Journal ArticleDOI
Multiple memristor series–parallel connections with use in synaptic circuit design
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In this study, multiple memristors, both in series and parallel connections, and their characteristics are further studied including the transient behaviours when asynchronous change happens and the composite electric properties in steady state etc.Abstract:
With the increase of research interest on memristors, various single or multiple memristor configurations have been integrated with advanced complementary metal-oxide-semiconducor technology, which promises efficient implementations of synaptic connections in neuromorphic computing systems, or computing elements in signal processing systems. In this study, multiple memristors, both in series and parallel connections, and their characteristics are further studied including the transient behaviours when asynchronous change happens and the composite electric properties in steady state etc. Particularly, the specific conditions to reach steady state and produce composite memristive effects are presented in detail. Furthermore, several synaptic memristor circuits based on series and parallel connections are also discussed.read more
Citations
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Journal ArticleDOI
Dynamical analysis and image encryption application of a novel memristive hyperchaotic system
TL;DR: Experimental results and security analysis show that the proposed algorithm has excellent image encryption performance and can resist a variety of common attacks against the image encryption system.
Journal ArticleDOI
Electronically tunable memristor based on VDCC
TL;DR: The presented memristor exhibits electronically controllable characteristics, which are superior to its counterparts, and both the memductance value and the operating frequency can be electronically tuned through voltage sources.
Journal ArticleDOI
Fully memristive spiking-neuron learning framework and its applications on pattern recognition and edge detection
Zhiri Tang,Zhiri Tang,Yanhua Chen,Shizhuo Ye,Ruihan Hu,Hui Wang,Jin He,Qijun Huang,Sheng Chang +8 more
TL;DR: A fully memristive spiking-neuron learning framework is introduced, in which a neuron structure is just built of one drift and one diffusion memristor models and spikes are used as transmission signals and its peak signal noise ratio is much higher than traditional algorithms in edge detection.
Journal ArticleDOI
Experimental verification of a memristive neural network
TL;DR: An electronic circuit able to emulate the behavior of a neural network based on memristive synapses is presented, built with two flux-controlled floating memristor emulator circuits operating at high frequency and two passive resistors.
Journal ArticleDOI
Hybrid dual-complementary metal–oxide–semiconductor/memristor synapse-based neural network with its applications in image super-resolution
TL;DR: A novel dual-complementary metal-oxide-semiconductor/memristor synaptic circuit is presented, which is capable of performing the negative, zero, and positive synaptic weights via controlling the direction of current passing through the memristors.
References
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Journal ArticleDOI
The missing memristor found
TL;DR: It is shown, using a simple analytical example, that memristance arises naturally in nanoscale systems in which solid-state electronic and ionic transport are coupled under an external bias voltage.
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Memristor-The missing circuit element
TL;DR: In this article, the memristor is introduced as the fourth basic circuit element and an electromagnetic field interpretation of this relationship in terms of a quasi-static expansion of Maxwell's equations is presented.
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Nanoscale Memristor Device as Synapse in Neuromorphic Systems
TL;DR: A nanoscale silicon-based memristor device is experimentally demonstrated and it is shown that a hybrid system composed of complementary metal-oxide semiconductor neurons and Memristor synapses can support important synaptic functions such as spike timing dependent plasticity.
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Memristive devices and systems
Leon O. Chua,Sung-Mo Kang +1 more
TL;DR: In this article, a broad generalization of memristors to an interesting class of nonlinear dynamical systems called memristive systems is introduced, which are unconventional in the sense that while they behave like resistive devices, they can be endowed with a rather exotic variety of dynamic characteristics.
Journal Article
SPICE Model of Memristor with Nonlinear Dopant Drift
TL;DR: It is shown that the hitherto published approaches to the modeling of boundary conditions need not conform with the requirements for the behavior of a practical circuit element, and the described SPICE model of the memristor is constructed as an open model, enabling additional modifications of non-linear boundary conditions.