Journal ArticleDOI
Cellular neural networks: theory
Leon O. Chua,L. Yang +1 more
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TLDR
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.Abstract:
A novel class of information-processing systems called cellular neural networks is proposed. Like neural networks, they are large-scale nonlinear analog circuits that process signals in real time. Like cellular automata, they consist of a massive aggregate of regularly spaced circuit clones, called cells, which communicate with each other directly only through their nearest neighbors. Each cell is made of a linear capacitor, a nonlinear voltage-controlled current source, and a few resistive linear circuit elements. Cellular neural networks share the best features of both worlds: their continuous-time feature allows real-time signal processing, and their local interconnection feature makes them particularly adapted for VLSI implementation. Cellular neural networks are uniquely suited for high-speed parallel signal processing. >read more
Citations
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Journal ArticleDOI
A Memristive Multilayer Cellular Neural Network With Applications to Image Processing
TL;DR: A novel memristive multilayer CNN (Mm-CNN) model is presented along with its performance analysis and applications, which has several merits, such as compactness, nonvolatility, versatility, and programmability of synaptic weights.
Journal ArticleDOI
Attractors of non-autonomous stochastic lattice systems in weighted spaces
TL;DR: In this paper, the authors studied the asymptotic behavior of solutions to a class of non-autonomous stochastic lattice systems driven by multiplicative white noise, and established the upper semicontinuity of random attractors as the intensity of noise approaches zero.
Journal ArticleDOI
A new sufficient condition for complete stability of cellular neural networks with delay
TL;DR: In this article, a new sufficient condition for cellular neural networks with delay (DCNNs) to be completely stable was given, and a fixed-point theorem and a convergence theorem of the Gauss-Seidel method played important roles in the proof.
Journal ArticleDOI
An IC chip of Chua's circuit
J.M. Cruz,Leon O. Chua +1 more
TL;DR: In this paper, a working microelectronic chip implementation of Chua's circuit is reported, with the circuit itself occupying a silicon area of 2.5mm*2.8mm.
Journal ArticleDOI
A programmable analog cellular neural network CMOS chip for high speed image processing
Peter R. Kinget,Michiel Steyaert +1 more
TL;DR: In this article, the implementation of an analog programmable CNN-chip in a standard CMOS technology is discussed, where the control parameters or templates in all cells are under direct user control and are tunable over a continuous value range from 1/4 to 4.
References
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Neural networks and physical systems with emergent collective computational abilities
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Neurons with graded response have collective computational properties like those of two-state neurons.
TL;DR: A model for a large network of "neurons" with a graded response (or sigmoid input-output relation) is studied and collective properties in very close correspondence with the earlier stochastic model based on McCulloch - Pitts neurons are studied.
Book
Neurons with graded response have collective computational properties like those of two-state neurons
TL;DR: In this article, a model for a large network of "neurons" with a graded response (or sigmoid input-output relation) is studied, which has collective properties in very close correspondence with the earlier stochastic model based on McCulloch--Pitts neurons.
Journal ArticleDOI
Neural computation of decisions in optimization problems
John J. Hopfield,David W. Tank +1 more
TL;DR: Results of computer simulations of a network designed to solve a difficult but well-defined optimization problem-the Traveling-Salesman Problem-are presented and used to illustrate the computational power of the networks.