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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Proceedings ArticleDOI
Robust CNN templates: theory and simulations
TL;DR: This work presents design approaches applicable to several CNN processing tasks, which guarantee the correct operation of the network even when the template and data values are subjected to some random variations.
Journal ArticleDOI
Existence and Exponential Stability of Periodic Solution to Fuzzy Cellular Neural Networks with Distributed Delays
TL;DR: Some sufficient conditions for the existence and global exponential stability of periodic solution of such fuzzy cellular neural networks with distributed delays are established by using Gaines and Mawhin’s continuation theorem of coincidence degree theory and the method of Lyapunov function.
Journal ArticleDOI
Convergence and attractivity of memristor-based cellular neural networks with time delays
Sitian Qin,Jun Wang,Xiaoping Xue +2 more
TL;DR: It is shown that the state convergence time period is finite and can be quantitatively estimated using given parameters, and the positive invariance and attractivity of state in non-saturated regions are proven.
Journal ArticleDOI
Nanoarchitectonics for Heterogeneous Integrated Nanosystems
TL;DR: In this article, the authors discuss some possible methods forward in maintaining scaled metal-oxide semiconductor (CMOS) and going beyond the International Roadmap for Semiconductors (ITRS), and discuss potential device-level solutions that take advantage of new functional materials, self-assembly processes, low dissipation nanoscale devices.
Journal ArticleDOI
Spatial complexity in multi-layer cellular neural networks
TL;DR: This study gives sofic shift a realization through a realistic model, and a new phenomenon, the broken of symmetry of entropy, is discovered in multi-layer cellular neural networks with input.
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.