Memristors as Synapses in Artificial Neural Networks: Biomimicry Beyond Weight Change

  • Andrew J. Lohn
  • Patrick R. Mickel
  • James B. Aimone
  • Erik P. Debenedictis
  • Matthew J. Marinella
Part of the Advances in Information Security book series (ADIS, volume 61)


Cyberthreat security is a rapidly evolving landscape, where the diversity and number of attacks is constantly changing, requiring new approaches to defense. In the past, it was sufficient to predict likely attack methods and to monitor potential vulnerabilities, however, today the attacks are too varied and change too quickly for the traditional defenses to be effective. We need structures that are capable of identifying probable attacks and responding without human intervention. Due to the increasing rate of new attack methodologies, these structures need to be able to identify and respond to attacks that have never been seen before. That is, we need structures which are capable of learning.


Titanium Depression Nitrides Expense Tantalum 


  1. 1.
    W.S. Mcculloch, W. Pitts, “A logical calculus of the ideas immanent in nervous activity,” Bulletin of Mathematical Biophysics, 5, 115-133 (1943).MathSciNetCrossRefMATHGoogle Scholar
  2. 2.
    R. Kozma, R.E. Pino, G.E. Pazienza, “Advances in Neuromorphic Memristor Science and Applications,” Springer (2012).Google Scholar
  3. 3.
    L.O. Chua, “Memristor – the missing circuit element,” IEEE Transactions on Circuit Theory, 18, 507-519 (1971).CrossRefGoogle Scholar
  4. 4.
    L.O. Chua, S.M. Kang, “Memristive Devices and Systems,” Proceedings of the IEEE, 64, 209-223 (1976).MathSciNetCrossRefGoogle Scholar
  5. 5.
    T. Prodromakis, C. Toumazou, L. Chua, “Two centuries of memristors,” Nature Materials, 11, 478-481 (2012).CrossRefGoogle Scholar
  6. 6.
    D.B. Strukov, G.S. Snider, D.R. Stewart, R.S. Williams, “The missing memristor found,” Nature, 453, 80-83 (2008).CrossRefGoogle Scholar
  7. 7.
    C. Cagli, D. Ielmini, F. Nardi, A.L. Lacaita, “Evidence for threshold switching in the set process of NiO-based RRAM and physical modeling for set, reset, retention and disturb prediction,” IEEE International Electron Devices Meeting, p 1-4 (2008).Google Scholar
  8. 8.
    S. Murali, J.S. Rajachidambaram, S.-Y. Han, C.-H. Chang, G.S. Herman, J.F. Conley Jr, “Resistive switching in zinc-tin-oxide,” Solid-State Electronics, 79, 248-252 (2013).CrossRefGoogle Scholar
  9. 9.
    M.D. Pickett, D.B. Strukov, J.L. Borghetti, J.J. Yang, G.S. Snider, D.R. Stewart, R.S. Williams, “Switching dynamics in titanium dioxide memristive devices,” Journal of Applied Physics, 106, 074508 (2009).CrossRefGoogle Scholar
  10. 10.
    B.J. Choi, J.J. Yang, M.-X. Zhang, K.J. Norris, D.A.A. Ohlberg, N.P. Kobayashi, G. Medeiros-Ribeiro, R.S. Williams, “Nitride memristors,” Applied Physics A, 109, 1-4 (2012).CrossRefGoogle Scholar
  11. 11.
    M.J. Marinella, J.E. Stevens, E.M. Longoria, P.G. Kotula, “Resistive switching in aluminum nitride,” Device Research Conference, 89-90 (2012).Google Scholar
  12. 12.
    M. Mitkova, M.N. Kozicki, “Silver incorporation in Ge-Se glasses used in programmable metallization cell devices,” Journal of Non-Crystalline Solids, 299-302, 1023-1027 (2002).Google Scholar
  13. 13.
    M.N. Kozicki, M. Balakrishnan, C. Gopalan, C. Ratnakumar, M. Mitkova, “Programmable Metallization Cell Memory Based on Ag-Ge-S and Cu-Ge-S Solid Electrolytes,” Non-Volatile Memory Technology Symposium, 83-89 (2005).Google Scholar
  14. 14.
    R. Waser, M. Aono, “Nanoionics-based resistive switching memories,” Nature Materials, 6, 833-840 (2007).CrossRefGoogle Scholar
  15. 15.
    L.O. Chua, “Resistance switching memories are memristors,” Applied Physics A, 102, 765-783 (2011).CrossRefGoogle Scholar
  16. 16.
    J.J. Yang, D.B. Strukov, D.R. Stewart, “Memristive devices for computing,” Nature Nanotechnology, 8, 13-24 (2013).CrossRefGoogle Scholar
  17. 17.
    H.S.P. Wong, H.-Y. Lee, S. Yu, Y.-S. Chen, Y. Wu, P.-S. Chen, B. Lee, F.T. Chen, M.-J. Tsai, “Metal-Oxide RRAM,” Proceedings of the IEEE, 100, 1951-1970 (2012).CrossRefGoogle Scholar
  18. 18.
    J. Hutchby, M. Garner, “Assessment of the Potential & Maturity of Selected Emerging Research Memory Technologies Workshop & ERD/ERM Working Group Meeting,” (2010).Google Scholar
  19. 19.
    P.R. Mickel, A.J. Lohn, B.J. Choi, J.J. Yang, M.-X. Zhang, M.J. Marinella, C.D. James, R.S. Williams, “A physical model of switching dynamics in tantalum oxide memristive devices,” Applied Physics Letters, 102, 223502 (2013).CrossRefGoogle Scholar
  20. 20.
    J.P. Strachan, A.C. Torrezan, F. Miao, M.D. Pickett, J.J. Yang, W. Yi, G. Medeiros-Ribeiro, R.S. Williams, “State Dynamics and Modeling of Tantalum Oxide Memristors,” IEEE Transactions on Electron Devices, 60, 2194-2202 (2013).CrossRefGoogle Scholar
  21. 21.
    A.J. Lohn, P.R. Mickel, M.J. Marinella, “Dynamics of percolative breakdown mechanism in tantalum oxide resistive switching,” Applied Physics Letters, 103, 173503 (2013).CrossRefGoogle Scholar
  22. 22.
    H.Y. Lee, Y.S. Chen, P.S. Chen, T.Y. Wu, F. Chen, C.C. Wang, P.J. Tzeng, M.-J. Tsai, C. Lien, “Low-Power and Nanosecond Switching in Robust Hafnium Oxide Resistive Memory With a Thin Ti Cap, IEEE Electron Device Letters, 31, 44-46 (2010).CrossRefGoogle Scholar
  23. 23.
    S. Lee, W.-G. Kim, S.-W. Rhee, K. Yong, “Resistance Switching Behaviors of Hafnium Oxide Films Grown by MOCVD for Nonvolatile Memory Applications,” Journal of the Electrochemical Society, 155, H92-H96 (2008).CrossRefGoogle Scholar
  24. 24.
    D.O. Hebb, “The Organization of Behvior: A Neuropsychological Theory,” Wiley, (1949).Google Scholar
  25. 25.
    I.E. Ebong, P. Mazumder, “CMOS and Memristor-Based Neural Network Design for Position Detection,” Proceedings of the IEEE, 100, 2050-2060 (2012).CrossRefGoogle Scholar
  26. 26.
    A. Thomas, “Memristor-based neural networks,” Journal of Physics D: Applied Physics, 46, 093001 (2013).CrossRefGoogle Scholar
  27. 27.
    N. Brunel, V. Hakim, “Fast Global Oscillations in Networks of Integrate-and-Fire Neurons with low Firing Rates,” Neural Computation, 11, 1621-1671 (1999).CrossRefGoogle Scholar
  28. 28.
    A.L. Hodgkin, A.F. Huxley, “A quantitative description of membrane current and its application to conduction and excitation in nerve,” The Journal of Physiology 117 (4) 500-544 (1952).Google Scholar
  29. 29.
    M.D. Pickett, G. Medeiros-Ribeiro, R.S. Williams, “A scalable neuristor built with Mott memristors,” Nature Materials, 12, 114-117 (2012).CrossRefGoogle Scholar
  30. 30.
    C. Clopath, W. Gerstner, “Voltage and spike timing interact in STDP – a unified model,” Frontiers in Synaptic Neuroscience, 2, 1-11 (2010).Google Scholar
  31. 31.
    N. Caporale, Y. Dan, “Spike Timing-Dependent Plasticity: A Hebbian Learning Rule,” Annual Review of Neuroscience, 31, 25-46 (2008).CrossRefGoogle Scholar
  32. 32.
    D.E. Feldman, “The Spike-Timing Dependence of Plasticity,” Neuron, 75, 556-571 (2012).CrossRefGoogle Scholar
  33. 33.
    S. Ambrogio, S. Balatti, F. Nardi, S. Facchinetti, D. Ielmini, “Spike-timing dependent plasticity in a transistor-selected resistive switching memory,” Nanotechnology, 24, 384012 (2013).CrossRefGoogle Scholar
  34. 34.
    F. Miao, J.P. Strachan, J.J. Yang, M.-X. Zhang, I. Goldfarb, A.C. Torrezan, P. Eschbach, R.D. Kelley, G. Medeiros-Ribeiro, R.S. Williams, “Anatomy of a Nanoscale Conduction Channel Reveals the Mechanism of a High-Performance Memristor,” Advanced Materials, 23, 5633-5640 (2011).CrossRefGoogle Scholar
  35. 35.
    G.-S. Park, Y.B. Kim, S.Y. Park, X.S. Li, S. heo, M.-J. Lee, M. Chang, J.H. Kwon, M. Kim, U.-I. Chung, R. Dittmann, R. Waser, K. Kim, “In situ observation of filamentary conducting channels in an asymmetric Ta2O5-x/TaO2-x bilayer structure,” Nature Communications, 4, 1-9 (2013).Google Scholar
  36. 36.
    D.B. Strukov, F. Alibart, R.S. Williams, “Thermophoresis/diffusion as a plausible mechanism for unipolar resistive switching in metal-oxide-metal memristors,” Applied Physics A, 107, 509-518 (2012).CrossRefGoogle Scholar
  37. 37.
    F. Miao, W. Yi, I. Goldfarb, J.J. Yang, M.-X. Zhang, M.D. Pickett, J.P. Strachan, G. Medeiros-Ribeiro, R.S. Williams, “Continuous Electrical Tuning of the Chemical Composition of TaOx-Based Memristors,” ACS Nano, 6, 2312-2318 (2012).CrossRefGoogle Scholar
  38. 38.
    P.R. Mickel, A.J. Lohn, M.J. Marinella, “Isothermal Switching and Detailed Filament Evolution in Memristive Systems,” Advanced Materials, 26, 4486-4490 (2014).Google Scholar
  39. 39.
    A.J. Lohn, P.R. Mickel, C.D. James, M.J. Marinella, “Degenerate Resistive Switching and Ultrahigh Density Storage in Resistive Memory,” Applied Physics Letters, 105, 103501 (2014).Google Scholar
  40. 40.
    P.R. Mickel, A.J. Lohn, M.J. Marinella, “Precise electrical control of nanoscale resistive filament geometry,” unpublished (2013).Google Scholar
  41. 41.
    D.F. Marrone, T.L. Petit, “The role of synaptic morphology in neural plasticity: structural interactions underlying synaptic power,” Brain Research Reviews, 38, 291-308 (2002).CrossRefGoogle Scholar
  42. 42.
    R. Yuste, T. Bonhoeffer, “Morphological Changes in Dendritic Spines Associated with Long-Term Synaptic Plasticity,” Annual Reviews of Neuroscience, 24, 1071-1089 (2001).CrossRefGoogle Scholar
  43. 43.
    R. Lamprecht, J. LeDoux, “Structural Plasticity and Memory,” Nature Reviews Neuroscience, 5, 45-54 (2004).CrossRefGoogle Scholar
  44. 44.
    H. Kasai, M. Fukuda, S. Watanabe, A. Hayashi-Takagi, J. Noguchi, “Structural dynamics of dendritic spines in memory and cognition,” Trends in Neurosciences, 33, 121-129 (2010).CrossRefGoogle Scholar
  45. 45.
    M. Matamales, “Neuronal activity-regulated gene transcription: how are distant synaptic signals conveyed to the nucleus?,” F1000Research, 1, 69 (2012).Google Scholar
  46. 46.
    S.N. Burke, C.A. Barnes, “Neural plasticity in the ageing brain,” Nature Reviews Neuroscience, 7, 30-40 (2006).CrossRefGoogle Scholar
  47. 47.
    F.H. Gage, “Neurogenesis in the Adult Brain,” The Journal of Neuroscience, 22, 612-613 (2002).Google Scholar
  48. 48.
    J.B. Aimone, J. Wiles, F.H. Gage, “Potential role for adult neurogenesis in the encoding of time in new memories,” Nature Neuroscience, 9, 723-727 (2006).CrossRefGoogle Scholar
  49. 49.
    I. Imayoshi, M. Sakamoto, T. Ohtsuka, K. Takao, T. Miyakawa, M. Yamaguchi, K. Mori, T. Ikeda, S. Itohara, R. Kageyama, “Roles of continuous neurogenesis in the structural and functional integrity of the adult forebrain,” Nature Neuroscience, 11, 1153-1161 (2008).CrossRefGoogle Scholar
  50. 50.
    C.D. Clelland, M. Choi, C. Romberg, G.D. Clemenson Jr, A. Fragniere, P. Tyers, S. Jessberger, L.M. Saksida, R.A. Barker, F.H. Gage, T.J. Bussey, “A Functional Role for Adult Hippocampal Neurogenesis in Spatial Pattern Separation,” Science, 325, 210-213 (2009).CrossRefGoogle Scholar
  51. 51.
    J.B. Aimone, J. Wiles, F.H. Gage, “Computational Influence of Adult Neurogenesis on Memory Encoding,” Neuron, 61, 187-202 (2009).CrossRefGoogle Scholar
  52. 52.
    Y. Li, J.B. Aimone, X. Xu, E.M. Callaway, F.H. Gage, “Development of GABAergic inputs controls the contribution of maturing neurons to the adult hippocampal network,” Proceedings of the National Academy of Science, 109, 4290-4295 (2012).CrossRefGoogle Scholar
  53. 53.
    R.A. Chambers, M.N. Potenza, R.E. Hoffman, W. Miranker, “Simulated Apoptosis/Neurogenesis Regulates Learning and Memory Capabilities of Adaptive Neural Networks,” Neuropsychopharmacology, 29, 747-758 (2004).CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Andrew J. Lohn
    • 1
  • Patrick R. Mickel
    • 1
  • James B. Aimone
    • 1
  • Erik P. Debenedictis
    • 1
  • Matthew J. Marinella
    • 1
  1. 1.Cybersecurity Systems for Human Cognition Augmentation, Sandia National LaboratoriesAlbuquerqueUSA

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