60F10 #60G10 #60G15 #60G57 #60G60 #60H10 #62M45 #92C20 #FOS: Mathematics #Neural dynamics and brain function #Neuroscience and Neuropharmacology Research #Probability (math.PR) #math.PR #msc:28C20 #msc:34F05 #msc:34K50 #msc:37L55 #msc:60B11 #msc:60F10 #msc:60G10 #msc:60G15 #msc:60G57 #msc:60G60 #msc:60H10 #msc:62M45 #msc:92C20 #stochastic dynamics and bifurcation
paper · pdf · doi:10.48550/arxiv.1311.4400
This paper has been withdrawn by the authors. 50 pages. arXiv admin note: substantial text overlap with arXiv:1302.1029 This paper has been withdrawn because I meant to replace arXiv:1302.1029, not arXiv:1311.4400
openalex publication_date 2013/11/18 · arxiv created 2013/12/13 · arxiv updated 2013/12/16 · openalex created_date 2019/06/27 · openalex updated_date 2026/07/28
We study the asymptotic law of a network of interacting neurons when the number of neurons becomes infinite. Given a completely connected network of neurons in which the synaptic weights are Gaussian correlated random variables, we describe the asymptotic law of the network when the number of neurons goes to infinity. We introduce the process-level empirical measure of the trajectories of the solutions to the equations of the finite network of neurons and the averaged law (with respect to the synaptic weights) of the trajectories of the solutions to the equations of the network of neurons. The main result of this article is that the image law through the empirical measure satisfies a large deviation principle with a good rate function which is shown to have a unique global minimum. Our analysis of the rate function allows us also to characterize the limit measure as the image of a stationary Gaussian measure defined on a transformed set of trajectories.