L. Chua, V. Sbitnev, and H. Kim, HODGKIN???HUXLEY AXON IS MADE OF MEMRISTORS, International Journal of Bifurcation and Chaos, vol.11, issue.03, p.1230011, 2012.
DOI : 10.1101/SQB.1936.004.01.001

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

P. O. Vontobel, W. Robinett, P. J. Kuekes, D. R. Stewart, J. Straznicky et al., Writing to and reading from a nano-scale crossbar memory based on memristors, Nanotechnology, vol.20, issue.42, 2009.
DOI : 10.1088/0957-4484/20/42/425204

D. B. Strukov and K. K. Likharev, CMOL FPGA: a reconfigurable architecture for hybrid digital circuits with two-terminal nanodevices, Nanotechnology, vol.16, issue.6, p.888, 2005.
DOI : 10.1088/0957-4484/16/6/045

A. Bandyopadhyay and A. J. , Large conductance switching and memory effects in organic molecules for data-storage applications, Applied Physics Letters, vol.82, issue.8, pp.1215-1217, 2003.
DOI : 10.1063/1.126902

V. Erokhin, T. Berzina, A. Smerieri, P. Camorani, S. Erokhina et al., Bio-inspired adaptive networks based on organic memristors, Nano Communication Networks, vol.1, issue.2, 2010.
DOI : 10.1016/j.nancom.2010.05.002

S. Song, B. Cho, T. Kim, Y. Ji, M. Jo et al., Three-Dimensional Integration of Organic Resistive Memory Devices, Advanced Materials, vol.22, issue.44, pp.5048-5052, 2010.
DOI : 10.1002/adma.200900759

N. Kooy, K. Mohamed, L. T. Pin, and O. S. Guan, A review of roll-toroll nanoimprint lithography, Nanoscale research letters, vol.9, issue.1, pp.1-13, 2014.

D. Querlioz, O. Bichler, P. Dollfus, and C. Gamrat, Immunity to Device Variations in a Spiking Neural Network With Memristive Nanodevices, IEEE Transactions on Nanotechnology, vol.12, issue.3, pp.288-295, 2013.
DOI : 10.1109/TNANO.2013.2250995

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

D. Soudry, D. D. Castro, A. Gal, A. Kolodny, and S. Kvatinsky, Memristor-Based Multilayer Neural Networks With Online Gradient Descent Training, IEEE Transactions on Neural Networks and Learning Systems, vol.26, issue.10, 2015.
DOI : 10.1109/TNNLS.2014.2383395

M. L. Minsky and S. A. Papert, Perceptrons -Expanded Edition, 1987.

D. Tank and J. J. Hopfield, Simple 'neural' optimization networks: An A/D converter, signal decision circuit, and a linear programming circuit, IEEE Transactions on Circuits and Systems, vol.33, issue.5, pp.533-541, 1986.
DOI : 10.1109/TCS.1986.1085953

D. Chabi, D. Querlioz, W. Zhao, and J. Klein, Robust learning approach for neuro-inspired nanoscale crossbar architecture, ACM Journal on Emerging Technologies in Computing Systems, vol.10, issue.1, pp.1-520, 2014.
DOI : 10.1126/science.1090899

Z. Wang, W. Zhao, W. Kang, Y. Zhang, J. Klein et al., Compact modelling of ferroelectric tunnel memristor and its use for neuromorphic simulation, Applied Physics Letters, vol.18, issue.5, p.53505, 2014.
DOI : 10.1162/NECO_a_00377

D. Chabi, Z. Wang, W. Zhao, and J. Klein, On-chip supervised learning rule for ultra high density neural crossbar using memristor for synapse and neuron, IEEE/ACM Int. Symp. Nanoscale Architectures (NANOARCH), pp.7-12, 2014.
DOI : 10.1109/nanoarch.2014.6880483

S. Liao, Design and modeling of a neuro-inspired learning circuit using nanotube-based memory devices, IEEE Trans. Circ. Syst, vol.58, issue.9, pp.2172-2181, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00584909

V. Klein and . Derycke, Neuromorphic function learning with carbon nanotube based synapses, Nanotechnol, vol.24, p.384013, 2013.

F. Alibart, E. Zamanidoost, and D. B. Strukov, Pattern classification by memristive crossbar circuits using ex situ and in situ training, Nature Communications, vol.2007, 2013.
DOI : 10.1063/1.3236506

URL : https://hal.archives-ouvertes.fr/hal-00871928

T. Cabaret, Etude, réalisation et caractérisation de memristors organiques électro-greffés en tant que nanosynapses de circuits neuroinspirés, 2014.

T. Cabaret, L. Fillaud, B. Jousselme, J. Klein, and V. Derycke, Electrografted organic memristors: Properties and prospects for artificial neural networks based on stdp, Int. Conf. Nanotechnology (IEEE-NANO)
DOI : 10.1109/nano.2014.6968169

URL : https://hal.archives-ouvertes.fr/hal-01187807

D. Chabi, W. Zhao, D. Querlioz, and J. Klein, On-Chip Universal Supervised Learning Methods for Neuro-Inspired Block of Memristive Nanodevices, ACM Journal on Emerging Technologies in Computing Systems, vol.11, issue.4, p.34, 2015.
DOI : 10.1109/TNANO.2012.2206051