S. Amari, Information geometry on hierarchy of probability distributions, IEEE Transactions on Information Theory, vol.47, issue.5, pp.1701-1711, 2001.
DOI : 10.1109/18.930911

A. L. Berger, S. A. Pietra, and V. J. Pietra, A maximum entropy approach to natural language processing, Computational lainguistics, vol.22, pp.39-71, 1996.

J. Besag, Spatial interaction and the statistical analysis of lattice systems, Journal of the Royal Statistical Society. Series B (Methodological), vol.36, issue.2, pp.192-236, 1974.

R. Bowen, Equilibrium states and the ergodic theory of Anosov diffeomorphisms, Lect. Notes.in Math, vol.470, 1975.
DOI : 10.1007/BFb0081279

T. Broderick, M. Dudik, G. Tkacik, E. Robert, W. Schapire et al., Faster solutions of the inverse pairwise ising problem, 2007.

B. Cessac and R. Cofre, Estimating maximum entropy distributions from periodic orbits in spike trains, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00842776

J. R. Chazottes and G. Keller, Pressure and equilibrium states in ergodic theory, Israel Journal of Mathematics, vol.131, issue.1, 2008.

F. Stanley, R. Chen, and . Rosenfeld, Efficient sampling and feature selection in whole sentence maximum entropy language models, 1999.

M. Collins, R. E. Schapire, and Y. Singer, Logistic Regression , AdaBoost and Bregman Distances, Machine Learning, pp.253-285, 2002.

M. Dudík, S. Phillips, and R. Schapire, Performance Guarantees for Regularized Maximum Entropy Density Estimation, Proceedings of the 17th Annual Conference on Computational Learning Theory, 2004.
DOI : 10.1007/978-3-540-27819-1_33

R. Fernandez and G. Maillard, Chains with complete connections : General theory, uniqueness, loss of memory and mixing properties, J. Stat. Phys, vol.118, pp.3-4555, 2005.
URL : https://hal.archives-ouvertes.fr/hal-01296844

E. Ferrea, . Maccione, . Medrihan, . Nieus, . Ghezzi et al., Large-scale, high-resolution electrophysiological imaging of field potentials in brain slices with microelectronic multielectrode arrays, Frontiers in Neural Circuits, vol.6, issue.80, p.2012
DOI : 10.3389/fncir.2012.00080

E. Ganmor, R. Segev, and E. Schneidman, The Architecture of Functional Interaction Networks in the Retina, Journal of Neuroscience, vol.31, issue.8, pp.313044-3054, 2011.
DOI : 10.1523/JNEUROSCI.3682-10.2011

E. Ganmor, R. Segev, and E. Schneidman, Sparse low-order interaction network underlies a highly correlated and learnable neural population code, Proceedings of the National Academy of Sciences, vol.108, issue.23, pp.9679-9684, 2011.
DOI : 10.1073/pnas.1019641108

U. Garibaldi and M. A. Penco, Probability theory and physics between bernoulli and laplace: The contribution of j. h. lambert (1728-1777), Proc. Fifth National Congress on the History of Physics, pp.341-346, 1985.

H. Georgii, Gibbs measures and phase transitions. De Gruyter Studies in Mathematics:9. Berlin, 1988.
DOI : 10.1515/9783110850147

I. I. Gikhman and A. V. Skorokhod, The Theory of Stochastic Processes, 1979.
DOI : 10.1007/978-3-642-61921-2

N. Daniel, S. B. Hill, D. Mehta, and . Kleinfeld, Quality Metrics to Accompany Spike Sorting of Extracellular Signals, The Journal of Neuroscience, issue.24, pp.318699-8705, 2011.

T. Edwin and . Jaynes, Where do we stand on maximum entropy. The maximum entropy formalism, pp.15-118, 1978.

T. Edwin and . Jaynes, The minimum entropy production principle, Annual Review of Physical Chemistry, vol.31, issue.1, pp.579-601, 1980.

E. T. Jaynes, Information Theory and Statistical Mechanics, Physical Review, vol.106, issue.4, p.620, 1957.
DOI : 10.1103/PhysRev.106.620

E. Jaynes, Macroscopic Prediction, Complex Systems -Operational Approaches in Neurobiology, Physics, and Computers, pp.254-269, 1985.
DOI : 10.1007/978-3-642-70795-7_18

URL : http://bayes.wustl.edu/etj/articles/macroscopic.prediction.ps.gz

H. J. Kappen and F. B. Rodriguez, Boltzmann machine learning using mean field theory and linear response correction, Advances in Neural Information Processing Systems, pp.280-286, 1998.

G. Keller, Equilibrium States in Ergodic Theory, 1998.
DOI : 10.1017/CBO9781107359987

R. Koeling, Chunking with maximum entropy models, Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning -, 2000.
DOI : 10.3115/1117601.1117634

URL : http://acl.ldc.upenn.edu/W/W00/W00-0729.pdf

A. M. Litke, N. Bezayiff, E. J. Chichilnisky, W. Cunningham, W. Dabrowski et al., What does the eye tell the brain?: Development of a system for the large scale recording of retinal output activity, Nuclear Science Symposium Conference Record, pp.951-955, 2003.

O. Marre, D. Amodei, N. Deshmukh, K. Sadeghi, F. Soo et al., Mapping a Complete Neural Population in the Retina, Journal of Neuroscience, vol.32, issue.43, pp.14859-14873, 2012.
DOI : 10.1523/JNEUROSCI.0723-12.2012

O. Marre, S. Boustani, Y. Frégnac, and A. Destexhe, Prediction of spatiotemporal patterns of neural activity from pairwise correlations. Physical review letters, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00444939

V. Mayer and M. Urba´nskiurba´nski, Thermodynamical formalism and multifractal analysis for meromorphic functions of finite order. Memoirs of the, 2010.

H. Nakahara and S. Amari, Information-geometric decomposition in spike analysis, NIPS, pp.253-260, 2001.

H. Nasser, O. Marre, and B. Cessac, Spatio-temporal spike train analysis for large scale networks using the maximum entropy principle and montecarlo method, Journal of Statistical Mechanics: Theory and Experiment, issue.03, pp.2013-03006, 2013.

M. Otten and G. Stock, Maximum caliber inference of nonequilibrium processes, The Journal of Chemical Physics, vol.133, issue.3, 2010.
DOI : 10.1063/1.3455333

J. Pillow, . Shlens, . Paninski, . Sher, E. J. Litke et al., Spatio-temporal correlations and visual signaling in a complete neuronal population, Nature, issue.7206, pp.454995-999, 2008.

R. Quian-quiroga, Z. Nadasdy, and Y. Ben, Unsupervised Spike Detection and Sorting with Wavelets and Superparamagnetic Clustering, Neural Computation, vol.84, issue.8, pp.1661-1687, 2004.
DOI : 10.1016/0370-2693(89)91563-3

R. Rosenfeld, J. Carbonell, and A. Rudnicky, Adaptive statistical language modeling: A maximum entropy approach, 1994.

D. Ruelle, Statistical Mechanics: Rigorous results, 1969.
DOI : 10.1142/4090

D. Ruelle, Thermodynamic formalism, 1978.
DOI : 10.1017/CBO9780511617546

T. Michael, S. R. Schaub, and . Schultz, The ising decoder: reading out the activity of large neural ensembles, 2010.

E. Schneidman, M. J. Berry, R. Segev, and W. Bialek, Weak pairwise correlations imply strongly correlated network states in a neural population, Nature, vol.37, issue.7087, pp.4401007-1012, 2006.
DOI : 10.1038/nature04701

H. Ian, K. P. Stevenson, and . Kording, How advances in neural recording affect data analysis, Nature Neuroscience, vol.14, issue.2, pp.139-142, 2011.

A. Tang, D. Jackson, J. Hobbs, W. Chen, J. L. Smith et al., A Maximum Entropy Model Applied to Spatial and Temporal Correlations from Cortical Networks In Vitro, Journal of Neuroscience, vol.28, issue.2, pp.505-518, 2008.
DOI : 10.1523/JNEUROSCI.3359-07.2008

G. Tkacik, O. Marre, T. Mora, D. Amodei, M. J. Berry et al., The simplest maximum entropy model for collective behavior in a neural network, J Stat Mech, p.3011, 2013.

G. Tka?ik, E. Schneidman, M. J. Berry, I. , and W. Bialek, Spin glass models for a network of real neurons, 2009.

J. C. Vasquez, O. Marre, G. Adrian, . Palacios, J. Michael et al., Gibbs distribution analysis of temporal correlation structure on multicell spike trains from retina ganglion cells, J. Physiol. Paris, vol.106, pp.3-4120, 2012.

Y. Zhou and L. Wu, A fast algorithm for feature selection in conditional maximum entropy modeling, Proceedings of the 2003 conference on Empirical methods in natural language processing -, pp.153-159, 2003.
DOI : 10.3115/1119355.1119375