Biological plausibility and stochasticity in scalable VO2 active memristor neurons

Abstract

Neuromorphic networks of artificial neurons and synapses can solve computationally hard problems with energy efficiencies unattainable for von Neumann architectures. For image processing, silicon neuromorphic processors outperform graphic processing units in energy efficiency by a large margin, but deliver much lower chip-scale throughput. The performance-efficiency dilemma for silicon processors may not be overcome by Moore’s law scaling of silicon transistors. Scalable and biomimetic active memristor neurons and passive memristor synapses form a self-sufficient basis for a transistorless neural network. However, previous demonstrations of memristor neurons only showed simple integrate-and-fire behaviors and did not reveal the rich dynamics and computational complexity of biological neurons. Here we report that neurons built with nanoscale vanadium dioxide active memristors possess all three classes of excitability and most of the known biological neuronal dynamics, and are intrinsically stochastic. With the favorable size and power scaling, there is a path toward an all-memristor neuromorphic cortical computer.

Full text

Its licence does not allow us to show the full text. Read it at PubMed Central:

References 45

1. 1.

   Xia Q, et al. Memristor-CMOS hybrid integrated circuits for reconfigurable logic. Nano. Lett. 2009;9:3640-3645.

   Not in our library10.1021/nl901874j

2. 2.

   Khiat A, Ayliffe P, Prodromakis T. High density crossbar arrays with sub- 15 nm single cells via liftoff process only. Sci. Rep. 2016;6:32614.

   Not in our library10.1038/srep32614

3. 3.

   Kim S, et al. Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity. Nano. Lett. 2015;15:2203-2211.

   Not in our library10.1021/acs.nanolett.5b00697

4. 4.

   Wang Z, et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat. Mater. 2016;16:101-108.

   Not in our library10.1038/nmat4756

5. 5.

   Pickett MD, Medeiros-Ribeiro G, Williams RS. A scalable neuristor built with Mott memristors. Nat. Mater. 2013;12:114-117.

   Not in our library10.1038/nmat3510

6. 6.

   Lim H, et al. Reliability of neuronal information conveyed by unreliable neuristor-based leaky integrate-and-fire neurons: a model study. Sci. Rep. 2015;5:09776.

   Not in our library10.1038/srep09776

7. 7.

   Izhikevich EM. Which model to use for cortical spiking neurons? IEEE. Trans. Neural Netw. 2004;15:1063-1070.

   Not in our library10.1109/tnn.2004.832719

8. 8.

   Chua LO, Sbitnev V, Kim H. Hodgkin–Huxley axon is made of memristors. Int. J. Bifur. Chaos. 2012;22:1230011.

   Not in our library10.1142/s021812741230011x

9. 9.

   Burr GW, et al. Experimental demonstration and tolerancing of a large-scale neural network (165000 synapses) using phase-change memory as the synaptic weight element. IEEE Trans. Electron Dev. 2015;62:3498-3507.

   Not in our library10.1109/ted.2015.2439635

10. 10.

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

    Not in our library10.1038/nature14441

11. 11.

    Eryilmaz SB, et al. Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array. Front. Neurosci. 2014;8:205.

    Not in our library10.3389/fnins.2014.00205

12. 12.

    Choi S, Shin JH, Lee J, Sheridan P, Lu WD. Experimental demonstration of feature extraction and dimensionality reduction using memristor networks. Nano. Lett. 2017;17:3113-3118.

    Not in our library10.1021/acs.nanolett.7b00552

13. 13.

    Tuma T, Pantazi A, Le Gallo M, Sebastian A, Eleftheriou E. Stochastic phase-change neurons. Nat. Nanotech. 2016;11:693-699.

    Not in our library10.1038/nnano.2016.70

14. 14.

    Chua LO, Kang SM. Memristive devices and systems. Proc. IEEE. 1976;64:209-223.

    Not in our library10.1109/proc.1976.10092

15. 15.

    Chua LO, Sbitnev V, Kim H. Neurons are poised near the edge of chaos. Int. J. Bifurc. Chaos. 2012;22:1250098.

    Not in our library10.1142/s0218127412500988

16. 16.

    Ascoli A., Tetzlaff R., Chua L. O., Yi W., Williams R. S. Memristor Emulators: A Note on Modeling. Advances in Memristors, Memristive Devices and Systems. 2017;1-17.

    Not in our library

17. 17.

    Alexandrov A. S., Bratkovsky A. M., Bridle B., Savel’ev S. E., Strukov D. B., Stanley Williams R. Current-controlled negative differential resistance due to Joule heating in TiO2. Applied Physics Letters. 2011;99(20):202104.

    Not in our library10.1063/1.3660229

18. 18.

    Ovshinsky SR. Reversible electrical switching phenomena in disordered structures. Phys. Rev. Lett. 1968;21:1450.

    Not in our library10.1103/physrevlett.21.1450

19. 19.

    Czubatyj W, Hudgens SJ. Thin-film ovonic threshold switch: its operation and application in modern integrated circuits. Electron. Mater. Lett. 2012;8:157-167.

    Not in our library10.1007/s13391-012-2040-z

20. 20.

    Ielmini D. Threshold switching mechanism by high-field energy gain in the hopping transport of chalcogenide glasses. Phys. Rev. B. 2008;78:035308.

    Not in our library10.1103/physrevb.78.035308

21. 21.

    Pickett MD, Williams RS. Sub-100 fJ and sub-nanosecond thermally driven threshold switching in niobium oxide crosspoint nanodevices. Nanotechnol. 2012;23:215202.

    Not in our library10.1088/0957-4484/23/21/215202

22. 22.

    Yi W, Oh TC, Crowell JA, Flores EA, King PA. Low-voltage threshold switch devices with current-controlled negative differential resistance based on electroformed vanadium oxide layer. US Pat. Appl. No. 2017;15/417:049.

    Not in our library

23. 23.

    Crane HD. The neuristor. IRE Trans. Elect. Comput. 1960;9:370-371.

    Not in our library10.1109/tec.1960.5219861

24. 24.

    Crane HD. Neuristor—a novel device and system concept. Proc. IRE. 1962;50:2048-2060.

    Not in our library10.1109/jrproc.1962.288234

25. 25.

    Nagumo J, Arimoto S, Yoshizawa S. An active pulse transmission line simulating nerve axon. Proc. IRE. 1962;50:2061-2070.

    Not in our library10.1109/jrproc.1962.288235

26. 26.

    Wilamowski BM, Czarnul Z, Bialko M. Novel inductorless neuristor line. Electron. Lett. 1975;11:355-356.

    Not in our library10.1049/el:19750270

27. 27.

    Moon, K. et al. High density neuromorphic system with Mo/Pr0.7Ca0.3MnO3 synapse and NbO2 IMT oscillator neuron. In IEEE International Electron Devices Meeting (IEDM) (IEEE, Washington, DC, USA, 2015) 10.1109/IEDM.2015.7409721.

    Not in our library

28. 28.

    Farhat, N. H. & Eldefrawy, M. H. Bifurcating neuron: characterization and dynamics. In Proc. Photonics for Computers, Neural Networks, and Memories. Vol 1773, 23–35 (SPIE, San Diego, CA, 1992).

    Not in our library

29. 29.

    Stoliar P, et al. A leaky-integrate-and-fire neuron analog realized with a Mott insulator. Adv. Funct. Mater. 2017;27:1604740.

    Not in our library10.1002/adfm.201604740

30. 30.

    Wang Z, et al. Fully memristive neural networks for pattern classification with unsupervised learning. Nat. Electron. 2018;1:137-145.

    Not in our library10.1038/s41928-018-0023-2

31. 31.

    Hodgkin AL. The local electric changes associated with repetitive action in a non-medullated axon. J. Physiol. 1948;15:165-181.

    Not in our library10.1113/jphysiol.1948.sp004260

32. 32.

    Schultz W. Predictive reward signal of dopamine neurons. J. Neurophysiol. 1998;80:1-27.

    Not in our library10.1152/jn.1998.80.1.1

33. 33.

    Meng X, Huguet G, Rinzel J. Type III excitability, slope sensitivity and coincidence detection. Discret. Contin. Dyn. Syst. A. 2012;32:2729-2757.

    Not in our library10.3934/dcds.2012.32.2729

34. 34.

    Rothman JSM. The roles potassium currents play in regulating the electrical activity of ventral cochlear nucleus neurons. J. Neurophysiol. 2003;89:3097-3113.

    Not in our library10.1152/jn.00127.2002

35. 35.

    Cassidy, A. S et al. Cognitive computing building block: A versatile and efficient digital neuron model for neurosynaptic cores. In Proc. International Joint Conference on Neural Networks (IJCNN) (IEEE, Dallas, TX, USA, 2013).

    Not in our library

36. 36.

    Rinzel, J. E. B. Analysis of neural excitability and oscillations. in Methods in neuronal modeling (MIT Press, Cambridge, MA, 1989).

    Not in our library

37. 37.

    Izhikevich EM. Neural excitability, spiking and bursting. Int. J. Bifur. Chaos. 2000;10:1171-1266.

    Not in our library10.1142/s0218127400000840

38. 38.

    FitzHugh R. Impulses and physiological states in theoretical models of nerve membrane. Biophys. J. 1961;1:445-466.

    Not in our library10.1016/s0006-3495(61)86902-6

39. 39.

    Segundo JP, Altshuler E, Stiber M, Garfinkel A. Periodic inhibition of living pacemaker neurons. I. Locked, intermittent, messy and hopping behaviors. Int. J. Bifur. Chaos. 1991;1:549-581.

    Not in our library10.1142/s0218127491000415

40. 40.

    Fitzurka MA, Tam DC. A joint interspike interval difference stochastic spike train analysis: detecting local trends in the temporal firing patterns of single neurons. Biol. Cybern. 1999;80:309-326.

    Not in our library10.1007/s004220050528

Cited by papers we hold 5

• Ion-tunable antiambipolarity in mixed ion–electron conducting polymers enables biorealistic organic electrochemical neurons · 2023
  No notice
• Advancement in Soft Iontronic Resistive Memory Devices and Their Application for Neuromorphic Computing · 2023
  Not checked
• Experimental demonstration of highly reliable dynamic memristor for artificial neuron and neuromorphic computing · 2022
  No notice
• Modeling Simplification and Dynamic Behavior of N-Shaped Locally-Active Memristor Based Oscillator · 2020
  Not checked
• An artificial spiking afferent nerve based on Mott memristors for neurorobotics · 2020
  No notice

Reads

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.