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
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Proceedings ArticleDOI
Overview of CNN research: 25 years history and the current trends
TL;DR: The history of the CNN research is summarized, the current activities in this field are overviewed and other areas are discussed which were fostered by the results of the 25 years of CNN research: memristor architectures and processing, spin-torque oscillator architectures, many-core FPGA processing and industrial vision chips.
Journal ArticleDOI
Global asymptotic stability of cellular neural networks with unequal delays: LMI approach
TL;DR: In this paper, a criterion for the global asymptotic stability and uniqueness of the equilibrium point of cellular neural networks with unequal delays is presented, which is computationally efficient, since it is in the form of linear matrix inequality.
Application of Feedforward Neural Networks to
TL;DR: Methods for identification and control of dynamical systems by adalines, two-layer, and three-layer feedforward neural networks using generalized weight adaptation algorithms are discussed and the type of nonlin- ear activation functions present in the neurons and in the weight adaptation algorithm have on F" system dynamics identification performance are investigated.
Journal ArticleDOI
Associative Learning of Integrate-and-Fire Neurons with Memristor-Based Synapses
TL;DR: This paper presents a class of memristor-based neural circuits comprising leaky integrate-and-fire (I & F) neurons and memristOr-based learning synapses and corresponding SPICE models, showing the properties of a two neurons network to be similar to biology.
Journal ArticleDOI
Fourier optical realization of cellular neural networks
TL;DR: An optical CNN implementation is suggested because optical processors are perfectly suited for both space invariant signal processing and complete interconnections between all elements.
References
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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.