Journal ArticleDOI
Neural Synaptic Weighting With a Pulse-Based Memristor Circuit
TLDR
A pulse-based programmable memristor circuit for implementing synaptic weights for artificial neural networks is proposed, and both positive and negative multiplications are performed via a charge-dependent Ohm's law.Abstract:
A pulse-based programmable memristor circuit for implementing synaptic weights for artificial neural networks is proposed. In the memristor weighting circuit, both positive and negative multiplications are performed via a charge-dependent Ohm's law (). The circuit is composed of five memristors with bridge-like connections and operates like an artificial synapse with pulse-based processing and adjustability. The sign switching pulses, weight setting pulses and synaptic processing pulses are applied through a shared input terminal. Simulations are done with both linear memristor and window-based nonlinear memristor models.read more
Citations
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Journal ArticleDOI
Memristor Emulator for Memristor Circuit Applications
TL;DR: The hardware and spice simulation of the proposed emulator showed promising results that provides an alternative solution of hp TiO2 memristor model in real circuit.
Journal ArticleDOI
Memristor-based neural networks
Andy Thomas,Andy Thomas +1 more
TL;DR: This work presents and explains the relevant mechanisms in a biological neural network, such as long-term potentiation and spike time-dependent plasticity, and determines the minimal requirements for an artificial neural network.
Journal ArticleDOI
Memristor Crossbar-Based Neuromorphic Computing System: A Case Study
TL;DR: The results show that the hardware-based training scheme proposed in the paper can alleviate and even cancel out the majority of the noise issue and apply it to brain-state-in-a-box (BSB) neural networks.
Journal ArticleDOI
Memristor Bridge Synapse-Based Neural Network and Its Learning
TL;DR: The use of memristor bridge synapse in the proposed architecture solves one of the major problems, regarding nonvolatile weight storage in analog neural network implementations, and a modified chip-in-the-loop learning scheme suitable for the proposed neural network architecture is proposed.
Journal ArticleDOI
Exponential Stabilization of Memristive Neural Networks With Time Delays
Ailong Wu,Zhigang Zeng +1 more
TL;DR: Some sufficient conditions in terms of linear matrix inequalities are obtained, in order to achieve exponential stabilization of memristive cellular neural networks, and a simplified and effective algorithm is considered for design of the optimal controller.
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.
Journal ArticleDOI
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.
Journal ArticleDOI
Cellular neural networks: theory
Leon O. Chua,L. Yang +1 more
TL;DR: In this article, a class of information processing systems called cellular neural networks (CNNs) are proposed, which consist of a massive aggregate of regularly spaced circuit clones, called cells, which communicate with each other directly through their nearest neighbors.
Journal ArticleDOI
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.
Journal ArticleDOI
Cellular neural networks: applications
Leon O. Chua,Lin-Bao Yang +1 more
TL;DR: Examples of cellular neural networks which can be designed to recognize the key features of Chinese characters are presented and their applications to such areas as image processing and pattern recognition are demonstrated.