J. Baladron, D. Fasoli, O. Faugeras, and J. Touboul, Mean-field description and propagation of chaos in networks of Hodgkin-Huxley and FitzHugh-Nagumo neurons, The Journal of Mathematical Neuroscience, vol.2, issue.1, p.2012
DOI : 10.1186/2190-8567-2-10
URL : https://hal.archives-ouvertes.fr/inserm-00732288

R. John, N. C. Baxter, and . Jain, An approximation condition for large deviations and some applications, Convergence in Ergodic Theory and Probability. Ohio State University Mathematical Research Institute Publications, 1993.

W. Bryc and A. Dembo, On large deviations of empirical measures for stationary Gaussian processes, Stochastic Processes and Their Applications, pp.23-34, 1995.
DOI : 10.1016/0304-4149(95)00003-P

W. Bryc and A. Dembo, Large deviations and strong mixing, Annales de l'IHP Probabilités et statistiques, pp.549-569, 1996.

T. Chiyonobu and S. Kusuoka, The large deviation principle for hypermixing processes. Probability Theory and Related Fields, pp.627-649, 1988.

A. Dembo and O. Zeitouni, Large deviations techniques, 1997.

J. D. Deuschel, D. W. Stroock, and H. Zessin, Microcanonical distributions for lattice gases, Communications in Mathematical Physics, vol.77, issue.1, 1991.
DOI : 10.1007/BF02102730

J. Deuschel and D. W. Stroock, Large Deviations, Pure and Applied Mathematics, vol.137, 1989.
DOI : 10.1090/chel/342

M. D. Donsker and S. R. Varadhan, Asymptotic evaluation of certain markov process expectations for large time. IV, Communications on Pure and Applied Mathematics, vol.58, issue.2, pp.183-212, 1983.
DOI : 10.1002/cpa.3160360204

M. D. Donsker and S. R. Varadhan, Large deviations for stationary Gaussian processes, Communications in Mathematical Physics, vol.36, issue.1-2, pp.187-210, 1985.
DOI : 10.1007/BF01206186

O. Faugeras and J. Maclaurin, Asymptotic Description of Neural Networks with Correlated Synaptic Weights, Entropy, vol.17, issue.7, 2014.
DOI : 10.3390/e17074701
URL : https://hal.archives-ouvertes.fr/hal-00955770

O. Faugeras and J. Maclaurin, Asymptotic description of stochastic neural networks. I. Existence of a large deviation principle, Comptes Rendus Mathematique, vol.352, issue.10
DOI : 10.1016/j.crma.2014.08.018
URL : https://hal.archives-ouvertes.fr/hal-01074827

O. Faugeras and J. Maclaurin, Large deviations of a spatially-ergodic neural network with learning. Arxiv depot, 2014.

O. Faugeras, J. Touboul, and B. Cessac, A constructive mean-field analysis of multi population neural networks with random synaptic weights and stochastic inputs, Frontiers in Computational Neuroscience, vol.3, issue.1, 2009.
DOI : 10.3389/neuro.10.001.2009
URL : https://hal.archives-ouvertes.fr/inria-00258345

E. T. Rolls and G. Deco, The noisy brain: stochastic dynamics as a principle of brain function, 2010.

Y. Steinberg and O. Zeitouni, On tests for normality, IEEE Transactions on Information Theory, vol.38, issue.6, 1992.
DOI : 10.1109/18.165450