Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Fully memristive neural networks for pattern classification with unsupervised learning


Neuromorphic computers comprised of artificial neurons and synapses could provide a more efficient approach to implementing neural network algorithms than traditional hardware. Recently, artificial neurons based on memristors have been developed, but with limited bio-realistic dynamics and no direct interaction with the artificial synapses in an integrated network. Here we show that a diffusive memristor based on silver nanoparticles in a dielectric film can be used to create an artificial neuron with stochastic leaky integrate-and-fire dynamics and tunable integration time, which is determined by silver migration alone or its interaction with circuit capacitance. We integrate these neurons with nonvolatile memristive synapses to build fully memristive artificial neural networks. With these integrated networks, we experimentally demonstrate unsupervised synaptic weight updating and pattern classification.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Diffusive memristor artificial neuron.
Fig. 2: Controlled firing of a diffusive memristor artificial neuron.
Fig. 3: Experimental demonstration of unsupervised synaptic weight update using a 2 × 2 drift memristor array interfaced with two diffusive memristor artificial neurons, illustrating circuits with large and small capacitance.
Fig. 4: Fully integrated memristive neural network for pattern classification.
Fig. 5: Unsupervised training of a fully connected network based on the integrated all-memristive neural network.


  1. 1.

    Silver, D. et al. Mastering the game of Go without human knowledge. Nature 550, 354 (2017).

    Article  Google Scholar 

  2. 2.

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

    Article  Google Scholar 

  3. 3.

    Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Lett. 10, 1297–1301 (2010).

    Article  Google Scholar 

  4. 4.

    Yu, S., Wu, Y., Jeyasingh, R., Kuzum, D. & Wong, H. S. P. An Electronic Synapse Device Based on Metal Oxide Resistive Switching Memory for Neuromorphic Computation. IEEE Trans. Elect. Dev. 58, 2729–2737 (2011).

    Article  Google Scholar 

  5. 5.

    Ohno, T. et al. Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. Nat. Mater. 10, 591–595 (2011).

    Article  Google Scholar 

  6. 6.

    Pershin, Y. V. & Di Ventra, M. Neuromorphic, Digital, and Quantum Computation With Memory Circuit Elements. Proc. IEEE 100, 2071–2080 (2012).

    Article  Google Scholar 

  7. 7.

    Lim, H., Kim, I., Kim, J. S., Hwang, C. S. & Jeong, D. S. Short-term memory of TiO2-based electrochemical capacitors: empirical analysis with adoption of a sliding threshold. Nanotechnology 24, 384005 (2013).

    Article  Google Scholar 

  8. 8.

    Sheridan, P., Ma, W. & Lu, W. in Circuits and Systems (ISCAS), 2014 IEEE International Symposium on. 1078–1081 (IEEE).

  9. 9.

    La Barbera, S., Vuillaume, D. & Alibart, F. Filamentary Switching: Synaptic Plasticity through Device Volatility. ACS Nano 9, 941–949 (2015).

    Article  Google Scholar 

  10. 10.

    Prezioso, M. et al. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature 521, 61–64 (2015).

    Article  Google Scholar 

  11. 11.

    Hu, S. G. et al. Associative memory realized by a reconfigurable memristive Hopfield neural network. Nat. Commun. 6, 7522 (2015).

    Article  Google Scholar 

  12. 12.

    Serb, A. et al. Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses. Nat. Commun. 7, 12611 (2016).

    Article  Google Scholar 

  13. 13.

    Park, J. et al. TiOx-based RRAM synapse with 64-levels of conductance and symmetric conductance change by adopting a hybrid pulse scheme for neuromorphic computing. IEEE Elect. Dev. Lett. 37, 1559–1562 (2016).

    Article  Google Scholar 

  14. 14.

    Shulaker, M. M. et al. Three-dimensional integration of nanotechnologies for computing and data storage on a single chip. Nature 547, 74–78 (2017).

    Article  Google Scholar 

  15. 15.

    Suri, M. et al. in Electron Devices Meeting (IEDM), 2011 IEEE International. 4.4.1-4.4. 4 (IEEE).

  16. 16.

    Eryilmaz, S. B. et al. Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array. Front. Neurosci. 8, 205 (2014).

    Article  Google Scholar 

  17. 17.

    Burr, G. W. et al. Experimental Demonstration and Tolerancing of a Large-Scale Neural Network (165 000 Synapses) Using Phase-Change Memory as the Synaptic Weight Element. IEEE Trans. Elect. Dev. 62, 3498–3507 (2015).

    Article  Google Scholar 

  18. 18.

    Ambrogio, S. et al. Unsupervised Learning by Spike Timing Dependent Plasticity in Phase Change Memory (PCM) Synapses. Front. Neurosci. 10, 56 (2016).

    Article  Google Scholar 

  19. 19.

    van de Burgt, Y. et al. A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing. Nat. Mater. 16, 414–418 (2017).

    Article  Google Scholar 

  20. 20.

    Indiveri, G. et al. Neuromorphic silicon neuron circuits. Front. Neurosci. 5, 73 (2011).

    Google Scholar 

  21. 21.

    Merolla, P. A. et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345, 668–673 (2014).

    Article  Google Scholar 

  22. 22.

    Sourikopoulos, I. et al. A 4-fJ/Spike Artificial Neuron in 65 nm CMOS Technology. Front. Neurosci. 11, 123 (2017).

    Article  Google Scholar 

  23. 23.

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

    Article  Google Scholar 

  24. 24.

    Lim, H. et al. Reliability of neuronal information conveyed by unreliable neuristor-based leaky integrate-and-fire neurons: a model study. Sci. Rep. 5, 9776 (2015).

    Article  Google Scholar 

  25. 25.

    Stoliar, P. et al. A Leaky-Integrate-and-Fire Neuron Analog Realized with a Mott Insulator. Adv. Funct. Mater., 1604740, (2017).

  26. 26.

    Tuma, T., Pantazi, A., Le Gallo, M., Sebastian, A. & Eleftheriou, E. Stochastic phase-change neurons. Nat. Nanotechnol. 11, 693–699 (2016).

    Article  Google Scholar 

  27. 27.

    Al-Shedivat, M., Naous, R., Cauwenberghs, G. & Salama, K. N. Memristors empower spiking neurons with stochasticity. IEEE Trans. Emerg. Sel. Top. Circuits Syst. 5, 242–253 (2015).

    Article  Google Scholar 

  28. 28.

    Mehonic, A. & Kenyon, A. J. Emulating the Electrical Activity of the Neuron Using a Silicon Oxide RRAM Cell. Front. Neurosci. 10, 57 (2016).

    Article  Google Scholar 

  29. 29.

    Gupta, I. et al. Real-time encoding and compression of neuronal spikes by metal-oxide memristors. Nat. Commun. 7, 12805 (2016).

    Article  Google Scholar 

  30. 30.

    Lim, H. et al. Relaxation oscillator-realized artificial electronic neurons, their responses, and noise. Nanoscale 8, 9629–9640 (2016).

    Article  Google Scholar 

  31. 31.

    Yang, Y. et al. Observation of conducting filament growth in nanoscale resistive memories. Nat. Commun. 3, 732 (2012).

    Article  Google Scholar 

  32. 32.

    Liu, Q. et al. Real-time observation on dynamic growth/dissolution of conductive filaments in oxide-electrolyte-based ReRAM. Adv. Mater. 24, 1844–1849 (2012).

    Article  Google Scholar 

  33. 33.

    Wang, Z. et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat. Mater. 16, 101–108 (2016).

    Article  Google Scholar 

  34. 34.

    Magee, J. C. Dendritic integration of excitatory synaptic input. Nat. Rev. Neurosci. 1, 181–190 (2000).

    Article  Google Scholar 

  35. 35.

    Gerstner, W. & Kistler, W. M. Spiking Neuron Models: Single Neurons, Populations, Plasticity. (Cambridge University Press, 2002).

  36. 36.

    Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9, 1735–1780 (1997).

    Article  Google Scholar 

  37. 37.

    Tsuruoka, T. et al. Effects of Moisture on the Switching Characteristics of Oxide-Based, Gapless-Type Atomic Switches. Adv. Funct. Mater. 22, 70–77 (2012).

    Article  Google Scholar 

  38. 38.

    Valov, I. et al. Atomically controlled electrochemical nucleation at superionic solid electrolyte surfaces. Nat. Mater. 11, 530–535 (2012).

    Article  Google Scholar 

  39. 39.

    Valov, I. et al. Nanobatteries in redox-based resistive switches require extension of memristor theory. Nat. Commun. 4, 1771 (2013).

    Article  Google Scholar 

  40. 40.

    Messerschmitt, F., Kubicek, M. & Rupp, J. L. M. How Does Moisture Affect the Physical Property of Memristance for Anionic-Electronic Resistive Switching Memories? Adv. Funct. Mater. 25, 5117–5125 (2015).

    Article  Google Scholar 

  41. 41.

    Valov, I. & Lu, W. D. Nanoscale electrochemistry using dielectric thin films as solid electrolytes. Nanoscale 8, 13828–13837 (2016).

    Article  Google Scholar 

  42. 42.

    Wong, H.-S. P. et al. Phase change memory. Proc. IEEE 98, 2201–2227 (2010).

    Article  Google Scholar 

  43. 43.

    Jeyasingh, R., Liang, J., Caldwell, M. A., Kuzum, D. & Wong, H.-S. P. in Custom Integrated Circuits Conference (CICC), 2012 IEEE. 1-7 (IEEE).

  44. 44.

    Mainen, Z. F. & Sejnowski, T. J. Influence of dendritic structure on firing pattern in model neocortical neurons. Nature 382, 363–366 (1996).

    Article  Google Scholar 

  45. 45.

    Roweis, S. T. & Saul, L. K. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science 290, 2323–2326 (2000).

    Article  Google Scholar 

  46. 46.

    Kim, S. et al. Experimental Demonstration of a Second-Order Memristor and Its Ability to Biorealistically Implement Synaptic Plasticity. Nano Lett. 15, 2203–2211 (2015).

    Article  Google Scholar 

  47. 47.

    Yu, S. et al. A low energy oxide-based electronic synaptic device for neuromorphic visual systems with tolerance to device variation. Adv. Mater. 25, 1774–1779 (2013).

    Article  Google Scholar 

  48. 48.

    Tuma, T., Le Gallo, M., Sebastian, A. & Eleftheriou, E. Detecting Correlations Using Phase-Change Neurons and Synapses. IEEE Elect. Dev. Lett. 37, 1238–1241 (2016).

    Article  Google Scholar 

  49. 49.

    Pantazi, A., Wozniak, S., Tuma, T. & Eleftheriou, E. All-memristive neuromorphic computing with level-tuned neurons. Nanotechnology 27, 355205 (2016).

    Article  Google Scholar 

  50. 50.

    Sebastian, A. et al. Temporal correlation detection using computational phase-change memory. Nat. Commun. 8, 1115 (2017).

    Article  Google Scholar 

  51. 51.

    Li, C. et al. Analogue signal and image processing with large memristor crossbars. Nat. Electron.. (2017).

    Google Scholar 

  52. 52.

    Midya, R. et al. Anatomy of Ag/Hafnia-Based Selectors with 1010 Nonlinearity. Adv. Mater. 29, 1604457 (2017).

    Article  Google Scholar 

  53. 53.

    Jiang, H. et al. A novel true random number generator based on a stochastic diffusive memristor. Nat. Commun. 8, 882 (2017).

    Article  Google Scholar 

Download references


This work was supported in part by the U.S. Air Force Research Laboratory (AFRL) (Grant No. FA8750-15-2-0044), the Defense Advanced Research Projects Agency (DARPA) (Contract No. D17PC00304), the Intelligence Advanced Research Projects Activity (IARPA) (contract 2014-14080800008), and the National Science Foundation (NSF) (ECCS-1253073). H.W. was supported by Beijing Advanced Innovation Center for Future Chip (ICFC) and NSFC (61674089, 61674092). The authors would like to thank Ning Ge from HP Inc. and Mark McLean from Laboratory for Physical Sciences at Research Park for vaulable discussions. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of AFRL. Part of the device fabrication was conducted in the clean room of Center for Hierarchical Manufacturing (CHM), an NSF Nanoscale Science and Engineering Center (NSEC) located at the University of Massachusetts Amherst. The authors thank Mark McLean for useful discussions on computing.

Author information




J.J.Y. conceived the concept. J.J.Y., Q.X., Z.W. and S.J. designed the experiments. Z.W., P.Y., and C.L. fabricated the devices. Z.W., S.J., W.S., Y.L, R.M., and M.R. performed electrical measurements. S.E.S. performed the simulation. S.A., Y.Z., H.J., P.L., J.H.Y., N.K.U., J.Z., M.H., J.P.S., M.B, Q.W., H.W., and R.S.W. helped with experiments and data analysis. J.J.Y., Q.X., Z.W., S.J., and R.S.W. wrote the paper. All authors discussed the results and implications and commented on the manuscript at all stages.

Corresponding authors

Correspondence to R. Stanley Williams or Qiangfei Xia or J. Joshua Yang.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figures, Table, Notes, and References

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wang, Z., Joshi, S., Savel’ev, S. et al. Fully memristive neural networks for pattern classification with unsupervised learning. Nat Electron 1, 137–145 (2018).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing