K. R. Apt, ?-models in analytical hierarchy, pp.901-904, 1972.

J. L. Balcázar, R. Gavaldà, and H. T. Siegelmann, Computational power of neural networks: a characterization in terms of Kolmogorov complexity, IEEE Transactions on Information Theory, vol.43, issue.4, pp.1175-1183, 1997.
DOI : 10.1109/18.605580

J. Cabessa and J. Duparc, Expressive power of nondeterministic recurrent neural networks in terms of their attractor dynamics, IJUC, vol.12, issue.1, pp.25-50, 2016.

J. Cabessa and H. T. Siegelmann, Evolving recurrent neural networks are super-Turing, The 2011 International Joint Conference on Neural Networks, pp.3200-3206, 2011.
DOI : 10.1109/IJCNN.2011.6033645

J. Cabessa and H. Siegelmann, The Computational Power of Interactive Recurrent Neural Networks, Neural Computation, vol.2, issue.42, pp.996-1019, 2012.
DOI : 10.1016/j.neunet.2010.08.006

J. Cabessa and H. T. Siegelmann, THE SUPER-TURING COMPUTATIONAL POWER OF PLASTIC RECURRENT NEURAL NETWORKS, International Journal of Neural Systems, vol.2, issue.08, 2014.
DOI : 10.1111/mice.12000

J. Cabessa and A. E. Villa, The expressive power of analog recurrent neural networks on infinite input streams, Theoretical Computer Science, vol.436, pp.23-34, 2012.
DOI : 10.1016/j.tcs.2012.01.042

URL : https://hal.archives-ouvertes.fr/inserm-00851237

J. Cabessa and A. E. Villa, An Attractor-Based Complexity Measurement for Boolean Recurrent Neural Networks, PLoS ONE, vol.317, issue.4, p.94204, 2014.
DOI : 10.1371/journal.pone.0094204.s006

J. Cabessa and A. E. Villa, Expressive power of first-order recurrent neural networks determined by their attractor dynamics, Journal of Computer and System Sciences, vol.82, issue.8, pp.1232-1250, 2016.
DOI : 10.1016/j.jcss.2016.04.006

J. Cabessa and A. E. Villa, Artificial Neural Networks: Methods and Applications in Bio-/Neuroinformatics, chap. Recurrent neural networks and super-Turing interactive computation, pp.1-29, 2015.

O. Finkel, Ambiguity of omega-languages of Turing machines, Logical Methods in Computer Science, vol.10, issue.3, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00735050

A. S. Kechris, Classical descriptive set theory, Graduate Texts in Mathematics, vol.156, 1995.
DOI : 10.1007/978-1-4612-4190-4

J. Kilian and H. T. Siegelmann, The Dynamic Universality of Sigmoidal Neural Networks, Information and Computation, vol.128, issue.1, pp.48-56, 1996.
DOI : 10.1006/inco.1996.0062

S. C. Kleene, Representation of Events in Nerve Nets and Finite Automata, Automata Studies, pp.3-41, 1956.
DOI : 10.1515/9781400882618-002

W. S. Mcculloch and W. Pitts, A logical calculus of the ideas immanent in nervous activity, The Bulletin of Mathematical Biophysics, vol.5, issue.4, pp.115-133, 1943.
DOI : 10.1007/BF02478259

M. L. Minsky, Computation: finite and infinite machines, N. J, 1967.

Y. N. Moschovakis, Descriptive Set Theory. Mathematical surveys and monographs, 2009.

H. Siegelmann, RECURRENT NEURAL NETWORKS AND FINITE AUTOMATA, Recurrent neural networks and finite automata, pp.567-574, 1996.
DOI : 10.1006/jcss.1995.1013

H. T. Siegelmann and E. D. Sontag, Analog computation via neural networks, Theoretical Computer Science, vol.131, issue.2, pp.331-360, 1994.
DOI : 10.1016/0304-3975(94)90178-3

H. T. Siegelmann and E. D. Sontag, On the Computational Power of Neural Nets, Journal of Computer and System Sciences, vol.50, issue.1, pp.132-150, 1995.
DOI : 10.1006/jcss.1995.1013

J. Síma and P. Orponen, General-Purpose Computation with Neural Networks: A Survey of Complexity Theoretic Results, Neural Computation, vol.4, issue.1, pp.2727-2778, 2003.
DOI : 10.1016/0020-0190(93)90202-K

L. Staiger, ??-Languages, pp.339-387, 1997.
DOI : 10.1007/978-3-642-59126-6_6