Skip to main content
Log in

Quantum materials for brain sciences and artificial intelligence

  • Technical Feature
  • Published:
MRS Bulletin Aims and scope Submit manuscript

Abstract

A hallmark of life is plasticity, which enables reproduction, evolution, and environmental adaptivity. It is natural to wonder if these remarkable features in nature and biology can be realized in the materials world and implemented in the emerging fields of autonomous systems, artificial intelligence, and animal–machine interfaces. First, we describe fundamental features of neurons and synapses in the brain that are responsible for information processing. Then we discuss mechanisms governing electronic plasticity in correlated electronic quantum materials that mimic organismic behavior. We give examples of learning networks and circuits designed using quantum materials that can be implemented for machine intelligence. We conclude with suggestions for future interdisciplinary research wherein synergistic interactions between orbital filling, defects, and strain could give rise to new functionality of relevance to sensory interfaces (e.g., haptics), neural information processing, and neuroscience.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7

Similar content being viewed by others

References

  1. S. Herculano-Houzel, Front. Hum. Neurosci. 3, 31 (2009).

    Google Scholar 

  2. B.P. Bean, Nat. Rev. Neurosci. 8, 451 (2007).

    Google Scholar 

  3. E.R. Kandel, J.H. Schwartz, T.M. Jessell, Eds., Principles of Neural Science (McGraw-Hill, New York, 2000).

    Google Scholar 

  4. S.B. Laughlin, T.J. Sejnowski, Science 301, 1870 (2003).

    Google Scholar 

  5. A.E. Pereda, Nat. Rev. Neurosci. 15, 250 (2014).

    Google Scholar 

  6. A.A. Grace, B.S. Bunney, J. Neurosci. 4, 2866 (1984).

    Google Scholar 

  7. V. Lovat, D. Pantarotto, L. Lagostena, B. Cacciari, M. Grandolfo, M. Righi, G. Spalluto, M. Prato, L. Ballerini, Nano Lett. 5, 1107 (2005).

    Google Scholar 

  8. M.A. Lebedev, M.A. Nicolelis, Trends Neurosci. 29, 536 (2006).

    Google Scholar 

  9. N. Caporale, Y. Dan, Annu. Rev. Neurosci. 31, 25 (2008).

    Google Scholar 

  10. Y. Dan, M.M. Poo, Physiol. Rev. 86, 1033 (2006).

    Google Scholar 

  11. D.S. Jeong, I. Kim, M. Ziegler, H. Kohlstedt, RSC Adv. 3 (10), 3169 (2013).

    Google Scholar 

  12. M. Tsodyks, K. Pawelzik, H. Markram, Neural Comput. 10, 821 (1998).

    Google Scholar 

  13. T.J. Sejnowski, T. Delbruck, Sci. Am. 307, 54 (2012).

    Google Scholar 

  14. P.W. Anderson, Science 177, 393 (1972).

    Google Scholar 

  15. D. Griffiths, A. Dickinson, N. Clayton, Trends Cogn. Sci. 3, 74 (1999).

    Google Scholar 

  16. X.S. Yang, Z. Cui, R. Xiao, A.H. Gandomi, M. Karamanoglu, Eds., Swarm Intelligence and Bio-inspired Computation: An Overview (Elsevier, Amsterdam, 2013).

    Google Scholar 

  17. P. Panda, J.M. Allred, S. Ramanathan, K. Roy, IEEE J. Emerg. Sel. Top. Circuits Syst. 8, 51 (2018).

    Google Scholar 

  18. W.A. Catterall, Annu. Rev. Biochem. 64, 493 (1995).

    Google Scholar 

  19. J.S. Lin, C. Sonde, C. Chen, L. Stan, K.V.L.V. Achari, S. Ramanathan, S. Guha, presented at the 62nd International Electron Devices Meeting (IEDM) (IEEE), San Francisco, December 3–7, 2016, p. 34.

  20. P. Stoliar, J. Tranchant, B. Corraze, E. Janod, M.P. Besland, F. Tesler, M. Rozenberg, L. Cario, Adv. Funct. Mater. 27, 11 (2017).

    Google Scholar 

  21. M.D. Pickett, G. Medeiros-Ribeiro, R.S. Williams, Nat. Mater. 12, 114 (2013).

    Google Scholar 

  22. L. Gao, P.Y. Chen, S. Yu, Appl. Phys. Lett. 111, 103503 (2017).

    Google Scholar 

  23. B. Lalevic, M. Shoga, Thin Solid Films 75, 199 (1981).

    Google Scholar 

  24. N. Locatelli, V. Cros, J. Grollier, Nat. Mater. 13, 11 (2014).

    Google Scholar 

  25. Y. Nishitani, Y. Kaneko, M. Ueda, T. Morie, E. Fujii, J. Appl. Phys. 111, 124108 (2012).

    Google Scholar 

  26. V. Burgt, Y.E. Lubberman, E.J. Fuller, S.T. Keene, G.C. Faria, S. Agarwal, M.J. Marinella, A.A. Talin, A. Salleo, Nat. Mater. 16, 414 (2017).

    Google Scholar 

  27. J. Shi, S.D. Ha, Y. Zhou, F. Schoofs, S. Ramanathan, Nat. Commun. 4, 2676 (2013).

    Google Scholar 

  28. M.K. Nowotny, T. Bak, J. Nowotny, J. Phys. Chem. B 110, 16270 (2006).

    Google Scholar 

  29. D.M. Smyth, The Defect Chemistry of Metal Oxides (Oxford University Press, Oxford, 2000).

    Google Scholar 

  30. H.L. Tuller, S.R. Bishop, Annu. Rev. Mater. Res. 41, 369 (2011).

    Google Scholar 

  31. K. Ramadoss, N. Mandal, X. Dai, Z. Wan, Y. Zhou, L. Rokhinson, Y.P. Chen, J. Hu, S. Ramanathan, Phys. Rev. B Condens. Matter 94, 235124 (2016).

    Google Scholar 

  32. I.V. Nikulin, M.A. Novojilov, A.R. Kaul, S.N. Mudretsova, S.V. Kondrashov, Mater. Res. Bull. 39, 775 (2004).

    Google Scholar 

  33. F. Zuo, P. Panda, M. Kotiuga, J. Li, M. Kang, C. Mazzoli, H. Zhou, A. Barbour, S. Wilkins, B. Narayanan, M. Cherukara, Z. Zhang, S.K.R.S. Sankaranarayanan, R. Comin, K.M. Rabe, K. Roy, S. Ramanathan, Nat. Commun. 8, 240 (2017).

    Google Scholar 

  34. S.D. Ha, J. Shi, Y. Meroz, L. Mahadevan, S. Ramanathan, Phys. Rev. Appl. 2, 064003 (2014).

    Google Scholar 

  35. K. Moon, E. Cha, J. Park, S. Gi, M. Chu, K. Baek, B. Lee, S. Oh, H. Hwang, presented at the 61st International Electron Devices Meeting (IEDM) (IEEE), Washington, DC, December 7–9, 2015, p. 17.

  36. J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A.A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska, D. Hassabis, Proc. Natl. Acad. Sci. U.S.A. 114, 3521 (2017).

    Google Scholar 

  37. M.A. Lancaster, M. Renner, C.A. Martin, D. Wenzel, L.S. Bicknell, M.E. Hurles, T. Homfray, J.M. Penninger, A.P. Jackson, J.A. Knoblich, Nature 501, 373 (2013).

    Google Scholar 

  38. C.H. Rankin, C.D. Beck, C.M. Chiba, Behav. Brain Res. 37, 89 (1990).

    Google Scholar 

  39. J.M. Rondinelli, S.J. May, J.W. Freeland, MRS Bull. 37, 261 (2012).

    Google Scholar 

  40. X. Leng, J. Pereiro, J. Strle, G. Dubuis, A.T. Bollinger, A. Gozar, J. Wu, N. Litombe, C. Panagopoulos, D. Pavuna, I. Božovi c´, NPJ Quantum Mater. 2, 35 (2017).

    Google Scholar 

Download references

Acknowledgments

S.R. acknowledges ARO W911NF-16–1-0289 and AFOSR FA9550–16–1-0159 for support and Z. Zhang for reading of the manuscript and assistance with figures.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shriram Ramanathan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ramanathan, S. Quantum materials for brain sciences and artificial intelligence. MRS Bulletin 43, 534–540 (2018). https://doi.org/10.1557/mrs.2018.147

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1557/mrs.2018.147

Navigation