The missing memristor found, Nature, vol.4, issue.7191, pp.80-83, 2008. ,
DOI : 10.1038/nature06932
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
URL : http://arxiv.org/pdf/1110.1393
Two-Terminal Carbon Nanotube Programmable Devices for Adaptive Architectures, Advanced Materials, vol.5, issue.6, pp.702-706, 2010. ,
DOI : 10.4324/9780203451519
URL : https://hal.archives-ouvertes.fr/hal-00548986
A Functional Hybrid Memristor Crossbar-Array/CMOS System for Data Storage and Neuromorphic Applications, Nano Letters, vol.12, issue.1, pp.389-395, 2011. ,
DOI : 10.1021/nl203687n
Variability, compensation and homeostasis in neuron and network function, Nature Reviews Neuroscience, vol.15, issue.7, pp.563-574, 2006. ,
DOI : 10.1016/j.neuint.2005.12.029
Learning in silicon: Timing is everything Advances in neural information processing systems, pp.281-1185, 2006. ,
A VLSI Array of Low-Power Spiking Neurons and Bistable Synapses With Spike-Timing Dependent Plasticity, IEEE Transactions on Neural Networks, vol.17, issue.1, pp.211-221, 2006. ,
DOI : 10.1109/TNN.2005.860850
URL : https://pub.uni-bielefeld.de/download/2426586/2459959
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
On neuromorphic spiking architectures for asynchronous STDP memristive systems, Proceedings of 2010 IEEE International Symposium on Circuits and Systems, pp.1659-1662, 2010. ,
DOI : 10.1109/ISCAS.2010.5537484
Spike-timing-dependent learning in memristive nanodevices, 2008 IEEE International Symposium on Nanoscale Architectures, pp.85-92, 2008. ,
DOI : 10.1109/NANOARCH.2008.4585796
Implementation of biologically plausible spiking neural network models on the memristor crossbarbased CMOS/nano circuits, European Conference on Circuit Theory and Design (ECCTD), pp.563-566, 2009. ,
DOI : 10.1109/ecctd.2009.5275035
Simulation of a memristorbased spiking neural network immune to device variations, Proc. of the Int. Joint Conf. on Neural Networks (IJCNN), pp.1775-1781, 2011. ,
DOI : 10.1109/ijcnn.2011.6033439
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
Phase change memory as synapse for ultra-dense neuromorphic systems: Application to complex visual pattern extraction, 2011 International Electron Devices Meeting, 2011. ,
DOI : 10.1109/IEDM.2011.6131488
URL : https://hal.archives-ouvertes.fr/hal-00799997
Analog memory and spike-timing-dependent plasticity characteristics of a nanoscale titanium oxide bilayer resistive switching device, Nanotechnology, vol.22, issue.25, p.254023, 2011. ,
DOI : 10.1088/0957-4484/22/25/254023
An Electronic Synapse Device Based on Metal Oxide Resistive Switching Memory for Neuromorphic Computation, IEEE Transactions on Electron Devices, vol.58, issue.8, pp.2729-2737, 2011. ,
DOI : 10.1109/TED.2011.2147791
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
Visual Pattern Extraction Using Energy-Efficient ???2-PCM Synapse??? Neuromorphic Architecture, IEEE Transactions on Electron Devices, vol.59, issue.8, pp.1-9, 2012. ,
DOI : 10.1109/TED.2012.2197951
URL : https://hal.archives-ouvertes.fr/hal-00787385
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
Synaptic Modification by Correlated Activity: Hebb's Postulate Revisited, Annual Review of Neuroscience, vol.24, issue.1, pp.139-166, 2001. ,
DOI : 10.1146/annurev.neuro.24.1.139
Spike Timing-Dependent Plasticity of Neural Circuits, Neuron, vol.44, issue.1, pp.23-30, 2004. ,
DOI : 10.1016/j.neuron.2004.09.007
URL : https://doi.org/10.1016/j.neuron.2004.09.007
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
Cognitive computing, Communications of the ACM, vol.54, issue.8, p.62, 2011. ,
DOI : 10.1145/1978542.1978559
Neuromorphic, Digital, and Quantum Computation With Memory Circuit Elements, Proceedings of the IEEE, vol.100, issue.6, 2010. ,
DOI : 10.1109/JPROC.2011.2166369
URL : http://arxiv.org/pdf/1009.6025
Hebbian Learning in Spiking Neural Networks With Nanocrystalline Silicon TFTs and Memristive Synapses, IEEE Transactions on Nanotechnology, vol.10, issue.5, pp.1066-1073, 2011. ,
DOI : 10.1109/TNANO.2011.2105887
???Memristive??? switches enable ???stateful??? logic operations via material implication, Nature, vol.20, issue.7290, pp.873-876, 2010. ,
DOI : 10.1038/nature08940
On the stochastic nature of resistive switching in metal oxide RRAM: physical modeling, monte carlo simulation , and experimental characterization Gradient-based learning applied to document recognition, Electron Devices Meeting (IEDM) Proc. IEEE, pp.2278-2324, 1998. ,
A Defect-Tolerant Computer Architecture: Opportunities for Nanotechnology, Science, vol.280, issue.5370, pp.1716-1721, 1998. ,
DOI : 10.1126/science.280.5370.1716
Defect-tolerant nanoelectronic pattern classifiers, International Journal of Circuit Theory and Applications, vol.441, issue.3, pp.239-264, 2007. ,
DOI : 10.1016/B978-044451494-3/50002-0
Robust neural logick block (NLB) based on memristor crossbar array, Proc. of IEEE/ACM Int. Symp. Nanoscale Architectures, 2011. ,
DOI : 10.1109/nanoarch.2011.5941495
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
Learning Real-World Stimuli in a Neural Network with Spike-Driven Synaptic Dynamics, Neural Computation, vol.82, issue.11, pp.2881-2912, 2007. ,
DOI : 10.1126/science.1082212
URL : http://www.zora.uzh.ch/id/eprint/93166/1/E2881.pdf
STDP enables spiking neurons to detect hidden causes of their inputs, Advances in Neural Information Processing Systems (NIPS'09), pp.1357-1365 ,
A neuromorphic visual system using RRAM synaptic devices with Sub-pJ energy and tolerance to variability: Experimental characterization and large-scale modeling, 2012 International Electron Devices Meeting, 2012. ,
DOI : 10.1109/IEDM.2012.6479018
Self-organized computation with unreliable, memristive nanodevices, Nanotechnology, vol.18, issue.36, p.365202, 2007. ,
DOI : 10.1088/0957-4484/18/36/365202
Instar and outstar learning with memristive nanodevices, Nanotechnology, vol.22, issue.1, p.15201, 2011. ,
DOI : 10.1088/0957-4484/22/1/015201
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
Complementary resistive switches for passive nanocrossbar memories, Nature Materials, vol.5, issue.5, pp.403-406, 2010. ,
DOI : 10.1063/1.1823026
Engineering nonlinearity into memristors for passive crossbar applications, Applied Physics Letters, vol.100, issue.11, pp.113501-113501, 2012. ,
DOI : 10.1103/PhysRev.187.828
Physical aspects of low power synapses based on phase change memory devices, Journal of Applied Physics, vol.18, issue.5, pp.54904-054904, 2012. ,
DOI : 10.1109/IMW.2012.6213674
URL : https://hal.archives-ouvertes.fr/hal-00787372
A 128$\times$128 120 dB 15 $\mu$s Latency Asynchronous Temporal Contrast Vision Sensor, IEEE Journal of Solid-State Circuits, vol.43, issue.2, pp.566-576, 2008. ,
DOI : 10.1109/JSSC.2007.914337
AER EAR: A Matched Silicon Cochlea Pair With Address Event Representation Interface, IEEE Transactions on Circuits and Systems I: Regular Papers, vol.54, issue.1, pp.48-59, 2007. ,
DOI : 10.1109/TCSI.2006.887979
Learning with memristive devices: How should we model their behavior?, 2011 IEEE/ACM International Symposium on Nanoscale Architectures, p.150, 2011. ,
DOI : 10.1109/NANOARCH.2011.5941497
A digital neurosynaptic core using embedded crossbar memory with 45pJ per spike in 45nm, 2011 IEEE Custom Integrated Circuits Conference (CICC), pp.1-4, 2011. ,
DOI : 10.1109/CICC.2011.6055294
URL : http://www.modha.org/papers/012.CICC1.pdf
Implementing homeostatic plasticity in VLSI networks of spiking neurons, 2008 15th IEEE International Conference on Electronics, Circuits and Systems, pp.682-685, 2008. ,
DOI : 10.1109/ICECS.2008.4674945
URL : http://www.zora.uzh.ch/id/eprint/17606/1/Bartolozzi_VLSI_V.pdf
Extraction of temporally correlated features from dynamic vision sensors with spike-timing-dependent plasticity, Neural Networks, vol.32, pp.339-348, 2012. ,
DOI : 10.1016/j.neunet.2012.02.022
URL : https://hal.archives-ouvertes.fr/hal-00706681
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, 2012. ,
DOI : 10.1109/5.726791
Deep big simple neural nets excel on handwritten digit recognition, 2010. ,