]. R. Bowen, Equilibrium states and the ergodic theory of Anosov diffeomorphisms. Second revised version, Lect. Notes.in Math, vol.470, issue.1, 2008.

P. Brémaud, Markov chains. Gibbs fields, Monte Carlo simulation, and queues, 1999.

J. R. Chazottes and E. Ugalde, On the preservation of Gibbsianness under symbol amalgamation, Entropy of Hidden Markov Processes and Connections to Dynamical Systems, p.81, 2011.
DOI : 10.1017/CBO9780511819407.003

P. Dayan and L. F. Abbott, Theoretical Neuroscience : Computational and Mathematical Modeling of Neural Systems, 2001.

M. Delescluse and C. Pouzat, Efficient spike-sorting of multi-state neurons using inter-spike intervals information, Journal of Neuroscience Methods, vol.150, issue.1, pp.16-29, 2006.
DOI : 10.1016/j.jneumeth.2005.05.023
URL : https://hal.archives-ouvertes.fr/hal-00005002

R. L. Dobrushin, The Description of a Random Field by Means of Conditional Probabilities and Conditions of Its Regularity, Theory of Probability & Its Applications, vol.13, issue.2, pp.201-229, 1968.
DOI : 10.1137/1113026

G. T. Einevoll, F. Franke, E. Hagen, C. Pouzat, and K. D. Harris, Towards reliable spike-train recordings from thousands of neurons with multielectrodes, Current Opinion in Neurobiology, vol.22, issue.1, pp.11-17, 2012.
DOI : 10.1016/j.conb.2011.10.001
URL : https://hal.archives-ouvertes.fr/hal-00725382

G. B. Ermentrout and D. H. Terman, Mathematical Foundations of Neuroscience, 2010.
DOI : 10.1007/978-0-387-87708-2

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

R. Fernandez and C. Pfister, Global specifications and non-quasilocality of projections of gibbs measures, Ann. Proba, vol.25, issue.3, pp.1284-315, 1997.

R. Fernandez, S. Gallo, and G. Maillard, Regular $g$-measures are not always Gibbsian, Electronic Communications in Probability, vol.16, issue.0, pp.732-740, 2011.
DOI : 10.1214/ECP.v16-1681
URL : https://hal.archives-ouvertes.fr/hal-01296794

E. Ferrea, A. Maccione, L. Medrihan, T. Nieus, D. 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

H. Föllmer, On the global Markov property Quantum fields -algebras, processes, Proc. Symp, pp.293-302, 1978.

A. Galves and E. Löcherbach, Stochastic chains with memory of variable length, TICSP Series, vol.38, pp.117-133, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00798528

H. O. Georgii, Gibbs Measures and Phase Transitions, De Gruyter Studies in Mathematics, vol.9, 1988.

W. Gerstner and W. Kistler, Spiking Neuron Models, 2002.

S. Goldstein, Remarks on the global Markov property, Communications in Mathematical Physics, vol.11, issue.3, pp.223-234, 1980.
DOI : 10.1007/BF01952887

P. C. Hohenberg and B. I. Halperin, Theory of dynamic critical phenomena, Reviews of Modern Physics, vol.49, issue.3, pp.435-479, 1977.
DOI : 10.1103/RevModPhys.49.435

S. Ma, Modern theory of critical phenomena, 1976.

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

I. Mastromatteo and M. Marsili, On the criticality of inferred models, Journal of Statistical Mechanics: Theory and Experiment, vol.2011, issue.10, p.10012, 2011.
DOI : 10.1088/1742-5468/2011/10/P10012

S. E. Palmer, O. Marre, M. J. Berry, I. , and W. Bialek, Predictive information in a sensory population, Proceedings of the National Academy of Sciences, vol.112, issue.22, pp.6908-6913, 2015.
DOI : 10.1073/pnas.1506855112

V. Privman and M. E. Fisher, Universal critical amplitudes in finite-size scaling, Physical Review B, vol.30, issue.1, pp.322-327, 1984.
DOI : 10.1103/PhysRevB.30.322

F. Redig and F. Wang, Transformations of one-dimensional Gibbs measures with infinite range interaction, Markov Process. Relat. Fields, pp.737-752, 2010.

J. Rissanen, A universal data compression system, IEEE Transactions on Information Theory, vol.29, issue.5, pp.656-664, 1983.
DOI : 10.1109/TIT.1983.1056741

E. Seneta, Non-negative Matrices and Markov Chains, 2006.
DOI : 10.1007/0-387-32792-4

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

A. Van-enter and A. L. Ny, Decimation of the dyson-ising model, 2016.

A. C. Van-enter, R. Fernández, and A. D. Sokal, Regularity properties and pathologies of position-space renormalization-group transformations: Scope and limitations of Gibbsian theory, Journal of Statistical Physics, vol.40, issue.97, pp.5-6879, 1993.
DOI : 10.1007/BF01048183

E. Verbitskiy, On factors of <mml:math altimg="si1.gif" display="inline" overflow="scroll" xmlns:xocs="http://www.elsevier.com/xml/xocs/dtd" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.elsevier.com/xml/ja/dtd" xmlns:ja="http://www.elsevier.com/xml/ja/dtd" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:tb="http://www.elsevier.com/xml/common/table/dtd" xmlns:sb="http://www.elsevier.com/xml/common/struct-bib/dtd" xmlns:ce="http://www.elsevier.com/xml/common/dtd" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:cals="http://www.elsevier.com/xml/common/cals/dtd"><mml:mi>g</mml:mi></mml:math>-measures, Indagationes Mathematicae, vol.22, issue.3-4, pp.315-329, 2011.
DOI : 10.1016/j.indag.2011.09.001

K. G. Wilson, The renormalization group: Critical phenomena and the Kondo problem, Reviews of Modern Physics, vol.47, issue.4, pp.773-840, 1975.
DOI : 10.1103/RevModPhys.47.773