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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
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Periodic solutions in an inhibitory two-neuron network
Shangjiang Guo,Lihong Huang +1 more
TL;DR: In this article, the authors consider a delayed network of two neurons with self-feedback and interaction described by an all-or-none threshold function and show that the dynamics of the network can be understood in terms of the iterations of a one-dimensional map.
Journal ArticleDOI
Equilibrium analysis of cellular neural networks
Marco Gilli,Mario Biey,P. Checco +2 more
TL;DR: A set of sufficient conditions (and a simple algorithm for checking them) ensuring the existence of at least one stable equilibrium point are yielded, which give rise to simple constraints, that extend the class of CNNs, for which theexistence of a stable equilibrium Point is rigorously proved.
Journal ArticleDOI
New necessary and sufficient conditions for absolute stability of neural networks
Tianguang Chu,Cishen Zhang +1 more
TL;DR: The main result is based on a solvable Lie algebra condition, which generalizes existing results for symmetric and normal neural networks and demonstrates how to generate larger sets of weight matrices for absolute stability of the neural networks from known normal Weight matrices through simple procedures.
Journal ArticleDOI
Quantum-cnn to generate nanoscale chaotic oscillators
Luigi Fortuna,Domenico Porto +1 more
TL;DR: Coupled quantum-dot cells, which are usually used for Quantum-dots Cellular Automata (QCA), are considered to build Cellular Nonlinear Networks and it is shown how simple connection of few quantum- dot cells can cause the onset of chaotic oscillation only with small differences of polarizations and template between cells.
Journal ArticleDOI
Bio-Inspired Computer Fovea Model Based on Hexagonal-Type Cellular Neural Network
Chao-Hui Huang,Chin-Teng Lin +1 more
TL;DR: This study proposes a computer fovea model based on hexagonal-type cellular neural network (hCNN), which certain biological mechanisms of a retina can be simulated using an in-state-of-art architecture named CNN.
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.