C. Alippi, Selecting accurate, robust, and minimal feedforward neural networks, IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, vol.49, issue.12, pp.1799-1810, 2002.
DOI : 10.1109/TCSI.2002.805710

A. Avi?ienis, Framework for a taxonomy of fault-tolerance attributes in computer systems, ACM SIGARCH Computer Architecture News, vol.11, issue.3, pp.16-21, 1983.
DOI : 10.1145/1067651.801633

P. W. Protzel, D. L. Palumbo, and M. K. Arras, Performance and fault-tolerance of neural networks for optimization, IEEE Transactions on Neural Networks, vol.4, issue.4, pp.600-614, 1993.
DOI : 10.1109/72.238315

W. Maass, Noise as a Resource for Computation and Learning in Networks of Spiking Neurons, Proceedings of the IEEE, pp.860-880, 2014.
DOI : 10.1109/JPROC.2014.2310593

T. Sejnowksi and T. Delbruck, The language of the brain, Scientific American, vol.307, pp.54-59, 2012.

S. S. Venkatesh, Robustness in neural computation: random graphs and sparsity, IEEE Transactions on Information Theory, vol.38, issue.3, pp.1114-1119, 1992.
DOI : 10.1109/18.135650

Z. Wang, K. H. Lee, and N. Verma, Overcoming Computational Errors in Sensing Platforms Through Embedded Machine-Learning Kernels, IEEE Transactions on Very Large Scale Integration (VLSI) Systems, pp.1459-1470, 2015.
DOI : 10.1109/TVLSI.2014.2342153

J. Neumann, Probabilistic Logics and the Synthesis of Reliable Organisms From Unreliable Components, Automata Studies, pp.43-98, 1956.
DOI : 10.1515/9781400882618-003

A. Rahimi, L. Benini, and R. K. Gupta, Variability Mitigation in Nanometer CMOS Integrated Systems: A Survey of Techniques From Circuits to Software, Proceedings of the IEEE, vol.104, issue.7, pp.1410-1448, 2016.
DOI : 10.1109/JPROC.2016.2518864

N. C. Hammadi and H. Ito, Improving the performance of feedforward neural networks by noise injection into hidden neurons, Journal of Intelligent and Robotic Systems, vol.21, issue.2, pp.103-115, 1998.
DOI : 10.1023/A:1007965819848

X. Zeng and D. S. Yeung, Sensitivity analysis of multilayer perceptron to input and weight perturbations, IEEE Transactions on Neural Networks, vol.12, issue.6, pp.1358-1366, 2001.
DOI : 10.1109/72.963772

E. B. Tchernev, R. G. Mulvaney, and D. S. Phatak, Investigating the Fault Tolerance of Neural Networks, Neural Computation, vol.17, issue.7, pp.1646-1664, 2005.
DOI : 10.1109/12.324565

T. Kohonen, The self-organizing map, Proceedings of the IEEE, vol.78, issue.9, pp.1464-1480, 1990.
DOI : 10.1109/5.58325

M. Yasunaga, I. Hachiya, K. Moki, and J. H. Kim, Fault-tolerant self-organizing map implemented by wafer-scale integration, IEEE Transactions on Very Large Scale Integration (VLSI) Systems, pp.257-265, 1998.
DOI : 10.1109/92.678883

R. Talumassawatdi and C. Lursinsap, FAULT IMMUNIZATION CONCEPT FOR SELF-ORGANIZING MAPPING NEURAL NETWORKS, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol.40, issue.06, pp.781-790, 2001.
DOI : 10.1109/72.105414

N. Rougier and Y. Boniface, Dynamic self-organising map, Neurocomputing, vol.74, issue.11, pp.1840-1847, 2011.
DOI : 10.1016/j.neucom.2010.06.034

URL : https://hal.archives-ouvertes.fr/inria-00495827

A. Ultsch, Data mining and knowledge discovery with emergent selforganizing feature maps for multivariate time series, pp.33-46, 1999.
DOI : 10.1016/b978-044450270-4/50003-6

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

M. Cottrell, M. Olteanu, F. Rossi, and N. , Villa-Vialaneix, Theoretical and Applied Aspects of the Self-Organizing Maps, pp.3-26, 2016.

T. Kohonen, The self-organizing map, Neurocomputing, vol.21, issue.1-3, pp.1-6, 1998.
DOI : 10.1016/S0925-2312(98)00030-7

V. Piuri, Analysis of Fault Tolerance in Artificial Neural Networks, Journal of Parallel and Distributed Computing, vol.61, issue.1, pp.18-48, 2001.
DOI : 10.1006/jpdc.2000.1663

J. A. Abraham and W. K. Fuchs, Fault and error models for VLSI, Proceedings of the IEEE, pp.639-654, 1986.
DOI : 10.1109/PROC.1986.13528

M. Hsueh, T. K. Tsai, and R. K. Iyer, Fault injection techniques and tools, Computer, vol.30, issue.4, pp.75-82, 1997.
DOI : 10.1109/2.585157

E. De-bodt, M. Cottrell, and M. Verleysen, Statistical tools to assess the reliability of self-organizing maps, Neural Networks, vol.15, issue.8-9, pp.967-978, 2002.
DOI : 10.1016/S0893-6080(02)00071-0

URL : https://hal.archives-ouvertes.fr/hal-00122768

G. Pölzlbauer, Survey and comparison of quality measures for selforganizing maps, Proceedings of the Fifth Workshop on Data Analysis (WDA'04), pp.67-82, 2004.

D. Beaton, I. Valova, and D. Maclean, CQoCO: A measure for comparative quality of coverage and organization for self-organizing maps, Neurocomputing, vol.73, issue.10-12, pp.2147-2159, 2010.
DOI : 10.1016/j.neucom.2010.02.004

J. Rynkiewicz, Self-organizing map algorithm and distortion measure, Neural Networks, vol.19, issue.6-7, pp.830-837, 2006.
DOI : 10.1016/j.neunet.2006.05.016

URL : https://hal.archives-ouvertes.fr/hal-00257839

Y. Wu and M. Takatsuka, Spherical self-organizing map using efficient indexed geodesic data structure, Self Organising Maps -WSOM'05, pp.900-910, 2006.
DOI : 10.1016/j.neunet.2006.05.021

C. Chin, K. Mehrotra, C. K. Mohan, and S. Rankat, Training techniques to obtain fault-tolerant neural networks, " in Fault-Tolerant Computing, 1994. FTCS-24, Digest of Papers., Twenty-Fourth International Symposium on, pp.360-369, 1994.

S. Cavalieri and O. Mirabella, A novel learning algorithm which improves the partial fault tolerance of multilayer neural networks, Neural Networks, vol.12, issue.1, pp.91-106, 1999.
DOI : 10.1016/S0893-6080(98)00094-X

C. H. Sequin and R. D. Clay, Fault tolerance in artificial neural networks, 1990 IJCNN International Joint Conference on Neural Networks, pp.703-708, 1990.
DOI : 10.1109/IJCNN.1990.137651

H. Allende, S. Moreno, C. Rogel, and R. Salas, Robust Self-organizing Maps, pp.179-186, 2004.
DOI : 10.1007/978-3-540-30463-0_22