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Stochastic phase-change neurons

Abstract

Artificial neuromorphic systems based on populations of spiking neurons are an indispensable tool in understanding the human brain and in constructing neuromimetic computational systems. To reach areal and power efficiencies comparable to those seen in biological systems, electroionics-based and phase-change-based memristive devices have been explored as nanoscale counterparts of synapses. However, progress on scalable realizations of neurons has so far been limited. Here, we show that chalcogenide-based phase-change materials can be used to create an artificial neuron in which the membrane potential is represented by the phase configuration of the nanoscale phase-change device. By exploiting the physics of reversible amorphous-to-crystal phase transitions, we show that the temporal integration of postsynaptic potentials can be achieved on a nanosecond timescale. Moreover, we show that this is inherently stochastic because of the melt-quench-induced reconfiguration of the atomic structure occurring when the neuron is reset. We demonstrate the use of these phase-change neurons, and their populations, in the detection of temporal correlations in parallel data streams and in sub-Nyquist representation of high-bandwidth signals.

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Figure 1: Artificial neuron based on a phase-change device, with an array of plastic synapses at its input.
Figure 2: Dynamics of the phase-change neuron.
Figure 3: Detection of temporal correlations using a single phase-change neuron.
Figure 4: Stochastic firing response of phase-change neurons.
Figure 5: Representation of input stimulus by means of population code.

References

  1. Kandel, E. R., Schwartz, J. H., Jessell, T. M., Siegelbaum, S. A. & Hudspeth, A. J. Principles of Neural Science (McGraw-Hill, 2000).

    Google Scholar 

  2. Mead, C. Neuromorphic electronic systems. Proc. IEEE 78, 1629–1636 (1990).

    Article  Google Scholar 

  3. Sterratt, D., Graham, B. P., Gillies, A. & Willshaw, D. J. Principles of Computational Modelling in Neuroscience (Cambridge Univ. Press, 2011).

    Book  Google Scholar 

  4. Gerstner, W., Kistler, W. M., Naud, R. & Paninski, L. Neuronal Dynamics (Cambridge Univ. Press, 2014).

    Book  Google Scholar 

  5. Indiveri, G., Chicca, E. & Douglas, R. A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity. IEEE Trans. Neural Networks 17, 211–221 (2006).

    Article  Google Scholar 

  6. Schemmel, J., Fieres, J. & Meier, K. Wafer-scale integration of analog neural networks. Proc. Int. Joint Conf. Neural Networks 431–438 (2008).

  7. Indiveri, G. et al. Neuromorphic silicon neuron circuits. Front. Neurosci. 5, 1–23 (2011).

    Google Scholar 

  8. Choudhary, S. et al. Silicon neurons that compute. Lect. Notes Comput. Sci. 7552, 121–128 (2012).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  10. Indiveri, G., Linares-Barranco, B., Legenstein, R., Deligeorgis, G. & Prodromakis, T. Integration of nanoscale memristor synapses in neuromorphic computing architectures. Nanotechnology 24, 384010 (2013).

    Article  Google Scholar 

  11. Gentet, L. J., Stuart, G. J. & Clements, J. D. Direct measurement of specific membrane capacitance in neurons. Biophys. J. 79, 314–320 (2000).

    Article  CAS  Google Scholar 

  12. Averbeck, B. B., Latham, P. E. & Pouget, A. Neural correlations, population coding and computation. Nature Rev. Neurosci. 7, 358–366 (2006).

    Article  CAS  Google Scholar 

  13. Maass, W. Noise as a resource for computation and learning in networks of spiking neurons. Proc. IEEE 102, 860–880 (2014).

    Article  Google Scholar 

  14. Borst, A. & Theunissen, F. Information theory and neural coding. Nature Neurosci. 2, 947–957 (1999).

    Article  CAS  Google Scholar 

  15. Pouget, A., Dayan, P., Zemel, R. & House, A. Information processing with population codes. Nature Rev. Neurosci. 1, 125–132 (2000).

    Article  CAS  Google Scholar 

  16. Modha, D. S. et al. Cognitive computing. Commun. ACM 54, 62–71 (2011).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  19. Kuzum, D., Jeyasingh, R. G. D., Lee, B. & Wong, H. S. P. Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing. Nano Lett. 12, 2179–2186 (2012).

    Article  CAS  Google Scholar 

  20. 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. Electron Dev. 62, 3498–3507 (2015).

    Article  Google Scholar 

  21. Ovshinsky, S. R. Analog neurons and neurosynaptic networks. US patent 6,999,953 B2 (2006).

  22. Wright, C. D., Liu, Y., Kohary, K. I., Aziz, M. M. & Hicken, R. J. Arithmetic and biologically-inspired computing using phase-change materials. Adv. Mater. 23, 3408–3413 (2011).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  24. Burr, G. W. et al. Phase change memory technology. J. Vac. Sci. Technol. B 28, 223 (2010).

    Article  CAS  Google Scholar 

  25. Xiong, F., Liao, A. D., Estrada, D. & Pop, E. Low-power switching of phase-change materials with carbon nanotube electrodes. Science 332, 568–570 (2011).

    Article  CAS  Google Scholar 

  26. Sebastian, A., Le Gallo, M. & Krebs, D. Crystal growth within a phase change memory cell. Nature Commun. 5, 4314 (2014).

    Article  CAS  Google Scholar 

  27. Breitwisch, M. et al. Novel lithography-independent pore phase change memory. Proc. IEEE Symp. VLSI Technol. 100–101 (2007).

  28. Song, S., Miller, K. D. & Abbott, L. F. Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nature Neurosci. 3, 919–926 (2000).

    Article  CAS  Google Scholar 

  29. Abbott, L. F. & Nelson, S. B. Synaptic plasticity taming the beast. Nature Neurosci. 3, 1178–1183 (2000).

    Article  CAS  Google Scholar 

  30. Gütig, R., Aharonov, R., Rotter, S. & Sompolinsky, H. Learning input correlations through nonlinear temporally asymmetric Hebbian plasticity. J. Neurosci. 23, 3697–3714 (2003).

    Article  Google Scholar 

  31. Zipoli, F., Krebs, D. & Curioni, A. Structural origin of resistance drift in amorphous GeTe. Phys. Rev. B 93, 115201 (2016).

    Article  Google Scholar 

  32. Lee, B. S. et al. Distribution of nanoscale nuclei in the amorphous dome of a phase change random access memory. Appl. Phys. Lett. 104, 071907 (2014).

    Article  Google Scholar 

  33. Kalb, J., Spaepen, F. & Wuttig, M. Atomic force microscopy measurements of crystal nucleation and growth rates in thin films of amorphous Te alloys. Appl. Phys. Lett. 84, 5240–5242 (2004).

    Article  CAS  Google Scholar 

  34. Senkader, S. & Wright, C. D. Models for phase-change of Ge2Sb2Te5 in optical and electrical memory devices. J. Appl. Phys. 95, 504–511 (2004).

    Article  CAS  Google Scholar 

  35. Mishali, M. & Eldar, Y. C. Sub-Nyquist sampling bridging theory and practice. IEEE Signal Process. Mag. 28, 98–124 (2011).

    Article  Google Scholar 

  36. Liu, S. C. & Delbruck, T. Neuromorphic sensory systems. Curr. Opin. Neurobiol. 20, 288–295 (2010).

    Article  Google Scholar 

  37. Corradi, F., Superiore, I., You, H., Giulioni, M. & Indiveri, G. Decision making and perceptual bistability in spike-based neuromorphic VLSI systems. Proc. IEEE Int. Symp. Circuits Syst. 2708–2711 (2015).

  38. Pecevski, D., Buesing, L. & Maass, W. Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons. PLoS Comput. Biol. 7, e1002294 (2011).

    Article  CAS  Google Scholar 

  39. Bollen, J., Mao, H. & Zeng, X. Twitter mood predicts the stock market. J. Comput. Sci. 2, 1–8 (2011).

    Article  Google Scholar 

  40. Perera, C., Zaslavsky, A., Christen, P. & Georgakopoulos, D. Context aware computing for the internet of things—a survey. IEEE Commun. Surv. Tutorials 16, 414–454 (2014).

    Article  Google Scholar 

  41. Neftci, E. et al. Synthesizing cognition in neuromorphic electronic systems. Proc. Natl Acad. Sci. USA 110, E3468–E3476 (2013).

    Article  CAS  Google Scholar 

  42. Izhikevich, E. M. Which model to use for cortical spiking neurons? IEEE Trans. Neural Networks 15, 1063–1070 (2004).

    Article  Google Scholar 

  43. Marder, E. & Goaillard, J.-M. Variability, compensation and homeostasis in neuron and network function. Nature Rev. Neurosci. 7, 563–574 (2006).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  45. Gaba, S., Sheridan, P., Zhou, J., Choi, S. & Lu, W. Stochastic memristive devices for computing and neuromorphic applications. Nanoscale 5, 5872–5878 (2013).

    Article  CAS  Google Scholar 

  46. Vincent, A. F. et al. Spin-transfer torque magnetic memory as a stochastic memristive synapse. IEEE Trans. Biomed. Circ. Syst. 1, 1074–1077 (2014).

    Google Scholar 

  47. Ríos, C. et al. Integrated all-photonic non-volatile multi-level memory. Nature Photon. 9, 725–732 (2015).

    Article  Google Scholar 

  48. Di Ventra, M. & Pershin, Y. V. The parallel approach. Nature Phys. 9, 200–202 (2013).

    Article  CAS  Google Scholar 

  49. Sebastian, A., Krebs, D., Le Gallo, M., Pozidis, H. & Eleftheriou, E. A collective relaxation model for resistance drift in phase change memory cells. IEEE Int. Rel. Phys. Symp. Proc. MY.5.1–MY.5.6 (2015).

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Acknowledgements

The authors thank F. Zipoli for the molecular dynamics simulations, L. Kull and M. Stanisavljevic for the electrical circuit design and simulations, W. W. Koelmans, S. Wozniak and G. Cherubini for discussions and C. Bolliger for help with preparation of the manuscript.

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Authors

Contributions

T.T., A.S., A.P. and E.E. conceived the idea of stochastic phase-change neurons. T.T., M.L. and A.P. performed the experiments. All authors contributed to the analysis and interpretation of results. T.T. and A.S. co-wrote the manuscript based on the input from all authors. E.E. supervised the work.

Corresponding authors

Correspondence to Tomas Tuma or Evangelos Eleftheriou.

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The authors declare no competing financial interests.

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Tuma, T., Pantazi, A., Le Gallo, M. et al. Stochastic phase-change neurons. Nature Nanotech 11, 693–699 (2016). https://doi.org/10.1038/nnano.2016.70

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