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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Exponential stability analysis of travelling waves solutions for nonlinear delayed cellular neural networks
TL;DR: In this article, the exponential stability of travelling wave solutions for nonlinear cellular neural networks with distribute delays in the lattice is studied and the weighted energy method and comparison principle are employed to derive the sufficient conditions under which the networks proposed are exponentially stable.
Journal ArticleDOI
Convergence and Multistability of Nonsymmetric Cellular Neural Networks With Memristors
TL;DR: The main result is that convergence holds when there are (possibly) nonsymmetric, non-negative interconnections between cells and an irreducibility assumption is satisfied, similar to the classic convergence result for standard (S)-CNNs with positive cell-linking templates.
Journal ArticleDOI
Deep and Shallow Architecture of Multilayer Neural Networks
TL;DR: This paper provides a systematic methodology to indicate when two hidden spaces are topologically conjugated and some criteria are presented for some specific cases.
Journal ArticleDOI
Sufficient conditions for the existence of global random attractors for stochastic lattice dynamical systems and applications
Caidi Zhao,Shengfan Zhou +1 more
TL;DR: These sufficient conditions provide a convenient approach to obtain an upper bound of Kolmogorov e-entropy for the global random attractor and the abstract result is applied to the stochastic lattice sine-Gordon equation.
References
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Journal ArticleDOI
Neural networks and physical systems with emergent collective computational abilities
TL;DR: A model of a system having a large number of simple equivalent components, based on aspects of neurobiology but readily adapted to integrated circuits, produces a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size.
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Self Organization And Associative Memory
TL;DR: The purpose and nature of Biological Memory, as well as some of the aspects of Memory Aspects, are explained.
Journal ArticleDOI
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.