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- W3201969487 abstract "Modern artificial neural network technology using a deterministic computing framework is faced with a critical challenge in dealing with massive data that are largely unstructured and ambiguous. This challenge demands the advances of an elementary physical device for tackling these uncertainties. Here, we designed and fabricated a SiOx nanorod memristive device by employing the glancing angle deposition (GLAD) technique, suggesting a controllable stochastic artificial neuron that can mimic the fundamental integrate-and-fire signaling and stochastic dynamics of a biological neuron. The nanorod structure provides the random distribution of multiple nanopores all across the active area, capable of forming a multitude of Si filaments at many SiOx nanorod edges after the electromigration process, leading to a stochastic switching event with very high dynamic range (≈5.15 × 1010 ) and low energy (≈4.06 pJ). Different probabilistic activation (ProbAct) functions in a sigmoid form are implemented, showing its controllability with low variation by manufacturing and electrical programming schemes. Furthermore, as an application prospect, based on the suggested memristive neuron, we demonstrated the self-resting neural operation with the local circuit configuration and revealed probabilistic Bayesian inferences for genetic regulatory networks with low normalized mean squared errors (≈2.41 × 10-2 ) and its robustness to the ProbAct variation." @default.
- W3201969487 created "2021-10-11" @default.
- W3201969487 creator A5042017592 @default.
- W3201969487 creator A5054498826 @default.
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- W3201969487 creator A5085805508 @default.
- W3201969487 creator A5085950813 @default.
- W3201969487 date "2021-10-22" @default.
- W3201969487 modified "2023-10-18" @default.
- W3201969487 title "Controllable SiO <i> <sub>x</sub> </i> Nanorod Memristive Neuron for Probabilistic Bayesian Inference" @default.
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- W3201969487 doi "https://doi.org/10.1002/adma.202104598" @default.
- W3201969487 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34618384" @default.
- W3201969487 hasPublicationYear "2021" @default.
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