Skip to main content

Associative Enhancement and Its Application in Memristor Based Neuromorphic Devices

Abstract

Binary state resistance switching memristors and multistate or continuum resistance memristors have begun to diverge in their application. The former application in non-volatile logic memory and the later with more focus on neuromorphic computation. The properties of continuum resistance memristors as neuromorphic hardware are presented in this paper. Concepts such as polymorphism, voltage flux equivalence and non voltage based memristive flux are discussed. Emergent functionalities observed in neuromorphic hardware such as associative enhancement of current/charge generation, spike time dependent plasticity (STDP) and associative memory between voltage and light pulse stimuli are expanded upon. Finally various applications that this hardware may enable such as machine learning and adaptive programs for brain-like functionality are discussed.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-76375-0_19
  • Chapter length: 16 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   309.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-76375-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book
USD   399.99
Price excludes VAT (USA)
Fig. 1

Reprinted (adapted) with permission from O’Kelly et al. [17]. Copyright 2016 American Chemical Society

Fig. 2

Reprinted (adapted) with permission from O’Kelly et al. [17]. Copyright 2016 American Chemical Society

Fig. 3

Reprinted (adapted) with permission from O’Kelly et al. [17]. Copyright 2016 American Chemical Society

Fig. 4

Reprinted (adapted) with permission from O’Kelly et al. [22]. Copyright 2016 Advanced Electronic Materials

Fig. 5

Reprinted (adapted) with permission from O’Kelly et al. [30]. Copyright 2014 American Chemical Society

Fig. 6

Reprinted (adapted) with permission from O’Kelly et al. [22]. Copyright 2016 Advanced Electronic Materials

Fig. 7

Reprinted (adapted) with permission from O’Kelly et al. [22]. Copyright 2016 Advanced Electronic Materials

References

  1. Markram, H., Gerstner, W., Sjöström, P.J.: Spike-timing-dependent plasticity: a comprehensive overview. Front. Synaptic Neurosci. 4, 2 (2012)

    Google Scholar 

  2. Linares-Barranco, B., Serrano-Gotarredona, T., Camuñas-Mesa, L.A., Perez-Carrasco, J.A., Zamarreño-Ramos, C., Masquelier, T.: On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortex. Front. Neurosci. 5, 26 (2011)

    Google Scholar 

  3. Lodish, H., Berk, A., Kaiser, C.A., Krieger, M., Scott, M.P., Bretscher, A., Ploegh, H., Matsudaira, P.: Mol. Cell Biol., 7th edn. Freeman, W. H (2012)

    Google Scholar 

  4. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)

    CrossRef  Google Scholar 

  5. Huang, G., Huang, G.-B., Song, S., You, K.: Trends in extreme learning machines: a review. Neural Netw. 61, 32–48 (2015)

    CrossRef  Google Scholar 

  6. Djurfeldt, M., Lundqvist, M., Johansson, C., Rehn, M., Ekeberg, O., Lansner, A.: Brain-scale simulation of the neocortex on the IBM Blue Gene/L supercomputer. IBM J. Res. Dev. 52, 31–41 (2008)

    CrossRef  Google Scholar 

  7. Merolla, P.A., Arthur, J.V., Alvarez-Icaza, R., Cassidy, A.S., Sawada, J., Akopyan, F., Jackson, B.L., Imam, N., Guo, C., Nakamura, Y., Brezzo, B., Vo, I., Esser, S.K., Appuswamy, R., Taba, B., Amir, A., Flickner, M.D., Risk, W.P., Manohar, R., Modha, D.S.: A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345, 668–673 (2014)

    CrossRef  Google Scholar 

  8. Chua, L.: Memristor-the missing circuit element. IEEE Trans. Circ. Theory 18, 507–519 (1971)

    CrossRef  Google Scholar 

  9. Strukov, D.B., Snider, G.S., Stewart, D.R., Williams, R.S.: The missing memristor found. Nature 453, 80–83 (2008)

    CrossRef  Google Scholar 

  10. Pershin, Y.V., Castelano, L.K., Hartmann, F., Lopez-Richard, V., Ventra, M.D.: A memristive pascaline. IEEE Trans. Circuits Syst. II Express Briefs 63, 558–562 (2016)

    CrossRef  Google Scholar 

  11. Traversa, F.L., Ventra, M.D.: Universal memcomputing machines. IEEE Trans. Neural Netw. Learn. Syst. 26, 2702–2715 (2015)

    MathSciNet  CrossRef  Google Scholar 

  12. Pershin, Y.V., Shevchenko, S.N.: Computing with volatile memristors: an application of non-pinched hysteresis. Nanotechnology 28, 075204 (2017)

    CrossRef  Google Scholar 

  13. 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, 591–595 (2011)

    CrossRef  Google Scholar 

  14. van de Burgt, Y., Lubberman, E., Fuller, E.J., Keene, S.T., Faria, G.C., Agarwal, S., Marinella, M.J., Alec Talin, A., Salleo, A.: A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing. Nat. Mater. 16, 414–418 (2017)

    CrossRef  Google Scholar 

  15. Caponi, S., Mattana, S., Ricci, M., Sagini, K., Juarez-Hernandez, L.J., Jimenez-Garduño, A.M., Cornella, N., Pasquardini, L., Urbanelli, L., Sassi, P., Morresi, A., Emiliani, C., Fioretto, D., Serra, M.D., Pederzolli, C., Iannotta, S., Macchi, P., Musio, C.: A multidisciplinary approach to study the functional properties of neuron-like cell models constituting a living bio-hybrid system: SH-SY5Y cells adhering to PANI substrate. AIP Adv. 6, 111303 (2016)

    CrossRef  Google Scholar 

  16. Gkoupidenis, P., Schaefer, N., Garlan, B., Malliaras, G.G.: Neuromorphic functions in PEDOT: PSS organic electrochemical transistors. Adv. Mater. 27, 7176–7180 (2015)

    CrossRef  Google Scholar 

  17. O’Kelly, C.J., Abunahla, H.N.M., Abi Jaoude, M., Homouz, D., Mohammad, B.: Subthreshold continuum conductance change in NbO Pt memristor interfaces. J. Phys. Chem. C 120, 18971–18976 (2016)

    CrossRef  Google Scholar 

  18. Chua, L.: Resistance switching memories are memristors. Appl. Phys. A 102, 765–783 (2011)

    CrossRef  Google Scholar 

  19. Pickett, M.D., Medeiros-Ribeiro, G., Williams, R.S.: A scalable neuristor built with Mott memristors. Nat. Mater. 12, 114–117 (2013)

    CrossRef  Google Scholar 

  20. Chiolerio, A., Chiappalone, M., Ariano, P., Bocchini, S.: Coupling resistive switching devices with neurons: state of the art and perspectives. Front. Neurosc. 11 (2017)

    Google Scholar 

  21. Sze, S.M., Ng, K.K.: Physics of semiconductor Devices. John wiley & sons (2006)

    Google Scholar 

  22. O’Kelly, C.J., Fairfield, J.A., McCloskey, D., Manning, H.G., Donegan, J.F., Boland, J.J.: Associative enhancement of time correlated response to heterogeneous stimuli in a neuromorphic nanowire device. Adv. Electr. Mater. 2, 1500458 (2016)

    CrossRef  Google Scholar 

  23. Henderson, M.A.: A surface science perspective on TiO2 photocatalysis. Surf. Sci. Rep. 66, 185–297 (2011)

    CrossRef  Google Scholar 

  24. Liu, G., Hoivik, N., Wang, X., Lu, S., Wang, K., Jakobsen, H.: Photoconductive, free-standing crystallized TiO2 nanotube membranes. Electrochim. Acta 93, 80–86 (2013)

    CrossRef  Google Scholar 

  25. Pavlov, I.P.: Conditional reflexes: an investigation of the physiological activity of the cerebral cortex. Oxford University Press, New York (1927)

    Google Scholar 

  26. Feldman, D.E.: The spike-timing dependence of plasticity. Neuron 75, 556–571 (2012)

    CrossRef  Google Scholar 

  27. Hartley, C.A., Phelps, E.A.: Fear models in animals and humans. In: Vasa, R.A., Roy, A.K. (eds.) Pediatric Anxiety Disorders: A Clinical Guide, pp. 3–21. Springer, New York, NY (2013)

    CrossRef  Google Scholar 

  28. Pershin, Y.V., Di Ventra, M.: Experimental demonstration of associative memory with memristive neural networks. Neural Netw. 23, 881–886 (2010)

    CrossRef  Google Scholar 

  29. Zidan, M.A., Fahmy, H.A.H., Hussain, M.M., Salama, K.N.: Memristor-based memory: the sneak paths problem and solutions. Microelectron. J. 44, 176–183 (2013)

    CrossRef  Google Scholar 

  30. O’Kelly, C., Fairfield, J.A., Boland, J.J.: A single nanoscale junction with programmable multilevel memory. ACS Nano 8, 11724–11729 (2014)

    CrossRef  Google Scholar 

  31. Janotti, A., Varley, J.B., Rinke, P., Umezawa, N., Kresse, G., Van de Walle, C.G.: Hybrid functional studies of the oxygen vacancy in TiO 2. Phys. Rev. B 81 (2010)

    Google Scholar 

  32. Jeong, D.S., Schroeder, H., Breuer, U., Waser, R.: Characteristic electroforming behavior in Pt/TiO 2/Pt resistive switching cells depending on atmosphere. J. Appl. Phys. 104, 123716 (2008)

    CrossRef  Google Scholar 

  33. Kwon, D.H., Kim, K.M., Jang, J.H., Jeon, J.M., Lee, M.H., Kim, G.H., Li, X.S., Park, G.S., Lee, B., Han, S., Kim, M., Hwang, C.S.: Atomic structure of conducting nanofilaments in TiO 2 resistive switching memory. Nat. Nanotechnol. 5, 148–153 (2010)

    CrossRef  Google Scholar 

  34. Jeong, D.S., Schroeder, H., Waser, R.: Mechanism for bipolar switching in a Pt/TiO 2/Pt resistive switching cell. Phys. Rev. B 79, 195317 (2009)

    CrossRef  Google Scholar 

  35. Roose, B., Pathak, S., Steiner, U.: Doping of TiO 2 for sensitized solar cells. Chem. Soc. Rev. 44, 8326–8349 (2015)

    CrossRef  Google Scholar 

  36. Szczepankiewicz, S.H., Colussi, A.J., Hoffmann, M.R.: Infrared spectra of photoinduced species on hydroxylated titania surfaces. J. Phys. Chem. B 9842–9850 (2000)

    CrossRef  Google Scholar 

  37. Berger, T., Sterrer, M., Diwald, O., Knözinger, E., Panayotov, D., Thompson, T.L., Yates, J.T.: Light-induced charge separation in anatase TiO2 particles. J. Phys. Chem. B 109, 6061–6068 (2005)

    CrossRef  Google Scholar 

  38. Zou, J., Zhang, Q., Huang, K., Marzari, N.: Ultraviolet photodetectors based on anodic TiO2 nanotube arrays. J. Phys. Chem. C 114, 10725–10729 (2010)

    CrossRef  Google Scholar 

  39. Rasmussen, M.D., Molina, L.M., Hammer, B.: Adsorption, diffusion, and dissociation of molecular oxygen at defected TiO 2 (110): A density functional theory study. J. Chem. Phys. 120, 988–997 (2004)

    CrossRef  Google Scholar 

  40. Xu, C., Niu, D., Muralimanohar, N., Balasubramonian, R., Zhang, T., Yu, S., Xie, Y.: Overcoming the challenges of crossbar resistive memory architectures. In: 2015 IEEE 21st International Symposium on High Performance Computer Architecture (HPCA), 7–11 Feb. 2015, pp. 476–488 (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Curtis J. O’Kelly .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Verify currency and authenticity via CrossMark

Cite this chapter

O’Kelly, C.J. (2019). Associative Enhancement and Its Application in Memristor Based Neuromorphic Devices. In: Chua, L., Sirakoulis, G., Adamatzky, A. (eds) Handbook of Memristor Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-76375-0_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-76375-0_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76374-3

  • Online ISBN: 978-3-319-76375-0

  • eBook Packages: Computer ScienceComputer Science (R0)