K. G. Sheela, S. N. Deepa-]-c, J. B. James, N. E. Aimone, C. M. Miner et al., Review on Methods to Fix Number of Hidden Neurons in Neural Networks DOI: 10 A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications, Biologically Inspired Cognitive Architectures, pp.49-64, 1155.

P. U. Diehl and M. Cook, Unsupervised learning of digit recognition using spike-timing-dependent plasticity, Frontiers in Computational Neuroscience, vol.54, issue.178, p.99, 2015.
DOI : 10.1109/TNNLS.2014.2362542

URL : http://journal.frontiersin.org/article/10.3389/fncom.2015.00099/pdf

D. Cai, X. He, Y. Hu, J. Han, and T. Huang, Learning a Spatially Smooth Subspace for Face Recognition, 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007.
DOI : 10.1109/CVPR.2007.383054

URL : http://people.cs.uchicago.edu/%7Exiaofei/conference-9.pdf

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE, vol.86, issue.11, pp.2278-2323, 1998.
DOI : 10.1109/5.726791

URL : http://www.cs.berkeley.edu/~daf/appsem/Handwriting/papers/00726791.pdf

Y. Liu, J. A. Starzyk, and Z. Zhu, Optimizing Number of Hidden Neurons in Neural Networks, 2007.

D. Hunter, H. Yu, M. S. Iii, J. Kolbusz, and B. M. Wilamowski, Selection of Proper Neural Network Sizes and Architectures???A Comparative Study, IEEE Transactions on Industrial Informatics, vol.8, issue.2, pp.228-240, 2012.
DOI : 10.1109/TII.2012.2187914

A. Tavanaei and A. S. Maida, A Minimal Spiking Neural Network to Rapidly Train and Classify Handwritten Digits in Binary and 10-Digit Tasks, International Journal of Advanced Research in Artificial Intelligence, vol.4, issue.7, 2015.
DOI : 10.14569/IJARAI.2015.040701

URL : http://www.thesai.org/Downloads/IJARAI/Volume4No7/Paper_1-A_Minimal_Spiking_Neural_Network_to_Rapidly_Train.pdf

E. M. Izhikevich, Simple model of spiking neurons, IEEE Neural Networks Council, pp.1569-1572, 2003.
DOI : 10.1109/TNN.2003.820440

URL : http://www.nsi.edu/users/izhikevich/publications/spikes.pdf

M. Shahsavari and P. Boulet, Parameter Exploration to Improve Performance of Memristor-Based Neuromorphic Architectures, IEEE Transactions on Multi-Scale Computing Systems, vol.PP, issue.99, pp.1-1, 2017.
DOI : 10.1109/TMSCS.2017.2761231

URL : https://hal.archives-ouvertes.fr/hal-01615032

L. Chua, Memristor-The missing circuit element Circuit Theory, IEEE Transactions on, vol.18, pp.507-519, 1971.
DOI : 10.1109/tct.1971.1083337

D. B. Strukov, G. S. Snider, D. R. Stewart, and R. S. Williams, The missing memristor found, Nature, vol.4, issue.7191, pp.80-83, 2008.
DOI : 10.1038/nature06932

S. H. Jo, T. Chang, I. Ebong, B. B. Bhadviya, P. Mazumder et al., Nanoscale Memristor Device as Synapse in Neuromorphic Systems, Nano Letters, vol.10, issue.4, pp.1297-1301, 2010.
DOI : 10.1021/nl904092h

URL : http://www.eecs.umich.edu/~wluee/LuJo_Synapse_NL2010.pdf

M. Shahsavari, P. Devienne, and P. Boulet, N2s3, a Simulator for the Architecture Exploration of Neuromorphic Accelerators, 2nd International Workshop on Neuromorphic and Brain-Based Computing Systems DATE Conference, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01240444

C. Mead, Neuromorphic electronic systems, Proceedings of the IEEE, vol.78, issue.10, pp.1629-1636, 1990.
DOI : 10.1109/5.58356

URL : http://www.ini.uzh.ch/~tobi/introneuroscience/meadNeuromorphicElectronicSystemsIEEE1990.pdf

P. A. Merolla, J. V. Arthur, R. Alvarez-icaza, A. S. Cassidy, J. Sawada et al., A million spiking-neuron integrated circuit with a scalable communication network and interface, Science, vol.9, issue.2, pp.668-673, 2014.
DOI : 10.1007/BF00166411

S. B. Furber, F. Galluppi, S. Temple, and L. A. Plana, The SpiNNaker Project, Proceedings of the IEEE, vol.102, issue.5, pp.652-665, 2014.
DOI : 10.1109/JPROC.2014.2304638

URL : http://doi.org/10.1109/jproc.2014.2304638

S. H. Jo, T. Chang, I. Ebong, B. B. Bhadviya, P. Mazumder et al., Nanoscale Memristor Device as Synapse in Neuromorphic Systems, Nano Letters, vol.10, issue.4, pp.1297-1301, 2010.
DOI : 10.1021/nl904092h

URL : http://www.eecs.umich.edu/~wluee/LuJo_Synapse_NL2010.pdf

B. Rajendran and F. Alibart, Neuromorphic Computing Based on Emerging Memory Technologies, IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol.6, issue.2, pp.198-211, 2016.
DOI : 10.1109/JETCAS.2016.2533298

L. Shi, J. Pei, N. Deng, D. Wang, L. Deng et al., Development of a neuromorphic computing system, 2015 IEEE International Electron Devices Meeting (IEDM), pp.4-7, 2015.
DOI : 10.1109/IEDM.2015.7409624

P. O. Connor, D. Neil, S. Liu, T. Delbruck, and M. Pfeiffer, Realtime classification and sensor fusion with a spiking deep belief network, Neuromorphic Engineering, vol.7, p.178, 2013.

B. V. Benjamin, P. Gao, E. Mcquinn, S. Choudhary, A. R. Chandrasekaran et al., Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations, Proceedings of the IEEE, pp.699-716, 2014.
DOI : 10.1109/JPROC.2014.2313565

J. Yang, M. Pickett, X. Li, D. Ohlberg, D. Stewart et al., Memristive switching mechanism for metal/oxide/metal nanodevices, Nature Nanotechnology, vol.49, issue.7, pp.429-433, 2008.
DOI : 10.1038/nnano.2008.160

C. Xu, X. Dong, N. P. Jouppi, and Y. Xie, Design implications of memristor-based rram cross-point structures, DATE, pp.734-739, 2011.

M. Prezioso, F. Merrikh-bayat, B. Hoskins, G. Adam, K. K. Likharev et al., Training and operation of an integrated neuromorphic network based on metal-oxide memristors, Nature, vol.12, issue.2, pp.61-64, 2015.
DOI : 10.1007/978-3-642-35289-8_3

S. B. Eryilmaz, D. Kuzum, R. Jeyasingh, S. Kim, M. Brightsky et al., Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array, Frontiers in Neuroscience, vol.58, issue.186, p.205, 2014.
DOI : 10.1109/TED.2011.2147791

URL : http://journal.frontiersin.org/article/10.3389/fnins.2014.00205/pdf

S. Ambrogio, N. Ciocchini, M. Laudato, V. Milo, A. Pirovano et al., Unsupervised Learning by Spike Timing Dependent Plasticity in Phase Change Memory (PCM) Synapses, Frontiers in Neuroscience, vol.60, issue.438, p.56, 2016.
DOI : 10.1109/TED.2013.2285403

URL : http://journal.frontiersin.org/article/10.3389/fnins.2016.00056/pdf

Q. Liu, S. Long, H. Lv, W. Wang, J. Niu et al., Controllable Growth of Nanoscale Conductive Filaments in Solid-Electrolyte-Based ReRAM by Using a Metal Nanocrystal Covered Bottom Electrode, ACS Nano, vol.4, issue.10, pp.6162-6168, 2010.
DOI : 10.1021/nn1017582

O. Bichler, D. Roclin, C. Gamrat, and D. Querlioz, Design exploration methodology for memristor-based spiking neuromorphic architectures with the Xnet event-driven simulator, 2013 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH), pp.7-12, 2013.
DOI : 10.1109/NanoArch.2013.6623029

M. Suri, D. Querlioz, O. Bichler, G. Palma, E. Vianello et al., Bio-Inspired Stochastic Computing Using Binary CBRAM Synapses, Bio-Inspired Stochastic Computing Using Binary CBRAM Synapses, pp.2402-2409, 2013.
DOI : 10.1109/TED.2013.2263000

URL : https://hal.archives-ouvertes.fr/hal-00871918

A. Chanthbouala, V. Garcia, R. O. Cherifi, K. Bouzehouane, S. Fusil et al., A ferroelectric memristor, Nature Materials, vol.83, issue.10, 2012.
DOI : 10.1063/1.1621731

Y. Nishitani, Y. Kaneko, and M. Ueda, Artificial synapses using ferroelectric memristors embedded with CMOS Circuit for image recognition, 72nd Device Research Conference, pp.297-298, 2014.
DOI : 10.1109/DRC.2014.6872414

E. Chicca, D. Badoni, V. Dante, M. D. Andreagiovanni, G. Salina et al., A vlsi recurrent network of integrate-and-fire neurons connected by plastic synapses with long-term memory, IEEE Transactions on Neural Networks, vol.14, issue.5, pp.1297-1307, 2003.
DOI : 10.1109/TNN.2003.816367

S. Liu and R. Douglas, Temporal Coding in a Silicon Network of Integrate-and-Fire Neurons, IEEE Transactions on Neural Networks, vol.15, issue.5, pp.1305-1314, 2004.
DOI : 10.1109/TNN.2004.832725

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

M. Shahsavari, P. Falez, and P. Boulet, Combining a Volatile and Nonvolatile Memristor in Artificial Synapse to Improve Learning in Spiking Neural Networks, 12th ACM/IEEE International Symposium on Nanoscale Architectures, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01368954

M. Shahsavari, M. Nadeem, S. A. Ostadzadeh, P. Devienne, and P. Boulet, Unconventional digital computing approach: memristive nanodevice platform, physica status solidi (c), vol.12, issue.1-2, pp.222-228, 2015.
DOI : 10.1002/pssc.201400069

URL : https://hal.archives-ouvertes.fr/hal-01116577

W. Gerstner, R. Ritz, and J. L. Hemmen, Why spikes? Hebbian learning and retrieval of time-resolved excitation patterns, Biological Cybernetics, vol.76, issue.5-6, pp.5-6, 1993.
DOI : 10.1007/978-3-642-69421-9_14

URL : http://infoscience.epfl.ch/record/97756/files/Gerstner_1993.pdf

H. Markram, J. Lbke, M. Frotscher, and B. Sakmann, Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs, Science, vol.275, issue.5297, pp.213-215, 1997.
DOI : 10.1126/science.275.5297.213

T. Serrano-gotarredona, T. Masquelier, T. Prodromakis, G. Indiveri, and B. Linares-barranco, STDP and STDP variations with memristors for spiking neuromorphic learning systems, Frontiers in Neuroscience, vol.7, issue.2, 2013.
DOI : 10.3389/fnins.2013.00002

URL : https://hal.archives-ouvertes.fr/hal-01578521

B. Nessler, M. Pfeiffer, and W. Maass, Stdp enables spiking neurons to detect hidden causes of their inputs, Advances in Neural Information Processing Systems 22, pp.1357-1365, 2009.

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE, pp.2278-2324, 1998.
DOI : 10.1109/5.726791

URL : http://www.cs.berkeley.edu/~daf/appsem/Handwriting/papers/00726791.pdf

P. U. Diehl and M. Cook, Unsupervised learning of digit recognition using spike-timing-dependent plasticity, Frontiers in Computational Neuroscience, vol.54, issue.178, 2015.
DOI : 10.1109/TNNLS.2014.2362542

URL : http://journal.frontiersin.org/article/10.3389/fncom.2015.00099/pdf

M. Hines, NEURON ??? A Program for Simulation of Nerve Equations, Neural Systems: Analysis and Modeling (F. Eeckman, pp.127-136, 1993.
DOI : 10.1007/978-1-4615-3560-7_11

D. F. Goodman and R. Brette, Brian: a simulator for spiking neural networks in python, Frontiers in Neuroinformatics, vol.2, issue.5, 2008.

M. Gewaltig and M. Diesmann, NEST (NEural Simulation Tool), Scholarpedia, vol.2, issue.4, p.1430, 2007.
DOI : 10.4249/scholarpedia.1430

URL : https://doi.org/10.4249/scholarpedia.1430

O. Bichler, D. Roclin, C. Gamrat, and D. Querlioz, Design exploration methodology for memristor-based spiking neuromorphic architectures with the Xnet event-driven simulator, 2013 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH), pp.7-12, 2013.
DOI : 10.1109/NanoArch.2013.6623029