R. Alur and T. Henzinger, Reactive modules. Formal Methods in System Design, pp.7-48, 1999.
DOI : 10.1109/lics.1996.561320

E. M. Clarke, E. A. Emerson, and A. P. Sistla, Automatic verification of finite-state concurrent systems using temporal logic specifications, ACM Transactions on Programming Languages and Systems, vol.8, issue.2, pp.244-263, 1986.
DOI : 10.1145/5397.5399

E. M. Clarke, O. Grumberg, and D. Peled, Model checking, 1999.

G. Cybenko, Approximation by superpositions of a sigmoidal function, Mathematics of Control, Signals, and Systems, vol.27, issue.4, pp.303-314, 1989.
DOI : 10.1090/pspum/028.2/0507425

D. Maio, V. Lansky, P. Rodriguez, and R. , Different types of noise in leaky integrate-andfire model of neuronal dynamics with discrete periodical input, General physiology and biophysics, vol.23, pp.21-38, 2004.

N. Fourcaud and N. Brunel, Dynamics of the Firing Probability of Noisy Integrate-and-Fire Neurons, Neural Computation, vol.19, issue.9, pp.2057-2110, 2002.
DOI : 10.1111/j.1469-7793.1998.715bv.x

S. Gay, S. Soliman, and F. Fages, A graphical method for reducing and relating models in systems biology, Bioinformatics, vol.26, issue.18, pp.575-581, 2010.
DOI : 10.1093/bioinformatics/btq388

URL : https://hal.archives-ouvertes.fr/hal-01431335

C. Girard-riboulleau, Modèles probabilistes et vérification de réseaux de neurones, 2017.

H. Hansson and B. Jonsson, A logic for reasoning about time and reliability. Formal aspects of computing, pp.512-535, 1994.
DOI : 10.1007/bf01211866

URL : ftp://ftp.sics.se/pub/SICS-reports/Reports/SICS-R--90-13--SE.ps.Z

A. L. Hodgkin and A. F. Huxley, A quantitative description of membrane current and its application to conduction and excitation in nerve, The Journal of Physiology, vol.117, issue.4, pp.500-544, 1952.
DOI : 10.1113/jphysiol.1952.sp004764

E. M. Izhikevich, Which Model to Use for Cortical Spiking Neurons?, IEEE Transactions on Neural Networks, vol.15, issue.5, pp.1063-1070, 2004.
DOI : 10.1109/TNN.2004.832719

M. Kwiatkowska, G. Norman, P. , and D. , Stochastic Model Checking, International School on Formal Methods for the Design of Computer, Communication and Software Systems, pp.220-270, 2007.
DOI : 10.1007/978-3-540-72522-0_6

M. Kwiatkowska, G. Norman, P. , and D. , PRISM 4.0: Verification of Probabilistic Real-Time Systems, Proc. 23rd International Conference on Computer Aided Verification (CAV'11), pp.585-591, 2011.
DOI : 10.1007/3-540-45657-0_17

URL : https://hal.archives-ouvertes.fr/hal-00648035

L. Lapicque, Recherches quantitatives sur l'excitation electrique des nerfs traitee comme une polarization, J Physiol Pathol Gen, vol.9, pp.620-635, 1907.

W. Maass, Networks of spiking neurons: The third generation of neural network models, Neural Networks, vol.10, issue.9, pp.1659-1671, 1997.
DOI : 10.1016/S0893-6080(97)00011-7

W. S. Mcculloch and W. Pitts, A logical calculus of the ideas immanent in nervous activity . The bulletin of mathematical biophysics, pp.115-133, 1943.

J. E. Menke and T. R. Martinez, Artificial neural network reduction through oracle learning . Intelligent Data Analysis, pp.135-149, 2009.

A. Naldi, E. Remy, D. Thieffry, C. , and C. , Dynamically consistent reduction of logical regulatory graphs, Theoretical Computer Science, vol.412, issue.21, pp.2207-2218, 2011.
DOI : 10.1016/j.tcs.2010.10.021

URL : https://hal.archives-ouvertes.fr/hal-01284743

H. Paugam-moisy and S. M. Bohte, Computing with Spiking Neuron Networks, Handbook of Natural Computing, pp.335-376, 2012.
DOI : 10.1007/978-3-540-92910-9_10

URL : https://hal.archives-ouvertes.fr/hal-01587781

L. Paulevé, Goal-Oriented Reduction of Automata Networks, Computational Methods in Systems Biology -14th International Conference Proceedings, pp.252-272, 2016.
DOI : 10.3389/fgene.2013.00112

R. Reed, Pruning algorithms-a survey, IEEE Transactions on Neural Networks, vol.4, issue.5, pp.740-747, 1993.
DOI : 10.1109/72.248452