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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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Journal ArticleDOI
Attitude control in walking hexapod robots: an analogic spatio-temporal approach
TL;DR: The whole control system of a biologically inspired walking robot can be structured as an analog control system realized by CNNs generating the locomotion pattern as a function of the sensorial stimuli from the environment.
Journal ArticleDOI
Existence and characterization of limit cycles in nearly symmetric neural networks
TL;DR: In this paper, the authors considered a class of neural networks, and gave a necessary and sufficient condition for the existence of Hopf bifurcations (HBs) at the equilibrium point at the origin, arbitrarily close to symmetry.
Journal ArticleDOI
Exponential stability and periodic solutions of delayed cellular neural networks
TL;DR: In this article, a set of criteria are presented for the global exponential stability and the existence of periodic solutions of delayed cellular neural networks (DCNNs) by constructing suitable Lyapunov functionals.
Journal ArticleDOI
Discrete-time CNN for image segmentation by active contours
TL;DR: A new image segmentation strategy which operates by means of active contours implemented on a multilayer cellular neural network, guided by external information from a contour which evolves until it reaches the final desired position in the image processed.
Journal ArticleDOI
Turing instability and pattern formation of neural networks with reaction–diffusion terms
TL;DR: In this article, a model for a network of neurons with reaction-diffusion is investigated, and the linear stability of the system, Hopf bifurcation and Turing unstable conditions are obtained.
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.