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
Cellular neural networks: local patterns for general templates
TL;DR: This study investigates the mosaic local patterns for cellular neural networks with general templates and demonstrates that the set of templates can be divided into many nite regions, and proposes algorithms for verifying the linear separability for a given family of local patterns.
Journal ArticleDOI
Universal perceptron and DNA-like learning algorithm for binary neural networks
TL;DR: Universal perceptron (UP), a generalization of Rosenblatt's perceptron, is considered in this paper, which is capable of implementing all Boolean functions (BFs) and in the classification of BFs.
Journal ArticleDOI
Influence of boundary conditions on the behavior of cellular neural networks
TL;DR: In this article, it is shown that the dynamical behavior of an important group of cellular neural networks strongly depends on these boundary conditions: some boundary conditions make the network stable whereas other make it unstable.
Journal ArticleDOI
Analysis and design of associative memories based on stability of cellular neural networks
TL;DR: In this paper, some criteria about the stability of CNNs are established and these criteria give some constraint conditions for the relationship of parameters ofCNNs.
Journal ArticleDOI
Fault-tolerant design of analogic CNN templates and algorithms-Part I: The binary output case
TL;DR: This paper addresses the issue of designing a class of fault-tolerant cellular neural network (CNN) templates that, combined with CNN analogic algorithms, work correctly and reliably on given CNN universal machine (CNN-UM) chips and proposes a generic method for finding nonpropagating binary-output CNN templates.
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.