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.

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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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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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