Synaptic Plasticity with Memristive Nanodevices

  • Selina La Barbera
  • Fabien Alibart
Part of the Cognitive Systems Monographs book series (COSMOS, volume 31)


This chapter provides a comprehensive overview of current research on nanoscale memory devices suitable to implement some aspect of synaptic plasticity. Without being exhaustive on the different forms of plasticity that could be realized, we propose an overall classification and analysis of few of them, which can be the basis for going into the field of neuromorphic computing. More precisely, we present how nanoscale memory devices, implemented in a spike-based context, can be used for synaptic plasticity functions such as spike rate-dependent plasticity, spike timing-dependent plasticity, short-term plasticity, and long-term plasticity.


Synaptic Plasticity Synaptic Weight Hebbian Learning Memristive Device Causal Description 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors thank Dr. Dominique Vuillaume for careful reading of the manuscript and Dr. Damien Querlioz for fruitful discussions. This work was supported by ANR-12-PDOC- 0027-01 (Grant DINAMO).


  1. 1.
    Abbott, L., Varela, J., Sen, K., Nelson, S.: Synaptic depression and cortical gain control. Science 275(5297), 221–224 (1997)CrossRefGoogle Scholar
  2. 2.
    Abbott, L.F., Nelson, S.B.: Synaptic plasticity: taming the beast. Nat. Neurosci. 3, 1178–1183 (2000)CrossRefGoogle Scholar
  3. 3.
    Alibart, F., Pleutin, S., Guérin, D., Novembre, C., Lenfant, S., Lmimouni, K., Gamrat, C., Vuillaume, D.: An organic nanoparticle transistor behaving as a biological spiking synapse. Adv. Funct. Mater. 20(2), 330–337 (2010)CrossRefGoogle Scholar
  4. 4.
    Bi, G.-Q., Poo, M.-M.: Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J. Neurosci. 18(24), 10464–10472 (1998)Google Scholar
  5. 5.
    Bienenstock, E.L., Cooper, L.N., Munro, P.W.: Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex. J. Neurosci. 2(1), 32–48 (1982)Google Scholar
  6. 6.
    Bliss, T.V., Collingridge, G.L., et al.: A synaptic model of memory: long-term potentiation in the hippocampus. Nature 361(6407), 31–39 (1993)CrossRefGoogle Scholar
  7. 7.
    Boegerhausen, M., Suter, P., Liu, S.-C.: Modeling short-term synaptic depression in silicon. Neural Comput. 15(2), 331–348 (2003)CrossRefMATHGoogle Scholar
  8. 8.
    Boyden, E.S., Katoh, A., Raymond, J.L.: Cerebellum-dependent learning: the role of multiple plasticity mechanisms. Neuroscience 27 (2004)Google Scholar
  9. 9.
    Buonomano, D.V., Karmarkar, U.R.: Book review: how do we tell time? Neurosc. 8(1), 42–51 (2002)Google Scholar
  10. 10.
    Buonomano, D.V., Maass, W.: State-dependent computations: spatiotemporal processing in cortical networks. Nat. Rev. Neurosci. 10(2), 113–125 (2009)CrossRefGoogle Scholar
  11. 11.
    Cantley, K.D., Subramaniam, A., Stiegler, H.J., Chapman, R., Vogel, E.M., et al.: Hebbian learning in spiking neural networks with nanocrystalline silicon tfts and memristive synapses. IEEE Trans. Nanotechnol. 10(5), 1066–1073 (2011)CrossRefGoogle Scholar
  12. 12.
    Chang, S.H., Lee, S.B., Jeon, D.Y., Park, S.J., Kim, G.T., Yang, S.M., Chae, S.C., Yoo, H.K., Kang, B.S., Lee, M.-J., et al.: Oxide double-layer nanocrossbar for ultrahigh-density bipolar resistive memory. Adv. Mater. 23(35), 4063–4067 (2011a)CrossRefGoogle Scholar
  13. 13.
    Chang, T., Jo, S.-H., Kim, K.-H., Sheridan, P., Gaba, S., Lu, W.: Synaptic behaviors and modeling of a metal oxide memristive device. Appl. Phys. A 102(4), 857–863 (2011b)CrossRefGoogle Scholar
  14. 14.
    Clopath, C., Büsing, L., Vasilaki, E., Gerstner, W.: Connectivity reflects coding: a model of voltage-based stdp with homeostasis. Nat. Neurosci. 13(3), 344–352 (2010)CrossRefGoogle Scholar
  15. 15.
    Deng, Y., Josberger, E., Jin, J., Rousdari, A.F., Helms, B.A., Zhong, C., Anantram, M., Rolandi, M.: H+-type and oh–type biological protonic semiconductors and complementary devices. Sci. Rep. 3 (2013)Google Scholar
  16. 16.
    Desbief, S., Kyndiah, A., Guerin, D., Gentili, D., Murgia, M., Lenfant, S., Alibart, F., Cramer, T., Biscarini, F., Vuillaume, D.: Low voltage and time constant organic synapse-transistor. Org. Electron. 21, 47–53 (2015)CrossRefGoogle Scholar
  17. 17.
    Du, C., Ma, W., Chang, T., Sheridan, P., Lu, W.D.: Biorealistic implementation of synaptic functions with oxide memristors through internal ionic dynamics. Adv. Funct. Mater. 25(27), 4290–4299 (2015)CrossRefGoogle Scholar
  18. 18.
    Gjorgjieva, J., Clopath, C., Audet, J., Pfister, J.-P.: A triplet spike-timing-dependent plasticity model generalizes the bienenstock-cooper-munro rule to higher-order spatiotemporal correlations. Proc. Natl. Acad. Sci. 108(48), 19383–19388 (2011)CrossRefGoogle Scholar
  19. 19.
    Hebb, D.O.: The first stage of perception: growth of the assembly. Org. Behav. 60–78 (1949)Google Scholar
  20. 20.
    Izhikevich, E.M., et al.: Simple model of spiking neurons. IEEE Trans. Neural Netw. 14(6), 1569–1572 (2003)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Kim, S., Du, C., Sheridan, P., Ma, W., Choi, S., Lu, W.D.: Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity. Nano Lett. 15(3), 2203–2211 (2015)CrossRefGoogle Scholar
  22. 22.
    Kuzum, D., Jeyasingh, R.G., Lee, B., Wong, H.-S.P.: Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing. Nano Lett. 12(5), 2179–2186 (2011)CrossRefGoogle Scholar
  23. 23.
    La Barbera, S., Vuillaume, D., Alibart, F.: Filamentary switching: synaptic plasticity through device volatility. ACS Nano 9(1), 941–949 (2015)CrossRefGoogle Scholar
  24. 24.
    Lamprecht, R., LeDoux, J.: Structural plasticity and memory. Nat. Rev. Neurosci. 5(1), 45–54 (2004)CrossRefGoogle Scholar
  25. 25.
    Lim, J., Ryu, S.Y., Kim, J., Jun, Y.: A study of tio2/carbon black composition as counter electrode materials for dye-sensitized solar cells. Nanoscale Res. Lett. 8(1), 1–5 (2013)CrossRefGoogle Scholar
  26. 26.
    Maass, W., Natschläger, T.: Networks of spiking neurons can emulate arbitrary hopfield nets in temporal coding. Netw. Comput. Neural Syst. 8(4), 355–371 (1997)CrossRefMATHGoogle Scholar
  27. 27.
    Malenka, R.C., Bear, M.F.: Ltp and ltd: an embarrassment of riches. Neuron 44(1), 5–21 (2004)CrossRefGoogle Scholar
  28. 28.
    Markram, H., Lübke, J., Frotscher, M., Sakmann, B.: Regulation of synaptic efficacy by coincidence of postsynaptic aps and epsps. Science 275(5297), 213–215 (1997)CrossRefGoogle Scholar
  29. 29.
    Markram, H., Pikus, D., Gupta, A., Tsodyks, M.: Potential for multiple mechanisms, phenomena and algorithms for synaptic plasticity at single synapses. Neuropharmacology 37(4), 489–500 (1998)CrossRefGoogle Scholar
  30. 30.
    Mayr, C., Partzsch, J., Noack, M., Schüffny, R.: Live demonstration: multiple-timescale plasticity in a neuromorphic system. In: ISCAS, pp. 666–670 (2013)Google Scholar
  31. 31.
    Mayr, C., Stärke, P., Partzsch, J., Cederstroem, L., Schüffny, R., Shuai, Y., Du, N., Schmidt, H.: Waveform driven plasticity in bifeo3 memristive devices: model and implementation. In: Advances in Neural Information Processing Systems, pp. 1700–1708 (2012)Google Scholar
  32. 32.
    McCulloch, W.S., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5(4), 115–133 (1943)MathSciNetCrossRefMATHGoogle Scholar
  33. 33.
    Ohno, T., Hasegawa, T., Tsuruoka, T., Terabe, K., Gimzewski, J.K., Aono, M.: Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. Nat. Mater. 10(8), 591–595 (2011)CrossRefGoogle Scholar
  34. 34.
    Senn, W., Markram, H., Tsodyks, M.: An algorithm for modifying neurotransmitter release probability based on pre-and postsynaptic spike timing. Neural Comput. 13(1), 35–67 (2001)CrossRefMATHGoogle Scholar
  35. 35.
    Sjöström, P.J., Turrigiano, G.G., Nelson, S.B.: Rate, timing, and cooperativity jointly determine cortical synaptic plasticity. Neuron 32(6), 1149–1164 (2001)CrossRefGoogle Scholar
  36. 36.
    Snider, G.S.: Spike-timing-dependent learning in memristive nanodevices. In: IEEE International Symposium on Nanoscale Architectures, 2008. NANOARCH 2008, pp. 85–92. IEEE (2008)Google Scholar
  37. 37.
    Sourdet, V., Debanne, D.: The role of dendritic filtering in associative long-term synaptic plasticity. Learn. Mem. 6(5), 422–447 (1999)CrossRefGoogle Scholar
  38. 38.
    Strukov, D.B., Snider, G.S., Stewart, D.R., Williams, R.S.: The missing memristor found. Nature 453(7191), 80–83 (2008)CrossRefGoogle Scholar
  39. 39.
    Subramaniam, A., Cantley, K.D., Bersuker, G., Gilmer, D., Vogel, E.M.: Spike-timing-dependent plasticity using biologically realistic action potentials and low-temperature materials. IEEE Trans. Nanotechnol. 12(3), 450–459 (2013)CrossRefGoogle Scholar
  40. 40.
    Van Rossum, M.C., Bi, G.Q., Turrigiano, G.G.: Stable hebbian learning from spike timing-dependent plasticity. J. Neurosci. 20(23), 8812–8821 (2000)Google Scholar
  41. 41.
    Varela, J.A., Sen, K., Gibson, J., Fost, J., Abbott, L., Nelson, S.B.: A quantitative description of short-term plasticity at excitatory synapses in layer 2/3 of rat primary visual cortex. J. Neurosci. 17(20), 7926–7940 (1997)Google Scholar
  42. 42.
    Wang, Z.Q., Xu, H.Y., Li, X.H., Yu, H., Liu, Y.C., Zhu, X.J.: Synaptic learning and memory functions achieved using oxygen ion migration/diffusion in an amorphous ingazno memristor. Adv. Funct. Mater. 22(13), 2759–2765 (2012)CrossRefGoogle Scholar
  43. 43.
    Williamson, A., Schumann, L., Hiller, L., Klefenz, F., Hoerselmann, I., Husar, P., Schober, A.: Synaptic behavior and stdp of asymmetric nanoscale memristors in biohybrid systems. Nanoscale 5(16), 7297–7303 (2013)CrossRefGoogle Scholar
  44. 44.
    Yang, Y., Choi, S., Lu, W.: Oxide heterostructure resistive memory. Nano Lett. 13(6), 2908–2915 (2013)CrossRefGoogle Scholar
  45. 45.
    Yuan, P., Leonetti, M.D., Pico, A.R., Hsiung, Y., MacKinnon, R.: Structure of the human bk channel ca2+-activation apparatus at 3.0 å resolution. Science 329(5988), 182–186 (2010)CrossRefGoogle Scholar
  46. 46.
    Zenke, F., Agnes, E.J., Gerstner, W.: Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks. Nat. Commun. 6 (2015)Google Scholar
  47. 47.
    Ziegler, L., Zenke, F., Kastner, D.B., Gerstner, W.: Synaptic consolidation: from synapses to behavioral modeling. J. Neurosci. 35(3), 1319–1334 (2015)CrossRefGoogle Scholar

Copyright information

© Springer (India) Pvt. Ltd. 2017

Authors and Affiliations

  1. 1.Institut d’ElectroniqueMicroelectronique et NanotechnologiesVilleneuve-d’AscqFrance

Personalised recommendations