]. D. Strukov, G. S. Snider, D. R. Stewart, R. S. Williams-]-d, K. K. Strukov et al., The missing memristor found CMOL FPGA: a reconfigurable architecture for hybrid digital circuits with two-terminal nanodevices A defect-tolerant computer architecture: Opportunities for nanotechnology Array-based architecture for FET-based, nanoscale electronics Four-dimensional address topology for circuits with stacked multilayer crossbar arrays, Hybrid CMOS/nanoelectronic digital circuits: devices, architectures, and design automation Proceedings of the, pp.80-83, 1998.

J. Borghetti, Z. Li, J. Straznicky, X. Li, D. A. Ohlberg et al., A hybrid nanomemristor/transistor logic circuit capable of self-programming Complementary resistive switches for passive nanocrossbar memories, Nat. Mater, vol.106, issue.9, pp.1699-1703, 2009.
DOI : 10.1073/pnas.0806642106

URL : http://www.pnas.org/content/106/6/1699.full.pdf

R. S. Medeiros-ribeiro, ]. J. Williams10, G. S. Borghetti, P. J. Snider, J. J. Kuekes et al., Memristor?CMOS Hybrid Integrated Circuits for Reconfigurable LogicMemristive' switches enablèstateful' logic operations via material implication, 11] S.H. Jo, T. Chang, I. Ebong, B.B. Bhadviya, P. Mazumder, and W, pp.3640-3645, 2009.

H. Lu, H. Choi, J. Jung, J. Lee, J. Yoon et al., An electrically modifiable synapse array of resistive switching memory Investigating the switching dynamics and multilevel capability of bipolar metal oxide resistive switching memory Nanotube devices based crossbar architecture: toward neuromorphic computing, Nanoscale Memristor Device as Synapse in Neuromorphic Systems, pp.1297-1301, 2009.

T. Fregonese, D. Zimmer, A. Chabi, V. Filoramo, and . Derycke,

. Klein, Design and Modeling of a Neuro-Inspired Learning Circuit Using Nanotube-Based Memory Devices, press. [16] F. Alibart, S. Pleutin
URL : https://hal.archives-ouvertes.fr/hal-00584909

C. Lmimouni, D. Gamrat, . Vuillaume17-]-v, T. Erokhin, M. P. Berzina et al., Ionic/Electronic Hybrid Materials Integrated in a Synaptic Transistor with Signal Processing and Learning Functions The brain of a new machine Defect-tolerant nanoelectronic pattern classifiers Memristive model of amoeba learning Spike-timing-dependent learning in memristive nanodevices On neuromorphic spiking architectures for asynchronous STDP memristive systems A memristor-based spiking neural network immune to device variations Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity STDP enables spiking neurons to detect hidden causes of their inputs Memristor-The missing circuit element Self-organized computation with unreliable, memristive nanodevices Switching dynamics in titanium dioxide memristive devices Exponential ionic drift: fast switching and low volatility of thin-film memristors Electrical transport and thermometry of electroformed titanium dioxide memristive switches, Organic Nanoparticle Transistor Behaving as a Biological Spiking Synapse Hight fault tolerance in neural crossbar, " Int. Conf. on Design and Technology of Integrated Systems in Nanoscale Era (DTIS) Prof. of IEEE International Symposium on Nanoscale Architectures Proc. of IJCNN 2011 e31. [28] B. Nessler, M. Pfeiffer, and W. Maass Advances in Neural Information Processing Systems (NIPS'09) Programmable Resistance Switching in Nanoscale Two-Terminal Devices CMOS Compatible Nanoscale Nonvolatile Resistance Switching Memory, pp.330-337, 1971.

J. G. Simmons, Generalized Formula for the Electric Tunnel Effect between Similar Electrodes Separated by a Thin Insulating Film, Journal of Applied Physics, vol.16, issue.6, p.1793, 1963.
DOI : 10.1063/1.1753783