S
S. G. Hu
Researcher at University of Electronic Science and Technology of China
Publications - 34
Citations - 914
S. G. Hu is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: Artificial neural network & Memristor. The author has an hindex of 11, co-authored 34 publications receiving 626 citations. Previous affiliations of S. G. Hu include Nanyang Technological University.
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Journal ArticleDOI
Associative memory realized by a reconfigurable memristive Hopfield neural network
S. G. Hu,Liu Youliang,Zhen Liu,Tupei Chen,J. J. Wang,Qian Yu,L. J. Deng,You Yin,Sumio Hosaka +8 more
TL;DR: This work demonstrates the associative memory on the basis of a memristive Hopfield network, and shows that both single-associative memory and multi-association memories can be realized with the memristiveshopfield network.
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A light-stimulated synaptic transistor with synaptic plasticity and memory functions based on InGaZnOx–Al2O3 thin film structure
TL;DR: A synaptic transistor based on the indium gallium zinc oxide (IGZO)–aluminum oxide (Al2O3) thin film structure, which uses ultraviolet (UV) light pulses as the pre-synaptic stimulus, has been demonstrated and exhibits the behavior of synaptic plasticity like the paired-pulse facilitation.
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Emulating the paired-pulse facilitation of a biological synapse with a NiOx-based memristor
TL;DR: The behavior of the memristor is surprisingly similar to the paired-pulse facilitation of a biological synapse, and the magnitude of the facilitation decreases with the pulse interval, while it increases with the pulses magnitude or pulse width.
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Emulating the Ebbinghaus forgetting curve of the human brain with a NiO-based memristor
TL;DR: The well-known Ebbinghaus forgetting curve, which describes how information is forgotten over time, can be emulated using a NiO-based memristor with conductance that decreases with time after the application of electrical pulses.
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Review of Nanostructured Resistive Switching Memristor and Its Applications
TL;DR: In this paper, the authors reviewed the working mechanisms and mathematical models of nanostructured resistive switching memristors, and examined various emerging applications of the memristor, including memory, analog, logic and neuromorphic applications.