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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
Citations
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Efficient edge detection in digital images using a cellular neural network optimized by differential evolution algorithm
Alper Basturk,Enis Günay +1 more
TL;DR: Simulation results indicate that the proposed CNN operator outperforms competing edge detectors and offers superior performance in edge detection in digital images.
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Stability and dissipativity analysis of static neural networks with interval time-varying delay
TL;DR: A new augmented Lyapunov–Krasovskii functional is constructed and an improved stability criterion is derived for the considered neural networks, and a sufficient condition is established to assure the neural networks strictly dissipative.
Journal ArticleDOI
Periodic solutions and its exponential stability of reaction–diffusion recurrent neural networks with continuously distributed delays
TL;DR: In this article, the authors considered both exponential stability and periodic oscillatory solutions for reaction-diffusion recurrent neural networks with continuously distributed delays and provided sufficient conditions to ensure global exponential stability.
Journal ArticleDOI
An Introduction to Spin Wave Computing
Abdulqader Mahmoud,Florin Ciubotaru,Frederic Vanderveken,Andrii V. Chumak,Said Hamdioui,Christoph Adelmann,Sorin Cotofana +6 more
TL;DR: It is argued that spin-wave circuits need to be embedded in conventional CMOS circuits to obtain complete functional hybrid computing systems and the benchmark indicates that hybridspin-wave--CMOS systems promise ultralow-power operation and may ultimately outperform conventionalCMOS circuits in terms of the power-delay-area product.
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2013 Special Issue: Design of silicon brains in the nano-CMOS era: Spiking neurons, learning synapses and neural architecture optimization
TL;DR: This work presents a design framework for neuromorphic architectures in the nano-CMOS era and demonstrates the validity of the design methodology through the implementation of cortical development in a circuit of spiking neurons, STDP synapses, and neural architecture optimization.
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.
Book
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.