Nature | Vol 585 | 24 September 2020 | 523- Izhikevich, E. M. Dynamical Systems in Neuroscience. (MIT Press, 2007).
- Chua, L. Everything you wish to know about memristors but are afraid to ask.
Radioengineering 24 , 319–368 (2015). - Chua, L. Handbook of Memristor Networks (Springer Nature, 2019).
- Bohaichuk, S. M. et al. Fast spiking of a Mott VO 2 –carbon nanotube composite device.
Nano Lett. 19 , 6751–6755 (2019). - Kendall, J. D. & Kumar, S. The building blocks of a brain-inspired computer. Appl. Phys.
Rev. 7 , 011305 (2020). - Zidan, M. A., Strachan, J. P. & Lu, W. D. The future of electronics based on memristive
systems. Nature Electronics 1 , 22 (2018). - Paugam-Moisy, H. & Bohte, S. Computing with spiking neuron networks. In Handbook of
Natural Computing (eds Rozenberg, G. et al.) 335–376 (Springer, 2012). - Pickett, M. D., Borghetti, J., Yang, J. J., Medeiros-Ribeiro, G. & Williams, R. S. Coexistence
of memristance and negative differential resistance in a nanoscale metal–oxide–metal
system. Adv. Mater. 23 , 1730–1733 (2011). - Pickett, M. D., Medeiros-Ribeiro, G. & Williams, R. S. A scalable neuristor built with Mott
memristors. Nat. Mater. 12 , 114–117 (2013). - Yi, W. et al. Biological plausibility and stochasticity in scalable VO 2 active memristor
neurons. Nat. Commun. 9 , 4661 (2018). - Khanday, F. A., Kant, N. A., Dar, M. R., Zulkifli, T. Z. A. & Psychalinos, C. Low-voltage low-power
integrable CMOS circuit implementation of integer- and fractional-order FitzHugh–Nagumo
neuron model. IEEE Trans. Neural Netw. Learn. Syst. 30 , 2108–2122 (2018). - Markram, H. Seven challenges for neuroscience. Funct. Neurol. 28 , 145–151 (2013).
- Palmer, T. Modelling: build imprecise supercomputers. Nature 526 , 32 (2015).
- Gibson, G. A. et al. An accurate locally active memristor model for S-type negative
differential resistance in NbOx. Appl. Phys. Lett. 108 , 023505 (2016). - Slesazeck, S. et al. Physical model of threshold switching in NbO 2 -based memristors. RSC
Adv. 5 , 102318–102322 (2015). - Kumar, S. et al. Physical origins of current- and temperature-controlled negative
differential resistances in NbO 2. Nat. Commun. 8 , 658 (2017). - Li, S., Liu, X., Nandi, S. K., Nath, S. K. & Elliman, R. G. Origin of current-controlled negative
differential resistance modes and the emergence of composite characteristics with high
complexity. Adv. Funct. Mater. 29 , 1905060 (2019). - Goodwill, J. M. et al. Spontaneous current constriction in threshold switching devices.
Nat. Commun. 10 , 1628 (2019). - Zhang, J. et al. Thermally induced crystallization in NbO 2 thin films. Sci. Rep. 6 , 34294
(2016). - Seta, K. & Naito, K. Calorimetric study of the phase transition in NbO 2. J. Chem.
Thermodyn. 14 , 921–935 (1982). - Kumar, S. et al. Spatially uniform resistance switching of low current, high endurance
titanium–niobium–oxide memristors. Nanoscale 9 , 1793 (2017). - Kumar, S. et al. The phase transition in VO 2 probed using X-ray, visible and infrared
radiations. Appl. Phys. Lett. 108 , 073102 (2016).
27. Gibson, G. A. Designing negative differential resistance devices based on self-heating.
Adv. Funct. Mater. 28 , 1704175 (2018).
28. Pickett, M. D. & Williams, R. S. Phase transitions enable computational universality in
neuristor-based cellular automata. Nanotechnology 24 , 384002 (2013).
29. Kopell, N. & Somers, D. Anti-phase solutions in relaxation oscillators coupled through
excitatory interactions. J. Math. Biol. 33 , 261–280 (1995).
30. Hoppensteadt, F. C. & Izhikevich, E. M. Thalamo-cortical interactions modeled by weakly
connected oscillators: could the brain use FM radio principles? Biosystems 48 , 85–94
(1998).
31. Bansal, K. et al. Cognitive chimera states in human brain networks. Sci. Adv. 5 , eaau8535
(2019).
32. Steriade, M. Synchronized activities of coupled oscillators in the cerebral cortex and
thalamus at different levels of vigilance. Cereb. Cortex 7 , 583–604 (1997).
33. Csaba, G. & Porod, W. Coupled oscillators for computing: a review and perspective. Appl.
Phys. Rev. 7 , 011302 (2020).
34. Chou, J., Bramhavar, S., Ghosh, S. & Herzog, W. Analog coupled oscillator based
weighted Ising machine. Sci. Rep. 9 , 14786 (2019).
35. Parihar, A., Shukla, N., Jerry, M., Datta, S. & Raychowdhury, A. Vertex coloring of graphs
via phase dynamics of coupled oscillatory networks. Sci. Rep. 7 , 911 (2017); correction 8 ,
6120 (2018).
36. Maffezzoni, P., Bahr, B., Zhang, Z. & Daniel, L. Oscillator array models for associative
memory and pattern recognition. IEEE Trans. Circuits Syst. I 62 , 1591–1598 (2015).
37. Romera, M. et al. Vowel recognition with four coupled spin-torque nano-oscillators.
Nature 563 , 230–234 (2018).
38. Mahmoodi, M., Prezioso, M. & Strukov, D. Versatile stochastic dot product circuits based
on nonvolatile memories for high performance neurocomputing and neurooptimization.
Nat. Commun. 10 , 5113 (2019).
39. Cai, F. et al. Power-efficient combinatorial optimization using intrinsic noise in memristor
Hopfield neural networks. Nat. Electron. 3 , 409–418 (2020).
40. Huang, A., Kantor, R., DeLong, A., Schreier, L. & Istrail, S. QColors: an algorithm for
conservative viral quasispecies reconstruction from short and non-contiguous next
generation sequencing reads. In Silico Biol. 11 , 193–201 (2011).
41. Pang, J. et al. Potential rapid diagnostics, vaccine and therapeutics for 2019 novel
coronavirus (2019-nCoV): a systematic review. J. Clin. Med. 9 , 623 (2020).
42. Mangul, S. et al. Accurate viral population assembly from ultra-deep sequencing data.
Bioinformatics 30 , i329–i337 (2014).
43. Hamerly, R. et al. Experimental investigation of performance differences between
coherent Ising machines and a quantum annealer. Sci. Adv. 5 , eaau0823 (2019).
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