S. Amari, Characteristics of random nets of analog-like elements, IEEE Trans. Syst. Man and Cybernetics, issue.25, pp.643-657, 1972.

H. Araki, Mathematical Theory of Quantum Fields, 1999.

F. M. Atay and L. Roncoroni, Exact lumpability of linear evolution equations in Banach spaces " . MPI-MIS Preprint Series, pp.2013-109, 2013.

R. Axelrod, The Dissemination of Culture, Journal of Conflict Resolution, vol.68, issue.2, pp.203-226, 1997.
DOI : 10.1177/0022002797041002001

S. Banisch, The Probabilistic Structure of Discrete Agent-Based Models, The interdisciplinary journal of Discontinuity, Nonlinearity, and Complexity, vol.3, issue.3, pp.281-292, 2014.
DOI : 10.5890/DNC.2014.09.005

S. Banisch, Markov Chain Aggregation for Agent-Based Models. Understanding Complex Systems, p.2015

DOI : 10.1142/S0219525915500113

S. Banisch, R. Lima, and T. Araújo, Agent based models and opinion dynamics as Markov chains, Social Networks, vol.34, issue.4, pp.549-561, 2012.
DOI : 10.1016/j.socnet.2012.06.001

URL : http://arxiv.org/abs/1108.1716

M. Barber, P. Blanchard, E. Buchinger, B. Cessac, and L. Streit, A luhmann-based model of communication, learning and innovation, Journal of Artificial Societies and Social Simulation, vol.9, issue.4, 2006.

G. Ben-arous and A. Guionnet, Large deviations for langevin spin glass dynamics " . Probability Theory and Related Fields, pp.455-509, 1995.

P. Blanchard and M. Hellmich, Decoherence in infinite quantum systems, Theoretical and Experimental Foundations of Recent Quantum Technology, AIP Conf. Proc. 1469, pp.2-15, 2010.
DOI : 10.1063/1.4746056

P. Blanchard and R. Olkiewicz, DECOHERENCE INDUCED TRANSITION FROM QUANTUM TO CLASSICAL DYNAMICS, Reviews in Mathematical Physics, vol.15, issue.03, pp.217-243, 2003.
DOI : 10.1142/S0129055X03001631

J. P. Bouchaud, L. F. Cugliandolo, J. Kurchan, and M. Mézard, Mode-coupling approximations, glass theory and disordered systems, Physica A: Statistical Mechanics and its Applications, vol.226, issue.3-4, pp.243-273, 1996.
DOI : 10.1016/0378-4371(95)00423-8

URL : http://arxiv.org/abs/cond-mat/9511042

B. Cessac, Occurrence of Chaos and AT Line in Random Neural Networks, Europhysics Letters (EPL), vol.26, issue.8, pp.577-582, 1994.
DOI : 10.1209/0295-5075/26/8/004

B. Cessac, Increase in Complexity in Random Neural Networks, Journal de Physique I, vol.5, issue.3, pp.409-432, 1995.
DOI : 10.1051/jp1:1995135

URL : https://hal.archives-ouvertes.fr/jpa-00247065

B. Cessac, B. Doyon, M. Quoy, and M. Samuelides, Mean-field equations, bifurcation map and route to chaos in discrete time neural networks, Physica D: Nonlinear Phenomena, vol.74, issue.1-2, pp.24-44, 1994.
DOI : 10.1016/0167-2789(94)90024-8

T. Cover and J. Thomas, Elements of Information Theory, 1991.

E. Daucé, M. Quoy, B. Cessac, B. Doyon, and M. Samuelides, Self-organization and dynamics reduction in recurrent networks: stimulus presentation and learning, Neural Networks, vol.11, issue.3, pp.521-554, 1998.
DOI : 10.1016/S0893-6080(97)00131-7

O. Faugeras and J. M. Laurin, Asymptotic Description of Neural Networks with Correlated Synaptic Weights, Entropy, vol.17, issue.7, p.4701, 2015.
DOI : 10.3390/e17074701

URL : https://hal.archives-ouvertes.fr/hal-00955770

O. Faugeras, J. Touboul, and B. Cessac, A constructive mean field analysis of multi population neural networks with random synapticweights and stochastic inputs, Frontiers in Computational Neuroscience, vol.3, issue.1, 2009.

W. Freeman, Mass Action in the Nervous System, 1975.

D. Geiger, T. Verma, and J. Pearl, Identifying independence in bayesian networks, Networks, vol.9, issue.5, pp.507-534, 1990.
DOI : 10.1002/net.3230200504

S. Geman, Almost Sure Stable Oscillations in a Large System of Randomly Coupled Equations, SIAM Journal on Applied Mathematics, vol.42, issue.4, pp.695-703, 1982.
DOI : 10.1137/0142048

N. I. Gillespie and C. E. Praeger, Neighbour transitivity on codes in hamming graphs " . Designs, codes and cryptography, pp.385-393, 2013.

L. Horstmeyer and F. M. Atay, Characterization of exact lumpability of smooth dynamics on manifolds " . MPI-MIS Preprint Series, pp.2015-70, 2015.

B. H. Jansen and V. G. Rit, Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns, Biological Cybernetics, vol.580, issue.4, pp.357-366, 1995.
DOI : 10.1007/BF00199471

J. G. Kemeny and J. L. Snell, Finite Markov Chains, 1976.

L. Molgedey, J. Schuchardt, and H. Schuster, Suppressing chaos in neural networks by noise, Physical Review Letters, vol.69, issue.26, pp.3717-3719, 1992.
DOI : 10.1103/PhysRevLett.69.3717

O. Moynot and M. Samuelides, Large deviations and mean-field theory for asymmetric random recurrent neural networks " . Probability Theory and Related Fields, pp.41-75, 2002.

J. Naudé, B. Cessac, H. Berry, and B. Delord, Effects of Cellular Homeostatic Intrinsic Plasticity on Dynamical and Computational Properties of Biological Recurrent Neural Networks, Journal of Neuroscience, vol.33, issue.38, pp.15032-15043, 2013.
DOI : 10.1523/JNEUROSCI.0870-13.2013

O. Pfante, E. Olbrich, N. Bertschinger, N. Ay, and J. Jost, Closure measures for coarse-graining of the tent map, Chaos: An Interdisciplinary Journal of Nonlinear Science, vol.24, issue.1, p.13136, 2014.
DOI : 10.1063/1.4869075

O. Pfante, E. Olbrich, N. Bertschinger, N. Ay, and J. Jost, COMPARISON BETWEEN DIFFERENT METHODS OF LEVEL IDENTIFICATION, Advances in Complex Systems, p.1450007, 2014.
DOI : 10.1142/S0219525914500076

T. Schelling, Dynamic models of segregation???, The Journal of Mathematical Sociology, vol.1, issue.2, pp.143-186, 1971.
DOI : 10.2307/213133

H. Sompolinsky, A. Crisanti, and H. Sommers, Chaos in Random Neural Networks, Physical Review Letters, vol.61, issue.3, pp.259-262, 1988.
DOI : 10.1103/PhysRevLett.61.259

H. Sompolinsky and A. Zippelius, Relaxational dynamics of the Edwards-Anderson model and the mean-field theory of spin-glasses, Physical Review B, vol.25, issue.11, pp.6860-6875, 1982.
DOI : 10.1103/PhysRevB.25.6860

H. Wilson and J. Cowan, Excitatory and Inhibitory Interactions in Localized Populations of Model Neurons, Biophysical Journal, vol.12, issue.1, pp.1-24, 1972.
DOI : 10.1016/S0006-3495(72)86068-5

M. Schlosshauer, Decoherence, the measurement problem, and interpretations of quantum mechanics Available at arXiv:quant- ph, Rev. Mod. Phys, vol.76, 1267.