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Atomically thin optomemristive feedback neurons

A Publisher Correction to this article was published on 07 July 2023

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Cognitive functions such as learning in mammalian brains have been attributed to the presence of neuronal circuits with feed-forward and feedback topologies. Such networks have interactions within and between neurons that provide excitory and inhibitory modulation effects. In neuromorphic computing, neurons that combine and broadcast both excitory and inhibitory signals using one nanoscale device are still an elusive goal. Here we introduce a type-II, two-dimensional heterojunction-based optomemristive neuron, using a stack of MoS2, WS2 and graphene that demonstrates both of these effects via optoelectronic charge-trapping mechanisms. We show that such neurons provide a nonlinear and rectified integration of information, that can be optically broadcast. Such a neuron has applications in machine learning, particularly in winner-take-all networks. We then apply such networks to simulations to establish unsupervised competitive learning for data partitioning, as well as cooperative learning in solving combinatorial optimization problems.

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Fig. 1: Competitive neuron concept.
Fig. 2: Type-II heterojunction neuron.
Fig. 3: Excitory characteristics.
Fig. 4: Excitory and inhibitory characteristics.
Fig. 5: Winner-take-all learning.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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We acknowledge funding for this work from the European Union’s Horizon 2020 Research and Innovation Programme (HyBrain project no. 101046878 and Fun-COMP project no. 780848); from the Engineering and Physical Sciences Research Council through grant numbers EP/R001677/1, EP/M015173/1 and EP/J018694/1; and from the European Research Council through grant no. 725258.

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Authors and Affiliations



S.G.S. and Y.Z. carried out the experiments and analysis. Y.Z. fabricated the devices. S.G.S. conceptualized and performed the device and machine learning simulations. S.G.S, Y.Z., J.W. and H.B. discussed the data and wrote the manuscript. J.W. and H.B. supervised the research.

Corresponding authors

Correspondence to Ghazi Sarwat Syed or Harish Bhaskaran.

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Competing interests

H.B. is a founder of and holds shares in Salience Labs Ltd. The other authors declare no competing interests.

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Nature Nanotechnology thanks Chao Jiang and Suhas Kumar for their contribution to the peer review of this work.

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Supplementary Figs. 1–18 including tables in Figs. 10 and 14, and Discussion.

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Syed, G.S., Zhou, Y., Warner, J. et al. Atomically thin optomemristive feedback neurons. Nat. Nanotechnol. 18, 1036–1043 (2023).

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