R. Et, Q. Ouvertes-chapitre-6, R. Conclusion, S. Pleutin, O. Bichler et al., enjeu majeur évoqué dans l'introduction. A savoir, de déterminer la faisabilité d'applications concrètes et robustes basées sur des technologies memristives A memristive nanoparticle/organic hybrid synapstor for neuroinspired computing, Advanced Functional Materials, vol.22, issue.3, pp.609-616

O. Bichler, W. Zhao, F. Alibart, S. Pleutin, D. Vuillaume et al., Functional Model of a Nanoparticle Organic Memory Transistor for Use as a Spiking Synapse, IEEE Transactions on Electron Devices, vol.57, issue.11, pp.3115-3122, 2010.
DOI : 10.1109/TED.2010.2065951

URL : https://hal.archives-ouvertes.fr/hal-00548966

O. Bichler, D. Querlioz, S. Thorpe, J. Bourgoin, and C. Gamrat, Extraction of temporally correlated features from dynamic vision sensors with spike-timing-dependent plasticity, Neural Networks, vol.32, pp.339-348
DOI : 10.1016/j.neunet.2012.02.022

URL : https://hal.archives-ouvertes.fr/hal-00706681

O. Bichler, M. Suri, D. Querlioz, D. Vuillaume, B. Desalvo et al., Visual Pattern Extraction Using Energy-Efficient “2-PCM Synapse” Neuromorphic Architecture, IEEE Transactions on Electron Devices, vol.59, issue.8, pp.2206-2214, 2012.
DOI : 10.1109/TED.2012.2197951

O. Bichler, W. Zhao, F. Alibart, S. Pleutin, D. Vuillaume et al., Pavlov's Dog Associative Learning Demonstrated on Synaptic-Like Organic Transistors, Neural Computation, vol.21, issue.2, 2012.
DOI : 10.1002/adfm.201200244

D. Querlioz, O. Bichler, P. Dollfus, and C. Gamrat, Unsupervised neuro-inspired system with memristive devices, immune to device variations, Neural Networks, 2012.

D. Querlioz, O. Bichler, W. S. Zhao, J. Klein, P. Dollfus et al., Stochastic nanodevices for a bio-inspired system capable of learning, Applied Physics Letters, 2012.

M. Suri, O. Bichler, D. Querlioz, B. Traoré, O. Cueto et al., Physical aspects of low power synapses based on phase change memory devices, Journal of Applied Physics, vol.112, issue.5
DOI : 10.1063/1.4749411

URL : https://hal.archives-ouvertes.fr/hal-00787372

M. Suri, O. Bichler, D. Querlioz, O. Cueto, L. Perniola et al., Phase change memory as synapse for ultra-dense neuromorphic systems: Application to complex visual pattern extraction, 2011 International Electron Devices Meeting, pp.4-4, 2011.
DOI : 10.1109/IEDM.2011.6131488

URL : https://hal.archives-ouvertes.fr/hal-00799997

M. Suri, O. Bichler, D. Querlioz, G. Palma, E. Vianello et al., CBRAM devices as binary synapses for low-power stochastic neuromorphic systems: Auditory (Cochlea) and visual (Retina) cognitive processing applications, 2012 International Electron Devices Meeting, 2012.
DOI : 10.1109/IEDM.2012.6479017

URL : https://hal.archives-ouvertes.fr/hal-00803088

O. Autres-actes-de-conférences, W. Bichler, C. Zhao, F. Gamrat, S. Alibart et al., Development of a functional model for the nanoparticle-organic memory transistor, Circuits and Systems (ISCAS Proceedings of 2010 IEEE International Symposium on, pp.1663-1666, 2010.

O. Bichler, D. Querlioz, S. Thorpe, J. Bourgoin, and C. Gamrat, Unsupervised features extraction from asynchronous silicon retina through Spike-Timing-Dependent Plasticity, The 2011 International Joint Conference on Neural Networks, pp.859-866, 2011.
DOI : 10.1109/IJCNN.2011.6033311

D. Querlioz, O. Bichler, and C. Gamrat, Simulation of a memristor-based spiking neural network immune to device variations, The 2011 International Joint Conference on Neural Networks, pp.1775-1781
DOI : 10.1109/IJCNN.2011.6033439

D. Querlioz, P. Dollfus, O. Bichler, and C. Gamrat, Learning with memristive devices: How should we model their behavior?, 2011 IEEE/ACM International Symposium on Nanoscale Architectures, pp.150-156, 2011.
DOI : 10.1109/NANOARCH.2011.5941497

D. Querlioz, W. Zhao, P. Dollfus, J. Klein, O. Bichler et al., Bioinspired networks with nanoscale memristive devices that combine the unsupervised and supervised learning approaches, Proceedings of the 2012 IEEE/ACM International Symposium on Nanoscale Architectures, NANOARCH '12, p.2012
DOI : 10.1145/2765491.2765528

M. Suri, O. Bichler, Q. Hubert, L. Perniola, V. Sousa et al., Interface Engineering of PCM for Improved Synaptic Performance in Neuromorphic Systems, 2012 4th IEEE International Memory Workshop, pp.1-4, 2012.
DOI : 10.1109/IMW.2012.6213674

URL : https://hal.archives-ouvertes.fr/hal-00801096

L. F. Bibliographie, J. A. Abbott, K. Varela, S. B. Sen, and . Nelson, Synaptic depression and cortical gain control, Science, vol.275, issue.5297, pp.221-224, 1997.

-. Verilog and . Manual, Agilent Technology, 2007.

G. Agnus, W. Zhao, V. Derycke, A. Filoramo, Y. Lhuillier et al., Two-Terminal Carbon Nanotube Programmable Devices for Adaptive Architectures, Advanced Materials, vol.5, issue.6, pp.702-706, 2010.
DOI : 10.1002/adma.200902170

URL : https://hal.archives-ouvertes.fr/hal-00548986

T. R. Agus, S. J. Thorpe, and D. Pressnitzer, Rapid Formation of Robust Auditory Memories: Insights from Noise, Neuron, vol.66, issue.4, pp.610-618, 2010.
DOI : 10.1016/j.neuron.2010.04.014

URL : https://hal.archives-ouvertes.fr/hal-00488683

F. Alibart, S. Pleutin, D. Guérin, C. Novembre, S. Lenfant et al., An Organic Nanoparticle Transistor Behaving as a Biological Spiking Synapse, Advanced Functional Materials, vol.1, issue.2, pp.330-337, 2010.
DOI : 10.1002/adfm.200901335

URL : https://hal.archives-ouvertes.fr/hal-00548959

F. Alibart, T. Sherwood, and D. Strukov, Hybrid CMOS/nanodevice circuits for high throughput pattern matching applications, 2011 NASA/ESA Conference on Adaptive Hardware and Systems (AHS), pp.279-286, 2011.
DOI : 10.1109/AHS.2011.5963948

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

F. Alibart, L. Gao, B. D. Hoskins, and D. B. Strukov, High precision tuning of state for memristive devices by adaptable variation-tolerant algorithm, Nanotechnology, vol.23, issue.7, p.75201, 2012.
DOI : 10.1088/0957-4484/23/7/075201

F. Alibart, E. Zamanidoost, and D. B. Strukov, Pattern classification by memristive crossbar circuits with ex-situ and in-situ hebbian training, 2012.

H. Ames, E. Mingolla, A. Sohail, B. Chandler, A. Gorchetchnikov et al., The Animat: New Frontiers in Whole Brain Modeling, IEEE Pulse, vol.3, issue.1, pp.47-50, 2012.
DOI : 10.1109/MPUL.2011.2175638

R. Ananthanarayanan, S. K. Esser, H. D. Simon, and D. S. Modha, The cat is out of the bag, Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis, SC '09, pp.1-63, 2009.
DOI : 10.1145/1654059.1654124

A. Anzai, X. Peng, and D. C. Van-essen, Neurons in monkey visual area V2 encode combinations of orientations, Nature Neuroscience, vol.7, issue.10, pp.1313-1321, 2007.
DOI : 10.1038/nn1975

J. Backus, Can Programming Be Liberated from the Von Neumann Style? A Functional Style and its Algebra of Programs, Commun. ACM, vol.21, issue.8, pp.613-641, 1978.
DOI : 10.1007/978-3-662-09507-2_10

I. Baek, M. Lee, S. Seo, M. Lee, D. Seo et al., Highly scalable nonvolatile resistive memory using simple binary oxide driven by asymmetric unipolar voltage pulses, Electron Devices Meeting, pp.587-59010, 2004.
DOI : 10.1109/iedm.2004.1419228

A. Beck, J. G. Bednorz, C. Gerber, C. Rossel, and D. Widmer, Reproducible switching effect in thin oxide films for memory applications, Applied Physics Letters, vol.77, issue.1, pp.139-141, 2000.
DOI : 10.1063/1.126902

F. Bedeschi, R. Fackenthal, C. Resta, E. Donze, M. Jagasivamani et al., A bipolar-selected phase change memory featuring multi-level cell storage. Solid-State Circuits, IEEE Journal, vol.44, issue.1, pp.217-22710, 2008.

Y. Bengio, R. De-mori, G. Flammia, and R. Kompe, Global optimization of a neural network-hidden Markov model hybrid, IEEE Transactions on Neural Networks, vol.3, issue.2, pp.252-259, 1992.
DOI : 10.1109/72.125866

G. Bi and M. Poo, Synaptic modifications in cultured hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type, Journal of Neuroscience, vol.18, pp.10464-10472, 1998.

S. M. Bohte and M. C. Mozer, Reducing the Variability of Neural Responses: A Computational Theory of Spike-Timing-Dependent Plasticity, Neural Computation, vol.20, issue.24, pp.371-403, 2007.
DOI : 10.1038/25665

J. Borghetti, Z. Li, J. Straznicky, X. Li, D. A. Ohlberg et al., A hybrid nanomemristor/transistor logic circuit capable of selfprogramming, Proceedings of the National Academy of Sciences, pp.1699-1703, 2009.

J. Borghetti, G. S. Snider, P. J. Kuekes, J. J. Yang, D. R. Stewart et al., ???Memristive??? switches enable ???stateful??? logic operations via material implication, Nature, vol.20, issue.7290, pp.873-876, 2010.
DOI : 10.1038/nature08940

J. S. Bowers, On the biological plausibility of grandmother cells: Implications for neural network theories in psychology and neuroscience., Psychological Review, vol.116, issue.1, pp.220-25110, 2009.
DOI : 10.1037/a0014462

M. Breitwisch, T. Nirschl, C. Chen, Y. Zhu, M. Lee et al., Novel Lithography-Independent Pore Phase Change Memory, 2007 IEEE Symposium on VLSI Technology, pp.100-101104339743, 1109.
DOI : 10.1109/VLSIT.2007.4339743

B. M. Breitwisch, R. W. Cheek, C. H. Lam, D. Modha, and B. Rajendran, System for electronic learning synapse with spike-timing dependent plasticity using phase change memory, p.2010

F. Brglez, C. Gloster, and G. Kedem, Hardware-based weighted random pattern generation for boundary scan, Proceedings. 'Meeting the Tests of Time'., International Test Conference, pp.264-274, 1989.
DOI : 10.1109/TEST.1989.82307

/. Spectre and . Guide, Cadence MMSIM 6.1 guide documents. Cadence, 2008.

X. Cao, X. Li, X. Gao, Y. Zhang, X. Liu et al., Effects of the compliance current on the resistive switching behavior of TiO2 thin films, Applied Physics A, vol.3, issue.4, pp.883-88710, 2009.
DOI : 10.1007/s00339-009-5351-7

N. Carnevale and M. Hines, The NEURON Book, 2006.
DOI : 10.1017/CBO9780511541612

A. Chanthbouala, V. Garcia, R. O. Cherifi, K. Bouzehouane, S. Fusil et al., A ferroelectric memristor, Nature Materials, vol.83, issue.10
DOI : 10.1038/nmat3415

W. Chen, C. Lee, D. Chao, Y. Chen, F. Chen et al., A Novel Cross-Spacer Phase Change Memory with Ultra-Small Lithography Independent Contact Area, 2007 IEEE International Electron Devices Meeting, pp.319-32210, 1109.
DOI : 10.1109/IEDM.2007.4418935

S. Choudhary, S. Sloan, A. Fok, E. Neckar, P. Trautmann et al., Silicon Neurons That Compute, International Conference on Artificial Neural Networks, 2012.
DOI : 10.1007/978-3-642-33269-2_16

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

L. Chua, Memristor-the missing circuit element. Circuit Theory, IEEE Transactions on, vol.18, issue.5, pp.507-519, 1971.

L. Chua, Resistance switching memories are memristors, Applied Physics A, vol.97, issue.4, pp.765-78310, 2011.
DOI : 10.1007/s00339-011-6264-9

L. Chua and S. M. Kang, Memristive devices and systems, Proceedings of the IEEE, vol.64, issue.2, pp.209-223, 1976.
DOI : 10.1109/PROC.1976.10092

J. Cho, Y. Yoo, and . Jun, A 58nm 1.8V 1Gb PRAM with 6.4MB/s program BW, Solid-State Circuits Conference Digest of Technical Papers (ISSCC), pp.500-502, 2011.

D. C. Cire?an, U. Meier, L. M. Gambardella, and S. Jürgen, Deep, Big, Simple Neural Nets for Handwritten Digit Recognition, Neural Computation, vol.19, issue.12, pp.3207-3220, 2010.
DOI : 10.1109/ICDAR.2003.1227801

Y. Dan and M. Ming-poo, Spike Timing-Dependent Plasticity of Neural Circuits, Neuron, vol.44, issue.1, pp.23-30, 2004.
DOI : 10.1016/j.neuron.2004.09.007

J. G. Daugman, Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters, Journal of the Optical Society of America A, vol.2, issue.7, pp.1160-1169, 1985.
DOI : 10.1364/JOSAA.2.001160

T. Delbruck, Frame-free dynamic digital vision, Intl. Symp. on Secure-Life Electronics , Advanced Electronics for Quality Life and Society, pp.21-2610, 2008.

A. Delorme, J. Gautrais, R. Van-rullen, and S. Thorpe, SpikeNET: A simulator for modeling large networks of integrate and fire neurons, Neurocomputing, vol.26, issue.27, pp.26-27, 1999.
DOI : 10.1016/S0925-2312(99)00095-8

A. Delorme, G. Richard, and M. Fabre-thorpe, Ultra-rapid categorisation of natural scenes does not rely on colour cues: a study in monkeys and humans, Vision Research, vol.40, issue.16, pp.2187-200, 2000.
DOI : 10.1016/S0042-6989(00)00083-3

A. Delorme, L. Perrinet, and S. Thorpe, Networks of integrate-and-fire neurons using Rank Order Coding B: Spike timing dependent plasticity and emergence of orientation selectivity, Neurocomputing, vol.38, issue.40, pp.539-545, 2001.
DOI : 10.1016/S0925-2312(01)00403-9

J. Deng, W. Dong, R. Socher, L. Li, K. Li et al., ImageNet: A large-scale hierarchical image database, CVPR09, 2009.

Y. H. Do, J. S. Kwak, Y. C. Bae, K. Jung, H. Im et al., Hysteretic bipolar resistive switching characteristics in TiO2/TiO2???x multilayer homojunctions, Applied Physics Letters, vol.95, issue.9, pp.93507-093507, 2009.
DOI : 10.1063/1.3224179

A. Fantini, V. Sousa, L. Perniola, E. Gourvest, J. Bastien et al., N-doped GeTe as performance booster for embedded Phase-Change Memories, 2010 International Electron Devices Meeting, pp.29-30, 2010.
DOI : 10.1109/IEDM.2010.5703441

URL : https://hal.archives-ouvertes.fr/hal-00625213

N. Frolet, M. Charbonneau, R. Tiron, J. Buckley, D. Mariolle et al., Development of organic resistive memory for flexible electronics, MRS Proceedings, pp.12-1430, 2012.
DOI : 10.1002/sia.3534

S. B. Furber, S. Temple, and A. D. Brown, High-performance computing for systems of spiking neurons, University of Pennsylvania Law Review, vol.154, issue.3, p.477, 2006.

C. Gasnier, Implémentation d'une transformée de fourier sur une architecture neuromorphique, 2011.

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

B. R. Glasberg and B. C. Moore, Derivation of auditory filter shapes from notched-noise data, Hearing Research, vol.47, issue.1-2, pp.103-13810, 1990.
DOI : 10.1016/0378-5955(90)90170-T

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

A. Gupta and L. Long, Character Recognition using Spiking Neural Networks, 2007 International Joint Conference on Neural Networks, pp.53-58, 2007.
DOI : 10.1109/IJCNN.2007.4370930

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

R. Guyonneau, R. Vanrullen, and S. J. Thorpe, Temporal codes and sparse representations: A key to understanding rapid processing in the visual system, Journal of Physiology-Paris, vol.98, issue.4-6, pp.4-6487, 2004.
DOI : 10.1016/j.jphysparis.2005.09.004

R. Guyonneau, R. Vanrullen, and S. J. Thorpe, Neurons Tune to the Earliest Spikes Through STDP, Neural Computation, vol.76, issue.4, pp.859-879, 2005.
DOI : 10.1038/25665

URL : https://hal.archives-ouvertes.fr/hal-00330516

Y. Ha, J. Yi, H. Horii, J. Park, S. Joo et al., An edge contact type cell for Phase Change RAM featuring very low power consumption, 2003 Symposium on VLSI Technology. Digest of Technical Papers (IEEE Cat. No.03CH37407), pp.175-17610, 2003.
DOI : 10.1109/VLSIT.2003.1221142

M. Hassoun, Associative Neural Memories: Theory and Implementation, 1993.

T. W. Hickmott, Low???Frequency Negative Resistance in Thin Anodic Oxide Films, Journal of Applied Physics, vol.33, issue.9, pp.2669-2682, 1962.
DOI : 10.1063/1.1702530

K. Hynna and K. Boahen, Neuronal ion-channel dynamics in silicon, 2006 IEEE International Symposium on Circuits and Systems, 2006.
DOI : 10.1109/ISCAS.2006.1693409

J. Chung and . Moon, A unified 7.5nm dash-type confined cell for high performance PRAM device, Electron Devices Meeting, pp.1-44796654, 2008.

G. Indiveri, B. Linares-barranco, T. J. Hamilton, A. Van-schaik, R. Etienne-cummings et al., Neuromorphic Silicon Neuron Circuits, Frontiers in Neuroscience, vol.5, issue.00073
DOI : 10.3389/fnins.2011.00073

URL : https://hal.archives-ouvertes.fr/hal-00597675

H. Ishii, T. Shibata, H. Kosaka, and T. Ohmi, Hardware-backpropagation learning of neuron MOS neural networks, International Technical Digest on Electron Devices Meeting, pp.435-438, 1992.
DOI : 10.1109/IEDM.1992.307395

I. Itrs, The International Technology Roadmap for Semiconductors, 2011.

E. M. Izhikevich and N. S. Desai, Relating STDP to BCM, Neural Computation, vol.20, issue.7, pp.1511-152310, 2003.
DOI : 10.1016/S0896-6273(01)00542-6

B. L. Jackson, D. S. Modha, and B. Rajendran, Producing spike-timing dependent plasticity in an ultra-dense synapse cross-bar array, pp.6-2011

D. S. Jeong, H. Schroeder, and R. Waser, Coexistence of Bipolar and Unipolar Resistive Switching Behaviors in a Pt???TiO[sub 2]???Pt Stack, Electrochemical and Solid-State Letters, vol.10, issue.8, pp.51-53, 2007.
DOI : 10.1149/1.2742989

X. Jin, A. Rast, F. Galluppi, S. Davies, and S. Furber, Implementing spiketiming-dependent plasticity on spinnaker neuromorphic hardware, Neural Networks (IJCNN) The 2010 International Joint Conference on, pp.1-8, 2010.
DOI : 10.1109/ijcnn.2010.5596372

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

S. H. Jo and W. Lu, CMOS Compatible Nanoscale Nonvolatile Resistance Switching Memory, Nano Letters, vol.8, issue.2, pp.392-397, 2008.
DOI : 10.1021/nl073225h

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

S. H. Jo, K. Kim, and W. Lu, High-Density Crossbar Arrays Based on a Si Memristive System, Nano Letters, vol.9, issue.2, pp.870-874, 2009.
DOI : 10.1021/nl8037689

S. H. Jo, K. Kim, and W. Lu, Programmable Resistance Switching in Nanoscale Two-Terminal Devices, Nano Letters, vol.9, issue.1, pp.496-500, 2009.
DOI : 10.1021/nl803669s

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

A. Joubert, B. Belhadj, and R. Heliot, A robust and compact 65 nm LIF analog neuron for computational purposes, 2011 IEEE 9th International New Circuits and systems conference, pp.9-12, 2011.
DOI : 10.1109/NEWCAS.2011.5981206

A. Joubert, B. Belhadj, O. Temam, and R. Heliot, Hardware spiking neurons design: Analog or digital?, The 2012 International Joint Conference on Neural Networks (IJCNN), pp.1-5
DOI : 10.1109/IJCNN.2012.6252600

I. V. Karpov, M. Mitra, D. Kau, G. Spadini, Y. A. Kryukov et al., Fundamental drift of parameters in chalcogenide phase change memory, Journal of Applied Physics, vol.102, issue.12, pp.124503-124503, 2007.
DOI : 10.1063/1.2825650

I. Kim, S. Cho, D. Im, E. Cho, D. Kim et al., High performance PRAM cell scalable to sub-20nm technology with below 4F2 cell size, extendable to DRAM applications, 2010 Symposium on VLSI Technology, pp.203-204, 2010.
DOI : 10.1109/VLSIT.2010.5556228

K. Kim, S. Gaba, D. Wheeler, J. M. Cruz-albrecht, T. Hussain et al., A Functional Hybrid Memristor Crossbar-Array/CMOS System for Data Storage and Neuromorphic Applications, Nano Letters, vol.12, issue.1, pp.389-39510, 1021.
DOI : 10.1021/nl203687n

B. H. Kosaka, T. Shibata, H. Ishii, and T. Ohmi, An excellent weight-updating-linearity EEPROM synapse memory cell for self-learning Neuron-MOS neural networks, MOS neural networks. Electron Devices, pp.135-143, 1995.
DOI : 10.1109/16.370025

M. Kozicki, M. Park, and M. Mitkova, Nanoscale Memory Elements Based on Solid-State Electrolytes, IEEE Transactions On Nanotechnology, vol.4, issue.3, pp.331-338, 2005.
DOI : 10.1109/TNANO.2005.846936

M. N. Kozicki and W. C. West, Programmable metallization cell structure and method of making same, pp.6-1998

P. J. Kuekes, W. Robinett, G. Seroussi, and R. S. Williams, Defect-tolerant interconnect to nanoelectronic circuits: internally redundant demultiplexers based on error-correcting codes, Nanotechnology, vol.16, issue.6, p.16869, 2005.
DOI : 10.1088/0957-4484/16/6/043

G. Ufert and . Muller, Conductive bridging RAM (CBRAM): an emerging nonvolatile memory technology scalable to sub 20nm, Electron Devices Meeting , 2005. IEDM Technical Digest. IEEE International, pp.754-757, 2005.

D. Kuzum, R. G. Jeyasingh, B. Lee, and H. P. Wong, Nanoelectronic Programmable Synapses Based on Phase Change Materials for Brain-Inspired Computing, Nano Letters, vol.12, issue.5, pp.2179-2186, 2012.
DOI : 10.1021/nl201040y

Q. Lai, Z. Li, L. Zhang, X. Li, W. F. Stickle et al., An Organic/Si Nanowire Hybrid Field Configurable Transistor, Nano Letters, vol.8, issue.3, pp.876-880, 2008.
DOI : 10.1021/nl073112y

Q. V. Le, R. Monga, M. Devin, G. Corrado, K. Chen et al., Building high-level features using large scale unsupervised learning, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2011.
DOI : 10.1109/ICASSP.2013.6639343

URL : http://arxiv.org/abs/1112.6209

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

J. Lee, H. Park, S. Cho, Y. Park, B. Bae et al., Highly scalable phase change memory with CVD G e S b T e for sub 50nm generation, VLSI Technology IEEE Symposium on, pp.102-103104339744, 1109.

J. H. Lee and K. K. Likharev, Defect-tolerant nanoelectronic pattern classifiers, International Journal of Circuit Theory and Applications, vol.441, issue.3, pp.239-264, 2007.
DOI : 10.1002/cta.410

H. Jeong, C. Jeong, C. Kwak, K. Kim, and . Kim, A 90nm 1.8v 512Mb diodeswitch PRAM with 266MB/s read throughput, Solid-State Circuits Conference, pp.472-616, 2007.

G. Leuba and R. Kraftsik, Changes in volume, surface estimate, three-dimensional shape and total number of neurons of the human primary visual cortex from midgestation until old age, Anatomy and Embryology, vol.190, issue.4, pp.351-36610, 1994.
DOI : 10.1007/BF00187293

Y. Li, A. Sinitskii, and J. M. Tour, Electronic two-terminal bistable graphitic memories, Nature Materials, vol.7, issue.12, pp.966-971, 2008.
DOI : 10.1002/adma.200306117

J. Liang and H. Wong, Cross-Point Memory Array Without Cell Selectors—Device Characteristics and Data Storage Pattern Dependencies, IEEE Transactions on Electron Devices, vol.57, issue.10, pp.2531-2538, 2010.
DOI : 10.1109/TED.2010.2062187

J. Liang, R. Jeyasingh, H. Chen, and H. Wong, A 1.4 µA reset current phase change memory cell with integrated carbon nanotube electrodes for cross-point memory application, VLSI Technology (VLSIT), 2011 Symposium on, pp.100-101, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00374701

P. Lichtsteiner, C. Posch, and T. Delbruck, A 128×128 120 dB 15 µs latency asynchronous temporal contrast vision sensor. Solid-State Circuits, IEEE Journal, vol.43, issue.2, pp.566-576, 2008.

C. Lin, C. Liu, C. Lin, and T. Tseng, Current status of resistive nonvolatile memories, Journal of Electroceramics, vol.2003, issue.1, pp.61-66, 2008.
DOI : 10.1007/s10832-007-9081-y

B. Linares-barranco and T. Serrano-gotarredona, Memristance can explain spike-time dependent plasticity in neural synapses, Nature Precedings, 2009.

E. Linn, R. Rosezin, C. Kugeler, and R. Waser, Complementary resistive switches for passive nanocrossbar memories, Nature Materials, vol.5, issue.5, pp.403-406, 2010.
DOI : 10.1038/nmat2748

J. Lisman and N. Spruston, Questions about STDP as a General Model of Synaptic Plasticity, Frontiers in Synaptic Neuroscience, vol.2, issue.140
DOI : 10.3389/fnsyn.2010.00140

S. Liu, A. Van-schaik, B. Minch, and T. Delbruck, Event-based 64-channel binaural silicon cochlea with Q enhancement mechanisms, Proceedings of 2010 IEEE International Symposium on Circuits and Systems, pp.2027-2030, 2010.
DOI : 10.1109/ISCAS.2010.5537164

P. Livi and G. Indiveri, A current-mode conductance-based silicon neuron for addressevent neuromorphic systems, ISCAS 2009. IEEE International Symposium on, pp.2898-2901, 2009.

W. Maass, Networks of spiking neurons: The third generation of neural network models, Neural Networks, vol.10, issue.9, pp.1659-167110, 1997.
DOI : 10.1016/S0893-6080(97)00011-7

W. Maass, T. Natschläger, and H. Markram, Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations, Neural Computation, vol.7, issue.11, pp.2531-256010, 2002.
DOI : 10.1038/35009102

N. Macmillan and C. Creelman, Detection Theory: A User's Guide, 2004.

B. H. Markram, J. Lübke, M. Frotscher, and B. Sakmann, Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs, Science, vol.275, issue.5297, pp.275213-215, 1997.
DOI : 10.1126/science.275.5297.213

T. Masquelier and S. J. Thorpe, Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity, PLoS Computational Biology, vol.28, issue.40, p.31, 2007.
DOI : 10.1371/journal.pcbi.0030031.sv003

URL : https://hal.archives-ouvertes.fr/hal-00135582

T. Masquelier, R. Guyonneau, and S. J. Thorpe, Spike Timing Dependent Plasticity Finds the Start of Repeating Patterns in Continuous Spike Trains, PLoS ONE, vol.26, issue.1, p.1377, 2008.
DOI : 10.1371/journal.pone.0001377.g007

URL : https://hal.archives-ouvertes.fr/hal-00229791

T. Masquelier, R. Guyonneau, and S. J. Thorpe, Competitive STDP-Based Spike Pattern Learning, Neural Computation, vol.65, issue.1, pp.1259-127606, 2008.
DOI : 10.1038/25665

URL : https://hal.archives-ouvertes.fr/hal-00383703

R. Meixner, H. Gobel, H. Qiu, C. Ucurum, W. Klix et al., A Physical-Based PSPICE Compact Model for Poly(3-hexylthiophene) Organic Field-Effect Transistors, IEEE Transactions on Electron Devices, vol.55, issue.7, pp.1776-178110, 2008.
DOI : 10.1109/TED.2008.925339

D. Modha and R. S. Shenoy, Electronic learning synapse with spike-timing dependent plasticity using unipolar memory-switching elements. Patent Application, p.2010

B. Muthuswamy and P. Kokate, Memristor-Based Chaotic Circuits, IETE Technical Review, vol.26, issue.6, pp.417-42910, 2009.
DOI : 10.4103/0256-4602.57827

B. Nessler, M. Pfeiffer, and W. Maass, STDP enables spiking neurons to detect hidden causes of their inputs, Advances in Neural Information Processing Systems, pp.1357-1365, 2010.

S. Chen, S. Zaidi, Y. Raoux, Y. Chen, R. Zhu et al., Write strategies for 2 and 4-bit multi-level phase-change memory, Electron Devices Meeting, pp.461-464, 2007.

M. Nishiyama, K. Hong, K. Mikoshiba, M. Poo, and K. Kato, Calcium stores regulate the polarity and input specificity of synaptic modification, Nature, vol.408, pp.584-58810, 2000.

D. Norman, The Brain That Changes Itself. United States, 2007.

C. Novembre, D. Guérin, K. Lmimouni, C. Gamrat, and D. Vuillaume, Gold nanoparticle-pentacene memory transistors, Applied Physics Letters, vol.92, issue.10, p.103314, 2008.
DOI : 10.1063/1.2896602

URL : https://hal.archives-ouvertes.fr/hal-00257396

B. A. Olshausen and D. J. Field, Emergence of simple-cell receptive field properties by learning a sparse code for natural images, Nature, vol.381, issue.6583, pp.607-609, 1996.
DOI : 10.1038/381607a0

G. Palma, E. Vianello, C. Cagli, G. Molas, M. Reyboz et al., Experimental investigation and empirical modeling of the set and reset kinetics of Ag-GeS 2 conductive bridging memories, Memory Workshop (IMW), 2012 4th IEEE International, pp.1-4, 2012.

N. Papandreou, H. Pozidis, T. Mittelholzer, G. Close, M. Breitwisch et al., Drift-Tolerant Multilevel Phase-Change Memory, 2011 3rd IEEE International Memory Workshop (IMW), pp.1-4, 2011.
DOI : 10.1109/IMW.2011.5873231

I. Pavlov, Conditioned reflexes: An investigation of the physiological activity of the cerebral cortex. translated and edited by g. v. anrep, 1927.

F. Pellizzer, A. Benvenuti, B. Gleixner, Y. Kim, B. Johnson et al., A 90nm Phase Change Memory Technology for Stand-Alone Non-Volatile Memory Applications, 2006 Symposium on VLSI Technology, 2006. Digest of Technical Papers., pp.122-12310, 2006.
DOI : 10.1109/VLSIT.2006.1705247

C. Peng, L. Cheng, and M. Mansuripur, Experimental and theoretical investigations of laser-induced crystallization and amorphization in phase-change optical recording media, Journal of Applied Physics, vol.82, issue.9, pp.4183-4191, 1997.
DOI : 10.1063/1.366220

Y. Pershin and M. D. Ventra, Experimental demonstration of associative memory with memristive neural networks, Neural Networks, vol.23, issue.7, pp.881-886
DOI : 10.1016/j.neunet.2010.05.001

Y. Pershin and M. D. Ventra, Practical approach to programmable analog circuits with memristors. Circuits and Systems I: Regular Papers, IEEE Transactions on, vol.57, issue.8, pp.1857-1864, 2009.

Y. V. Pershin, S. L. Fontaine, and M. D. Ventra, Memristive model of amoeba learning, Physical Review E, vol.80, issue.2, p.21926, 2009.
DOI : 10.1103/PhysRevE.80.021926

M. D. Pickett, D. B. Strukov, J. L. Borghetti, J. J. Yang, G. S. Snider et al., Switching dynamics in titanium dioxide memristive devices, Journal of Applied Physics, vol.106, issue.7, p.74508, 2009.
DOI : 10.1063/1.3236506

A. Pirovano, F. Pellizzer, I. Tortorelli, R. Harrigan, M. Magistretti et al., Self-aligned µtrench phase-change memory cell architecture for 90nm technology and beyond, Solid State Device Research Conference, pp.222-22510, 2007.

G. Potamianos, C. Neti, G. Gravier, A. Garg, and A. Senior, Recent advances in the automatic recognition of audiovisual speech, Proceedings of the IEEE, pp.1306-1326, 2003.

D. Povey, L. Burget, M. Agarwal, P. Akyazi, F. Kai et al., The subspace Gaussian mixture model???A structured model for speech recognition, Computer Speech & Language, vol.25, issue.2, pp.404-439, 2011.
DOI : 10.1016/j.csl.2010.06.003

J. Pérez-carrasco, C. Serrano, B. Acha, T. Serrano-gotarredona, and B. Linares-barranco, Spike-Based Convolutional Network for Real-Time Processing, 2010 20th International Conference on Pattern Recognition, pp.3085-3088, 2010.
DOI : 10.1109/ICPR.2010.756

B. R. Quian-quiroga, L. Reddy, G. Kreiman, C. Koch, and I. Fried, Invariant visual representation by single neurons in the human brain, Nature, vol.435, issue.7045, pp.1102-1107, 2005.
DOI : 10.1038/nature03687

C. Ramana, M. Moodely, V. Kannan, A. Maity, J. Jayaramudu et al., Fabrication of stable low voltage organic bistable memory device, Sensors and Actuators B: Chemical, vol.161, issue.1, pp.684-688
DOI : 10.1016/j.snb.2011.11.012

W. Robinett, M. Pickett, J. Borghetti, Q. Xia, G. S. Snider et al., A memristor-based nonvolatile latch circuit, Nanotechnology, vol.21, issue.23, p.235203, 2010.
DOI : 10.1088/0957-4484/21/23/235203

F. Rosenblatt, The perceptron: A probabilistic model for information storage and organization in the brain., Psychological Review, vol.65, issue.6, pp.386-408, 1958.
DOI : 10.1037/h0042519

A. Roy, Discovery of concept cells in the human brain ? could it change our science? Natural Intelligence: the INNS Magazine, pp.23-29, 2011.

J. Rubin, D. D. Lee, and H. Sompolinsky, Equilibrium Properties of Temporally Asymmetric Hebbian Plasticity, Physical Review Letters, vol.86, issue.2, p.364, 2001.
DOI : 10.1103/PhysRevLett.86.364

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning Internal Representations by Error Propagation, Parallel distributed processing: explorations in the microstructure of cognition, pp.318-362, 1986.
DOI : 10.1016/B978-1-4832-1446-7.50035-2

Y. Sasago, M. Kinoshita, T. Morikawa, K. Kurotsuchi, S. Hanzawa et al., Cross-point phase change memory with 4F2 cell size driven by low-contact-resistivity poly-Si diode, VLSI Technology, 2009 Symposium on, pp.24-25, 2009.

Y. Sato, K. Tsunoda, K. Kinoshita, H. Noshiro, M. Aoki et al., Sub-<formula formulatype="inline"><tex>$\hbox{100-}\mu\hbox{A}$</tex> </formula> Reset Current of Nickel Oxide Resistive Memory Through Control of Filamentary Conductance by Current Limit of MOSFET, IEEE Transactions on Electron Devices, vol.55, issue.5, pp.1185-119110, 2008.
DOI : 10.1109/TED.2008.919385

A. Sawa, Resistive switching in transition metal oxides, Materials Today, vol.11, issue.6, pp.28-3610, 2008.
DOI : 10.1016/S1369-7021(08)70119-6

J. Schemmel, A. Grubl, K. Meier, and E. Mueller, Implementing synaptic plasticity in a vlsi spiking neural network model, Neural Networks, 2006.

J. Schemmel, D. Brüderle, A. Grübl, M. Hock, K. Meier et al., A waferscale neuromorphic hardware system for large-scale neural modeling, Circuits and Systems (ISCAS Proceedings of 2010 IEEE International Symposium on, pp.1947-1950, 2010.

C. Schindler, S. Thermadam, R. Waser, and M. Kozicki, Bipolar and Unipolar Resistive Switching in Cu-Doped <formula formulatype="inline"><tex>$ \hbox{SiO}_{2}$</tex></formula>, IEEE Transactions on Electron Devices, vol.54, issue.10, pp.2762-2768, 2007.
DOI : 10.1109/TED.2007.904402

C. Schindler, M. Weides, M. N. Kozicki, and R. Waser, Low current resistive switching in Cu???SiO2 cells, Applied Physics Letters, vol.92, issue.12, pp.122910-122910, 2008.
DOI : 10.1063/1.2903707

P. Schrogmeier, M. Angerbauer, S. Dietrich, M. Ivanov, H. Honigschmid et al., Time discrete voltage sensing and iterative programming control for a 4F2 multilevel CBRAM, VLSI Circuits IEEE Symposium on, pp.186-18710, 2007.

G. Servalli, A 45nm generation Phase Change Memory technology, 2009 IEEE International Electron Devices Meeting (IEDM), pp.1-4, 2009.
DOI : 10.1109/IEDM.2009.5424409

M. Sharad, C. Augustine, G. Panagopoulos, and K. Roy, Proposal for neuromorphic hardware using spin devices. CoRR, abs/1206, 2012.

M. Slaney, An efficient implementation of the patterson-holdsworth auditory filter bank, apple computer TR #35, 1993.

G. Snider, Spike-timing-dependent learning in memristive nanodevices, 2008 IEEE International Symposium on Nanoscale Architectures, pp.85-92, 2008.
DOI : 10.1109/NANOARCH.2008.4585796

G. Snider, R. Amerson, D. Carter, H. Abdalla, M. Qureshi et al., From Synapses to Circuitry: Using Memristive Memory to Explore the Electronic Brain, Computer, vol.44, issue.2, pp.21-28, 2011.
DOI : 10.1109/MC.2011.48

G. S. Snider, Self-organized computation with unreliable, memristive nanodevices, Nanotechnology, vol.18, issue.36, pp.36520210-1088, 2007.
DOI : 10.1088/0957-4484/18/36/365202

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

G. S. Snider and R. S. Williams, Nano/CMOS architectures using a field-programmable nanowire interconnect, Nanotechnology, vol.18, issue.3, pp.3520410-1088, 2007.
DOI : 10.1088/0957-4484/18/3/035204

K. Sonoda, A. Sakai, M. Moniwa, K. Ishikawa, O. Tsuchiya et al., A Compact Model of Phase-Change Memory Based on Rate Equations of Crystallization and Amorphization, IEEE Transactions on Electron Devices, vol.55, issue.7, pp.1672-168110, 2008.
DOI : 10.1109/TED.2008.923740

R. Stein, Some Models of Neuronal Variability, Biophysical Journal, vol.7, issue.1, pp.37-6810, 1967.
DOI : 10.1016/S0006-3495(67)86574-3

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

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

D. B. Strukov, J. L. Borghetti, and R. S. Williams, Coupled Ionic and Electronic Transport Model of Thin-Film Semiconductor Memristive Behavior, Small, vol.11, issue.9, pp.1058-1063, 2009.
DOI : 10.1002/smll.200801323

M. Suri, V. Sousa, L. Perniola, D. Vuillaume, and B. Desalvo, Phase change memory for synaptic plasticity application in neuromorphic systems, The 2011 International Joint Conference on Neural Networks, pp.619-624, 2011.
DOI : 10.1109/IJCNN.2011.6033278

URL : https://hal.archives-ouvertes.fr/hal-00800006

B. Swaroop, W. West, G. Martinez, M. Kozicki, and L. Akers, Programmable current mode Hebbian learning neural network using programmable metallization cell, ISCAS '98. Proceedings of the 1998 IEEE International Symposium on Circuits and Systems (Cat. No.98CH36187), pp.33-36, 1998.
DOI : 10.1109/ISCAS.1998.703888

K. Szot, W. Speier, G. Bihlmayer, and R. Waser, Switching the electrical resistance of individual dislocations in single-crystalline SrTiO3, Nature Materials, vol.44, issue.4, pp.312-32010, 1038.
DOI : 10.1038/nmat1614

S. Tam, M. Holler, J. Brauch, A. Pine, A. Peterson et al., A reconfigurable multi-chip analog neural network: recognition and back-propagation training, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks, pp.625-63010, 1992.
DOI : 10.1109/IJCNN.1992.226918

S. Thorpe, D. Fize, and C. Marlot, Speed of processing in the human visual system, Nature, issue.6582, pp.381520-522, 1996.

S. Thorpe, A. Brilhault, and J. Perez-carrasco, Suggestions for a biologically inspired spiking retina using order-based coding, Proceedings of 2010 IEEE International Symposium on Circuits and Systems, pp.265-268, 2010.
DOI : 10.1109/ISCAS.2010.5537898

S. J. Thorpe, Spike arrival tiems: A highly efficient coding scheme for neural networks, Parallel processing in neural systems, pp.91-94, 1990.

S. J. Thorpe and M. Imbert, Biological constraints on connectionist modelling, Connectionism in Perspective, pp.63-92, 1989.

S. J. Thorpe, R. Guyonneau, N. Guilbaud, J. Allegraud, and R. Vanrullen, SpikeNet: real-time visual processing with one spike per neuron, Neurocomputing, vol.58, issue.60, pp.58-60857, 2004.
DOI : 10.1016/j.neucom.2004.01.138

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

A. Torralba, How many pixels make an image?, Visual Neuroscience, vol.63, issue.01, pp.123-131, 2009.
DOI : 10.1080/13506280444000562

M. Tsodyks, K. Pawelzik, and H. Markram, Neural Networks with Dynamic Synapses, Neural Computation, vol.17, issue.4, pp.821-83510, 1998.
DOI : 10.1085/jgp.80.4.583

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

K. Tsuchida, T. Inaba, K. Fujita, Y. Ueda, T. Shimizu et al., A 64Mb MRAM with clampedreference and adequate-reference schemes, Solid-State Circuits Conference Digest of Technical Papers (ISSCC), pp.258-259, 2010.

R. Van-rullen and S. J. Thorpe, Rate Coding Versus Temporal Order Coding: What the Retinal Ganglion Cells Tell the Visual Cortex, Neural Computation, vol.78, issue.6, pp.1255-128310, 2001.
DOI : 10.1016/0042-6989(96)00098-3

R. Vanrullen and S. J. Thorpe, Surfing a spike wave down the ventral stream, Vision Research, vol.42, issue.23, pp.2593-261510, 2002.
DOI : 10.1016/S0042-6989(02)00298-5

URL : https://hal.archives-ouvertes.fr/tel-00078702

M. Versace and B. Chandler, The brain of a new machine, IEEE Spectrum, vol.47, issue.12, pp.30-37, 2010.
DOI : 10.1109/MSPEC.2010.5644776

P. Viola and M. J. Jones, Robust Real-Time Face Detection, International Journal of Computer Vision, vol.57, issue.2, pp.137-154, 2004.
DOI : 10.1023/B:VISI.0000013087.49260.fb

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

R. Waser and M. Aono, Nanoionics-based resistive switching memories, Nature Materials, vol.6, issue.11, pp.833-840, 2007.
DOI : 10.1142/9789814287005_0016

H. Wersing and E. Körner, Learning Optimized Features for Hierarchical Models of Invariant Object Recognition, Neural Computation, vol.27, issue.7, pp.1559-1588, 2003.
DOI : 10.1162/089976602317318938

B. Widrow, W. H. Pierce, and J. Angell, Birth, life, and death in microelectronic systems. Military Electronics, IRE Transactions on, issue.53, pp.191-201, 1961.
DOI : 10.1109/iret-mil.1961.5008348

S. Wimbauer, W. Gerstner, and J. Van-hemmen, Emergence of spatiotemporal receptive fields and its application to motion detection, Biological Cybernetics, vol.335, issue.1, pp.81-92, 1994.
DOI : 10.1007/BF00206240

G. M. Wittenberg and S. S. Wang, Malleability of Spike-Timing-Dependent Plasticity at the CA3-CA1 Synapse, Journal of Neuroscience, vol.26, issue.24, pp.6610-661710, 2006.
DOI : 10.1523/JNEUROSCI.5388-05.2006

M. A. Woodin, K. Ganguly, and M. Ming-poo, Coincident pre-and postsynaptic activity modifies gabaergic synapses by postsynaptic changes
DOI : 10.1016/s0896-6273(03)00507-5

URL : http://doi.org/10.1016/s0896-6273(03)00507-5

M. Wuttig and N. Yamada, Phase-change materials for rewriteable data storage, Nature Materials, vol.6, issue.11, pp.824-832, 2007.
DOI : 10.1038/nmat2009

. Williams, Memristor-cmos hybrid integrated circuits for reconfigurable logic, Nano Letters, vol.9, issue.10, pp.3640-3645, 2009.

F. Xiong, A. D. Liao, D. Estrada, and E. Pop, Low-Power Switching of Phase-Change Materials with Carbon Nanotube Electrodes, Science, vol.332, issue.6029, pp.568-570, 2011.
DOI : 10.1126/science.1201938

J. J. Yang, M. D. Pickett, X. Li, D. A. Ohlberg, D. R. 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

J. J. Yang, M. Zhang, M. D. Pickett, F. Miao, J. P. Strachan et al., Engineering nonlinearity into memristors for passive crossbar applications, Applied Physics Letters, vol.100, issue.11

J. J. Yon, E. Mottin, L. Biancardini, L. Letellier, and J. L. Tissot, Infrared Microbolometer Sensors and Their Application in Automotive Safety, Advanced Microsystems for Automotive Applications 2003, VDI- Buch, pp.137-157, 2003.
DOI : 10.1007/978-3-540-76988-0_13

B. S. Yu and H. Wong, Modeling the switching dynamics of programmablemetallization-cell (PMC) memory and its application as synapse device for a neuromorphic computation system, Electron Devices Meeting (IEDM), 2010.

S. Yu, B. Lee, and H. P. Wong, Metal Oxide Resistive Switching Memory, Functional Metal Oxide Nanostructures, volume 149 of Springer Series in Materials Science, pp.303-335
DOI : 10.1007/978-1-4419-9931-3_13

K. Zaghloul and K. Boahen, Optic Nerve Signals in a Neuromorphic Chip II: Testing and Results, IEEE Transactions on Biomedical Engineering, vol.51, issue.4, pp.667-675, 2004.
DOI : 10.1109/TBME.2003.821040

C. Zamarreño-ramos, L. A. Camuñas-mesa, J. A. Perez-carrasco, T. Masquelier, T. Serrano-gotarredona et al., On spike-timing-dependentplasticity , memristive devices, and building a self-learning visual cortex, Frontiers in Neuroscience, vol.5, issue.26

W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, Face recognition, ACM Computing Surveys, vol.35, issue.4, pp.399-458, 2003.
DOI : 10.1145/954339.954342

URL : https://hal.archives-ouvertes.fr/hal-01339319

V. V. Zhirnov, R. Meade, R. K. Cavin, and G. Sandhu, Scaling limits of resistive memories, Nanotechnology, vol.22, issue.25, pp.25402710-1088, 2011.
DOI : 10.1088/0957-4484/22/25/254027

M. Ziegler, R. Soni, T. Patelczyk, M. Ignatov, T. Bartsch et al., An Electronic Version of Pavlov's Dog, Advanced Functional Materials, vol.7, issue.13, pp.2744-2749, 2012.
DOI : 10.1002/adfm.201200244