Plasticity in memristive devices for spiking neural networks

Frontiers in Neuroscience

Volumn 9, Issue MAR, 2015, Pages

  Saïghi, Sylvain ^a   Mayr, Christian G ^b   Serrano Gotarredona, Teresa ^c   Schmidt, Heidemarie ^d   Lecerf, Gwendal ^a   Tomas, Jean ^a   Grollier, Julie ^e,f   Boyn, Sören ^e,f   Vincent, Adrien F ^f   Querlioz, Damien ^f   La Barbera, Selina ^g   Alibart, Fabien ^g   Vuillaume, Dominique ^g   Bichler, Olivier ^h   Gamrat, Christian ^h   Linares Barranco, Bernabé ^c  

^a UNIVERSITÉ DE BORDEAUX   (France)

^b UNIVERSITY OF ZURICH   (Switzerland)

^c UNIVERSITY OF SEVILLE   (Spain)

^d CHEMNITZ UNIVERSITY OF TECHNOLOGY   (Germany)

^e CNRS   (France)

^f UNIVERSITÉ PARIS SACLAY   (France)

^g INSTITUT D ELECTRONIQUE   (France)

^h DIF   (France)

Author keywords

Hardware neural network; Memristive device; Memristor; Neuromorphic engineering; Plasticity

Indexed keywords

ACTION POTENTIAL; CONTROLLED STUDY; ELECTRIC POTENTIAL; ELECTRIC RESISTANCE; HUMAN; LEARNING; LEARNING ALGORITHM; LONG TERM MEMORY; MEMRISTIVE DEVICE; NANODEVICE; NERVE CELL NETWORK; NERVE CELL PLASTICITY; NEUROTRANSMITTER RELEASE; REVIEW; SEMICONDUCTOR; SHORT TERM MEMORY; SPIKING NEURAL NETWORK; STOCHASTIC MODEL; SYNAPSE; TORQUE; WAVEFORM;

EID: 84928040161     PISSN: 16624548     EISSN: 1662453X     Source Type: Journal    
DOI: 10.3389/fnins.2015.00051     Document Type: Review

References (106)

1. Abbott, L. F., Varela, J. A., Sen, K., and Nelson, S. B. (1997). Synaptic depression and cortical gain control. Science 275, 221-224. doi: 10.1126/science.275.5297.221
2. Alibart, F., Pleutin, S., Bichler, O., Gamrat, C., Serrano-Gotarredona, T., Linares-Barranco, B., et al. (2012). A memristive nanoparticle/organic hybrid synapstor for neuroinspired computing. Adv. Funct. Mater . 22, 609-616. doi: 10.1002/adfm.201101935
3. Alibart, F., Pleutin, S., Guérin, D., Novembre, C., Lenfant, S., Lmimouni, K., et al. (2010). An organic nanoparticle transistor behaving as a biological spiking synapse. Adv. Funct. Mater . 19, 330-337. doi: 10.1002/adfm.200901335
4. Baek, I. G., Lee, M. S., Seo, S., Lee, M. J., Seo, D. H., Suh, D.-S., et al. (2004). Highly scalable nonvolatile resistive memory using simple binary oxide driven by asymmetric unipolar voltage pulses. Tech. Dig. IEEE Int. Electron Devices Meet . 587-590. doi: 10.1109/IEDM.2004.1419228
5. Bi, G., and Poo, M. (1998). Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J. Neurosci . 18, 10464-10472.
6. Bi, G., and Poo, M. (2001). Synaptic modification by correlated activity: Hebb's postulate revisited. Annu. Rev. Neurosci . 24, 139-166. doi: 10.1146/annurev.neuro.24.1.139
7. Bibes, M., Grollier, J., Barthélémy, A., and Mage, J.-C. (2010). Ferroelectric Device with Adjustable Resistance . Patent WO 2010142762 A1.
8. Bichler, O., Querlioz, D., Thorpe, S. J., Bourgoin, J.-P., and Gamrat, C. (2012a). Extraction of temporally correlated features from dynamic vision sensors with spike-timing-dependent plasticity. Neural Netw . 32, 339-348. doi: 10.1016/j.neunet.2012.02.022
9. Bichler, O., Suri, M., Querlioz, D., Vuillaume, D., DeSalvo, B., and Gamrat, C. (2012b). Visual pattern extraction using energy-efficient "2-PCM synapse" neuromorphic architecture. IEEE Trans. Electron Devices 59, 2206-2214. doi: 10.1109/TED.2012.2197951
10. Bliss, T. V. P., and Collingridge, G. L. (1993). A synaptic model of memory: long-term potentiation in the hippocampus. Nature 361, 31. doi: 10.1038/361031a0
11. Boegerhausen, M., Suter, P., and Liu, S.-C. (2003). Modeling short-term synaptic depression in silicon. Neural Comput . 15, 331-348. doi: 10.1162/089976603762552942
12. Boyn, S., Girod, S., Garcia, V., Fusil, S., Xavier, S., Deranlot, C., et al. (2014). High-performance ferroelectric memory based on fully patterned tunnel junctions. Appl. Phys. Lett . 104, 052909. doi: 10.1063/1.4864100
13. Buonomano, D. V., and Maass, W. (2009). State-dependent computations: spatiotemporal processing in cortical networks. Nat. Rev. Neurosci . 10, 113-125. doi: 10.1038/nrn2558
14. Cantley, K. D., Subramaniam, A., Stiegler, H. J., Chapman, R. A., and Vogel, E. M. (2011). Hebbian learning in spiking neural networks with nanocrystalline silicon TFTs and memristive synapses. IEEE Trans. Nanotechnol . 5, 1066. doi: 10.1109/TNANO.2011.2105887
15. Cassenaer, S., and Laurent, G. (2007). Hebbian STDP in mushroom bodies facilitates the synchronous flow of olfactory information in locusts. Nature 448, 709-713. doi: 10.1038/nature05973
16. Cederstroem, L., Starke, P., Mayr, C., Shuai, Y., Schmidt, H., and Schüffny, R. (2013). A model based comparison of BiFeO3 device applicability in neuromorphic hardware. IEEE Int. Symp. Circuits Syst . 2323-2326. doi: 10.1109/ISCAS.2013.6572343
17. Chang, T., Jo, S.-H., and Lu, W. (2011). Short term memory to long term memory transition in a nanoscale memristor. ACS Nano . 5, 7669-7676. doi: 10.1021/nn202983n
18. Chanthbouala, A., Crassous, A., Garcia, V., Bouzehouane, K., Fusil, S., Moya, X., et al. (2012a). Solid-state memories based on ferroelectric tunnel junctions. Nature Nano . 7, 101. doi: 10.1038/nnano.2011.213
19. Chanthbouala, A., Garcia, V., Cherifi, R. O., Bouzehouane, K., Fusil, S., Moya, X., et al. (2012b). A ferroelectric memristor. Nature Mater . 11, 860-864. doi: 10.1038/nmat3415
20. Chanthbouala, A., Matsumoto, R., Grollier, J., Cros, V., Anane, A., Fert, A., et al. (2011). Vertical-current-induced domain-wall motion in MgO-based magnetic tunnel junctions with low current densities. Nat. Phys . 7, 626-630. doi: 10.1038/nphys1968
21. Chua, L. (1971). Memristor-missing circuit element. IEEE Trans. Circuit Theor . 18, 507-519. doi: 10.1109/TCT.1971.1083337
22. Chua, L. (2014). If it's pinched it's a memristor. Semicond. Sci. Technol . 29:104001. doi: 10.1088/0268-1242/29/10/104001
23. Chua, L. O., and Kang, S. M. (1976). Memristive devices and systems. Proc. IEEE 64, 209-223. doi: 10.1109/PROC.1976.10092
24. Devolder, T., Hayakawa, J., Ito, K., Takahashi, H., Ikeda, S., Crozat, P., et al. (2008). Single-shot time-resolved measurements of nanosecond-scale spin-transfer induced switching: stochastic versus deterministic aspects. Phys. Rev. Lett . 100:057206. doi: 10.1103/PhysRevLett.100.057206
25. Diao, Z., Li, Z., Wang, S., Ding, Y., Panchula, A., Chen, E., et al. (2007). Spin-transfer torque switching in magnetic tunnel junctions and spin-transfer torque random access memory. J. Phys. Condens. Matter . 19:165209. doi: 10.1088/0953-8984/19/16/165209
26. Eisenreich, H., Mayr, C., Henker, S., Wickert, M., and Schüffny, R. (2009). A novel ADPLL design using successive approximation frequency control. Microelectron. J . 40, 1613-1622. doi: 10.1016/j.mejo.2008.12.005
27. Eliasmith, C., and Anderson, C. C. H. (2004). Neural Engineering: Computation, Representation, and Dynamics. Neurobiological Systems . Cambridge, MA: MIT Press.
28. Eliasmith, C., Stewart, T. C., Choo, X., Bekolay, T., DeWolf, T., Tang, Y., et al. (2012). A large-scale model of the functioning brain. Science 338, 1202-1205. doi: 10.1126/science.1225266
29. Fantini, A., Sousa, V., Perniola, L., Gourvest, E., Bastien, J. C., Maitrejean, S., et al. (2010). "N-doped GeTe as performance booster for embedded phase-change memories," in International Electron Devices Meeting (San Francisco, CA), 29.1.1-29.1.4. doi: 10.1109/IEDM.2010.5703441
30. Feldman, D. (2000). Timing-based LTP and LTD at vertical inputs to layer II/III pyramidal cells in rat barrel cortex. Neuron 27, 45-56. doi: 10.1016/S0896-6273(00)00008-8
31. Fieres, J., Schemmel, J., and Meier, K. (2008). "Realizing biological spiking network models in a configurable wafer-scale hardware system," in IEEE International Joint Conference Neural Network (Hong Kong), 969-976. doi: 10.1109/IJCNN.2008.4633916
32. Finelli, L. A., Haney, S., Bazhenov, M., Stopfer, M., and Sejnowski, T. J. (2008). Synaptic learning rules and sparse coding in a model sensory system. PLoS Comput. Biol . 4:e1000062. doi: 10.1371/journal.pcbi.1000062
33. Flocke, A., and Noll, T. G. (2007). "Fundamental analysis of resistive nanocrossbars for the use in hybrid nano/cmos-memory," Proceedings of 33rd ESSCIRC . 328-331. doi: 10.1109/ESSCIRC.2007.4430310
34. Froemke, R., and Dan, Y. (2002). Spike-timing-dependent synaptic modification induced by natural spike trains. Nature 416, 433-438. doi: 10.1038/416433a
35. Fukushima, A., Seki, T., Yakushiji, K., Kubota, H., Imamura, H., Yuasa, S., et al. (2014). Spin dice: a scalable truly random number generator based on spintronics. Appl. Phys. Express . 7:083001. doi: 10.7567/APEX.7.083001
36. Garcia, V., Fusil, S., Bouzehouane, K., Enouz-Vedrenne, S., Mathur, N. D., Barthélémy, A., et al. (2009). Giant tunnel electroresistance for non-destructive readout of ferroelectric states. Nature 460, 81-84. doi: 10.1038/nature08128
37. Gerstner, W., Kempter, R., Leo van Hemmen, J., and Wagner, H. (1996). A neuronal learning rule for sub-millisecond temporal coding. Lett. Nat . 383, 76-78. doi: 10.1038/383076a0
38. Gerstner, W., Ritz, R., and Hemmen, J. L. (1993). Why spikes? Hebbian learning and retrieval of time-resolved excitation patterns. Biol. Cybern . 69, 503-515. doi: 10.1007/BF00199450
39. Goldberg, D. H., Cauwenberghs, G., and Andreou, A. G. (2001). Probabilistic synaptic weighting in a reconfigurable network of VLSI integrate-and-fire neurons. Neural Netw . 14, 781-793. doi: 10.1016/S0893-6080(01)00057-0
40. Hebb, D. O. (1949). The Organization of Behavior . New York: Wiley and Sons.
41. Henker, S., Mayr, C., Schlüssler, J.-U., Schüffny, R., Ramacher, U., and Heittmann, A. (2007). "Active pixel sensor arrays in 90/65nm CMOS-technologies with vertically stacked photodiodes," in Proceedings if IEEE International Image Sensor Workshop . 16-19.
42. Indiveri, G., Chicca, E., and Douglas, R. (2006). A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity. IEEE Trans. Neural Netw . 17, 211-221. doi: 10.1109/TNN.2005.860850
43. International Technology Roadmap For Semiconductors (ITRS). (2011). Emerging Research Devices .
44. Jacob, V., Brasier, D. J., Erchova, I., Feldman, D., and Shulz, D. E. (2007). Spike-timing-dependent synaptic depression in the in vivo barrel cortex of the rat. J. Neurosci . 27, 1271-1284. doi: 10.1523/JNEUROSCI.4264-06.2007
45. Jeong, D. S., Kim, I., Ziegler, M., and Kohlstedt, H. (2013). Towards artificial neurons and synapses: a materials point of view. RSC Adv . 3, 3169. doi: 10.1039/c2ra22507g
46. Jo, S. H., Chang, T., Ebong, I., Bhadviya, B. B., Mazumder, P., and Lu, W. (2010). Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett . 10, 1297-1301. doi: 10.1021/nl904092h
47. Josberger, E. E., Deng, Y., Sun, W., Kautz, R., and Rolandi, M. (2014). Two terminal protonic devices with synaptic like short term depression and device memory. Adv. Mater . 26, 4986-4990. doi: 10.1002/adma.201400320
48. Joubert, A., Belhadj, B., Temam, O., and Heliot, R. (2012). "Hardware spiking neurons design: analog or digital?" in International Joint Conference Neural Network (Brisbane, QLD), 1-5. doi: 10.1109/IJCNN.2012.6252600
49. Kavehei, O. (2013). Highly scalable neuromorphic hardware with 1-bit stochastic nano-synapses. IEEE Int. Symp. Circuits Syst . 1648-1651. doi: 10.1109/ISCAS.2014.6865468
50. Kawahara, A., Azuma, R., Ikeda, Y., Kawai, K., Katoh, Y., Tanabe, K., et al. (2012). "An 8Mb multi-layered cross-point ReRAM macro with 443MB/s write throughput," in IEEE International Solid-State Circuits Conference 432-434. doi: 10.1109/ISSCC.2012.6177078
51. Khan, M., Lester, D., Plana, L., Rast, A., Jin, X., Painkras, E., et al. (2008). "Spinnaker: mapping neural networks onto a massively-parallel chip multiprocessor," in IEEE International Joint Conference Neural Network (Hong Kong), 2849-2856. doi: 10.1109/IJCNN.2008.4634199
52. Kornijcuk, V., Kavehei, O., Lim, H., Seok, J. Y., Kim, S. K., Kim, I., et al. (2014). Multiprotocol-induced plasticity in artificial synapses. Nanoscale 6, 15151-15160. doi: 10.1039/c4nr03405h
53. Kuzum, D., Jeyasingh, R. G. D., Lee, B., and Wong, H.-S. P. (2012). Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing. Nanoletters 12, 2179-2186. doi: 10.1021/nl201040y
54. Kuzum, D., Yu, S., and Wong, H. P. (2013). Synaptic electronics: materials, devices and applications. Nanotechnology 24:382001. doi: 10.1088/0957-4484/24/38/382001
55. Lamprecht, R., and Ledoux, J. (2004). Structural plasticity and memory. Nat. Rev. Neurosci . 5, 45-54. doi: 10.1038/nrn1301
56. Lau, C. N., Stewart, D. R., Williams, R. S., and Bockrath, M. (2004). Direct observation of nanoscale switching centers in metal/molecule/metal structures. Nano Lett . 4, 569-572. doi: 10.1021/nl035117a
57. Lee, H. Y., Chen, P. S., Wu, T. Y., Chen, Y. S., Wang, C. C., Tzeng, P. J., et al. (2008). "Low power and high speed bipolar switching with a thin reactive Ti buffer layer in robust HfO2 based RRAM," in Technology Digital IEEE International Electron Devices Meeting (San Francisco, CA), 1-4. doi: 10.1109/IEDM.2008.4796677
58. Lim, H., Kim, I., Kim, J.-S., Hwang, C. S., and Jeong, D. S. (2013). Short term memory of TiO2 based electrochemical capacitors: empirical analysis with adoption of a sliding threshold. Nanotechnology 24:384005. doi: 10.1088/0957-4484/24/38/384005
59. Linares Barranco, B., and Serrano-Gotarredona, T. (2009b). "Exploiting memristance in adaptive asynchronous spiking neuromorphic nanotechnology systems," 9th IEEE Conference Nanotechnology (Genoa), 601-604.
60. Linares-Barranco, B., and Serrano-Gotarredona, T. (2009a). Memristance can explain spike-time-dependent-plasticity in neural synapses. Nat. Proc. NPRE .
61. Linn, E., Rosezin, R., Kügeler, C., and Waser, R. (2010). Complementary resistive switches for passive nanocrossbar memories. Nat. Mater . 9, 403-406. doi: 10.1038/nmat2748
62. 22-layer 32Gb ReRAM memory device in 24nm technology. IEEE Int. Solid-State Circuits Conf . 432-434, 210-212. doi: 10.1109/ISSCC.2013.6487703
63. Livi, P., and Indiveri, G. (2009). A current-mode conductance-based silicon neuron for address-event neuromorphic systems. Int. Symp. Circuits Syst . 2898-2901. doi: 10.1109/ISCAS.2009.5118408
64. Lou, X., Gao, Z., Dimitrov, D. V., and Tang, M. X. (2008). Demonstration of multilevel cell spin transfer switching in MgO magnetic tunnel junctions. Appl. Phys. Lett . 93, 242502. doi: 10.1063/1.3049617
65. Marins de Castro, M., Sousa, R. C., Bandiera, S., Ducruet, C., Chavent, A., Auffret, S., et al. (2012). Precessional spin-transfer switching in a magnetic tunnel junction with a synthetic antiferromagnetic perpendicular polarizer. J. Appl. Phys . 111, 07C912-07C912-3. doi: 10.1063/1.3676610
66. Markram, H., Lübke, J., Frotscher, M., and Sakmann, B. (1997). Regulation of synaptic efficacy by coincidence of postsynaptic APS and EPSPS. Science 275, 213-215. doi: 10.1126/science.275.5297.213
67. Masquelier, T., Guyonneau, R., and Thorpe, S. J. (2008). Spike timing dependent plasticity finds the start of repeating patterns in continuous spike trains. PLoS ONE 3:e1377. doi: 10.1371/journal.pone.0001377
68. Masquelier, T., Guyonneau, R., and Thorpe, S. J. (2009). Competitive STDP-based spike pattern learning. Neural Comp . 21, 1259-1276. doi: 10.1162/neco.2008.06-08-804
69. Mayr, C., Ehrlich, M., Henker, S., Wendt, K., and Schüffny, R. (2007). Mapping complex, large scale spiking networks on neural VLSI. Int. J. Appl. Sci. Eng. Technol . 4, 37-42.
70. Mayr, C., Noack, M., Partzsch, J., and Schüffny, R. (2010). Replicating experimental spike and rate based neural learning in CMOS. IEEE Int. Symp. Circuits Systems . 105-108. doi: 10.1109/ISCAS.2010.5537009
71. Mayr, C., and Partzsch, J. (2010). Rate and pulse based plasticity governed by local synaptic state variables. Front. Synaptic Neurosci . 2:33. doi: 10.3389/fnsyn.2010.00033
72. Mayr, C., Partzsch, J., Noack, M., Hänzsche, S., Scholze, S., Höppner, S., et al. (2014b). A biological real time neuromorphic system in 28nm CMOS using low leakage switched capacitor circuits. Trans. Biomed. Circuits Syst . doi: 10.1109/TBCAS.2014.2379294
73. Mayr, C., Partzsch, J., Noack, M., and Schüffny, R. (2014a). Configurable analog-digital conversion using the neural engineering framework. Front. Neurosci . 8:201. doi: 10.3389/fnins.2014.00201
74. Mayr, C., Stärke, P., Partzsch, J., Cederstroem, L., Schüffny, R., Shuai, Y., et al. (2012). Waveform driven plasticity in BiFeO3 memristive devices: model and implementation. Adv. Neural Inf. Process. Syst . 25, 1700-1708.
75. Merolla, P., Arthur, J., Akopyan, F., Imam, N., Manohar, R., and Modha, D. S. (2011). "A digital neurosynaptic core using embedded crossbar memory with 45pJ per spike in 45 nm," in Custom Integrative Circuits Conference (San Jose, CA), 1-4. doi: 10.1109/CICC.2011.6055294
76. Mu, Y., and Poo, M. M. (2006). Spike timing-dependent LTP/LTD mediates visual experience-dependent plasticity in a developing retinotectal system. Neuron 50, 115-125. doi: 10.1016/j.neuron.2006.03.009
77. Nessler, B., Pfeiffer, M., Buesing, L., and Maass, W. (2013). Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity. PLoS Comput. Biol . 9:e1003037. doi: 10.1371/journal.pcbi.1003037
78. Noack, M., Partzsch, J., Mayr, C., and Schüffny, R. (2010). "Biology-derived synaptic dynamics and optimized system architecture for neuromorphic hardware," in 17th International Conference on Mixed Design of Integrated Circuits and Systems (Warsaw), 219-224.
79. Ohno, T., Hasegawa, T., Tsuruoka, T., Terabe, K., Gimzewski, J. K., and Aono, M. (2011). Short term plasticity and long term potentiation mimicked in single inorganic synapses. Nat. Mater . 10, 591-595. doi: 10.1038/nmat3054
80. Pershin, Y. V., and Di Ventra, M. (2008). Spin memristive systems: spin memory effects in semiconductor spintronics. Phys. Rev. B 78:113309. doi: 10.1103/PhysRevB.78.113309
81. Querlioz, D., Bichler, O., Dollfus, P., and Gamrat, C. (2013). Immunity to device variations in a spiking neural network with memristive nanodevices. IEEE Trans. Nanotechnol . 12, 288-295. doi: 10.1109/TNANO.2013.2250995
82. Querlioz, D., Dollfus, P., Bichler, O., and Gamrat, C. (2011). "Learning with memristive devices: how should we model their behavior?" in 7th IEEE/ACM International Symposium on Nanoscale Architectures (San Diego, CA), 150-156.
83. Rangan, V., Ghosh, A., Aparin, V., and Cauwenberghs, G. (2010). A subthreshold aVLSI implementation of the Izhikevich simple neuron model. Conf. Proc. IEEE Eng. Med. Biol. Soc . 2010, 4164-4167. doi: 10.1109/IEMBS.2010.5627392
84. Shuai, Y., Ou, X., Luo, W., Du, N., Wu, C., Zhang, W., et al. (2013). Nonvolatile multilevel resistive switching in Ar+ irradiated BiFeO3 thin films. IEEE Electron Device Lett . 34, 54-56. doi: 10.1109/LED.2012.2227666
85. Shuai, Y., Zhou, S., Wu, C., Zhang, W., Bürger, D., Slesazeck, S., et al. (2011). Control of rectifying and resistive switching behavior in bifeo3 thin films. Appl. Phys. Express 4, 095802. doi: 10.1143/APEX.4.095802
86. Sjöström, J., and Gerstner, W. (2010). Spike-timing dependent plasticity. Scholarpedia 5:1362. doi: 10.4249/scholarpedia.1362
87. Snider, G. S. (2008). "Spike-timing-dependent learning in memristive nanodevices," in IEEE International Symposium Nano Architecture (Anaheim, CA), 85-92. doi: 10.1109/NANOARCH.2008.4585796
88. Strukov, D. B., Snider, G. S., Stewart, D. R., and Williams, R. S. (2008). The missing memristor found. Nature 453, 80-83. doi: 10.1038/nature06932
89. Suri, M., Querlioz, D., Bichler, O., Palma, G., Vianello, E., Vuillaume, D., et al. (2013). Bio-inspired stochastic computing using binary CBRAM synapses. IEEE Trans. Electron Devices 60, 2402-2409. doi: 10.1109/TED.2013.2263000
90. Upadhyaya, H. M., and Chandra, S. (1995). Polarity dependent memory switching behavior in Ti/Cd Pb S/Ag system. Semicond. Sci. Technol . 10, 332-338. doi: 10.1088/0268-1242/10/3/016
91. Varela, J., Sen, K., Gibson, J., Fost, J., Abbott, L. F., and Nelson, S. B. (1997). A quantitative description of short term plasticity at excitatory synapses in layer 2/3 of rat visual cortex. J. Neurosci . 17, 7926-7940.
92. Vincent, A. F., Locatelli, N., Zhao, W. S., Klein, J.-O., Galdin-Retailleau, S., and Querlioz, D. (2015). Analytical macrospin modeling of the stochastic switching time of spin transfer-torque magnetic tunnel junctions. IEEE Trans. Electron Devices 62, 164-170. doi: 10.1109/TED.2014.2372475
93. Vincent, A., Larroque, J., Zhao, W. S., Ben Romdhane, N., Bichler, O., Gamrat, C., et al. (2014). "Spin-transfer torque magnetic memory as a stochastic memristive synapse," in IEEE International Symposium Circuits System (Melbourne, VIC), 1074-1077. doi: 10.1109/ISCAS.2014.6865325
94. Wang, X., Chen, Y., Xi, H., Li, H., and Dimitrov, D. (2009). Spintronic memristor through spin-torque-induced magnetization motion. IEEE Electron Device Lett . 30, 294-297. doi: 10.1109/LED.2008.2012270
95. Waser, R., and Aono, M. (2007). Nanoionics-based resistive switching memories. Nat. Mater . 6, 833-840. doi: 10.1038/nmat2023
96. Wijekoon, J., and Dudek, P. (2008). Compact silicon neuron circuit with spiking and bursting behavior. Neural Netw . 21, 524-534. doi: 10.1016/j.neunet.2007.12.037
97. Wong, H.-S. P., Lee, H.-Y., Yu, S., Chen, Y.-S., Wu, Y., Chen, P.-S., et al. (2012). Metal oxide RRAM. Proc. IEEE 100, 1951-1970. doi: 10.1109/JPROC.2012.2190369
98. Wu, X., Zhou, P., Li, J., Chen, L. Y., Lu, H. B., Lin, Y. Y., et al. (2007). Reproducible unipolar resistance switching in stoichiometric ZrO2 films. Appl. Phys. Lett . 90, 183507. doi: 10.1063/1.2734900
99. Yamada, H., Garcia, V., Fusil, S., Boyn, S., Marinova, M., Gloter, A., et al. (2013). Giant electroresistance of super-tetragonal BiFeO3-based ferroelectric tunnel junctions. ACS Nano . 7, 5385-5390. doi: 10.1021/nn401378t
100. Yang, R., Terabe, K., Yao, Y., Tsuruoka, T., Hasegawa, T., Gimzewski, J. K., et al. (2013). Synaptic plasticity and memory functions achieved in a WO3-x based nanoionics device by using the principle of atomic switch operation. Nanotechnology 24, 384003. doi: 10.1088/0957-4484/24/38/384003
101. 3memristors. ACS Appl Mater Interfaces 6, 19758-19765. doi: 10.1021/am504871g
102. Young, J. M. (2007). Cortical reorganization consistent with spike timing-but not correlation-dependent plasticity. Nat. Neurosci . 10, 887-895. doi: 10.1038/nn1913
103. Yu, S., Gao, B., Fang, Z., Yu, H., Kang, J., and Wong, H.-S. P. (2013). Stochastic learning in oxide binary synaptic device for neuromorphic computing. Front. Neurosci . 7:186. doi: 10.3389/fnins.2013.00186
104. Zamarreño-Ramos, C., Camuñas-Mesa, L. A., Pérez-Carrasco, J. A., Masquelier, T., Serrano-Gotarredona, T., and Linares-Barranco, B. (2011). On spike-timing-dependentplasticity, memristive devices, and building a self-learning visual cortex. Front. Neurosci . 5:26. doi: 10.3389/fnins.2011.00026
105. Zhang, L., Tao, H., Holt, C., Harris, W., and Poo, M. (1998). A critical window for cooperation and competition among developing retinotectal synapses. Nature 395, 37-44. doi: 10.1038/25665
106. Zhang, Y., Zhao, W., Klein, J.-O., Kang, W., Querlioz, D., Zhang, Y., et al. (2014). "Spintronics for low-power computing," in Design, Automation and Test in Europe Conference and Exhibition (Dresden), 1-6. doi: 10.7873/DATE.2014.316