Neural Networks Theory


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Cellular neural networks: theory | IEEE Journals & Magazine | IEEE ...

Cellular Neural Networks: Theory | IEEE Journals & Magazine | IEEE ...

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.< > Cellular neural networks: theory. Abstract: A novel class of information-processing systems called cellular neural networks is proposed. Like neural ... [ReadMore..]

Theory, Operation, and Application of Neural Networks

Theory, Operation, And Application Of Neural Networks

May 26, 2018 ... Theory behind machine learning is broken up into three approaches; rule-based, Bayesian, and neural networks. Operation of machine learning ... [ReadMore..]

Nonequilibrium landscape theory of neural networks | PNAS

Nonequilibrium Landscape Theory Of Neural Networks | PNAS

The brain map project aims at mapping out human brain neuron connections. Even with given wirings, the global and physical understandings of the funct... We developed a nonequilibrium landscape–flux theory for asymmetrically connected neural networks. We found the landscape topography is critical in ... [ReadMore..]

Cellular neural networks: Theory and circuit design - Nossek - 1992 ...

Cellular Neural Networks: Theory And Circuit Design - Nossek - 1992 ...

Cellular neural networks or CNNs are a novel neural network architecture introduced by Chua and Yang which is very general and flexible, has some important properties desirable for design application... Abstract Cellular neural networks or CNNs are a novel neural network architecture introduced by Chua and Yang which is very general and flexible, ... [ReadMore..]

Dynamic recurrent neural networks: Theory and applications | IEEE ...

Dynamic Recurrent Neural Networks: Theory And Applications | IEEE ...

This special issue illustrates both the scientific trends of the early work in recurrent neural networks, and the mathematics of training when at least some recurrent terms of the network derivatives can be non-zero. Herein is a brief description of each of the papers. We have organized this description into two parts. The first part contains the papers that are mainly theoretical, and the second part contains the papers that are mainly applications. The order of papers is alphabetical by first author. This special issue illustrates both the scientific trends of the early work in recurrent neural networks, and the mathematics of training when at least some ... [ReadMore..]

Theory of Gating in Recurrent Neural Networks

Theory Of Gating In Recurrent Neural Networks

The success of recurrent neural networks owes much to gating, a multiplicative interaction that controls the flow of information. New models lead to a comprehensive theory of gating that can help engineers and neuroscientists. Jan 18, 2022 ... Recurrent neural networks (RNNs) are powerful dynamical models, widely used in machine learning (ML) and neuroscience. Prior theoretical ... [ReadMore..]

New Theory Cracks Open the Black Box of Deep Neural Networks ...

New Theory Cracks Open The Black Box Of Deep Neural Networks ...

A new idea called the “information bottleneck” is helping to explain the puzzling success of today’s artificial-intelligence algorithms—and might also explain how human brains learn. Oct 8, 2017 ... Like a brain, a deep neural network has layers of neurons—artificial ones that are figments of computer memory. When a neuron fires, it sends ... [ReadMore..]

Neural Networks Theory | SpringerLink

Neural Networks Theory | SpringerLink

"Neural Networks Theory is a major contribution to the neural networks literature. It is a treasure trove that should be mined by the thousands of researchers ... [ReadMore..]

Quaternion Spiking and Quaternion Quantum Neural Networks ...

Quaternion Spiking And Quaternion Quantum Neural Networks ...

Biological evidence shows that there are neural networks specialized for recognition of signals and patterns acting as associative memories. The spiking neural networks are another kind which receive input from a broad range of other brain areas to produce output that selects particular cognitive or … Sep 16, 2020 ... Quaternion Spiking and Quaternion Quantum Neural Networks: Theory and Applications. Int J Neural Syst. 2021 Feb;31(2):2050059. doi: ... [ReadMore..]

The pages related to neural networks theory are also listed below:

    Neural network - Wikipedia

    Neural Network - Wikipedia

    Thus, a neural network is either a biological neural network, made up of biological neurons, or an artificial neural network, used for solving artificial ... [ReadMore..]

    Theory of Graph Neural Networks: Representation and Learning

    Theory Of Graph Neural Networks: Representation And Learning

    Graph Neural Networks (GNNs), neural network architectures targeted to learning representations of graphs, have become a popular learning model for prediction tasks on nodes, graphs and configurations of points, with wide success in practice. This article summarizes a selection of the emerging theoretical results on approximation and learning properties of widely used message passing GNNs and higher-order GNNs, focusing on representation, generalization and extrapolation. Along the way, it summarizes mathematical connections. Apr 16, 2022 ... Abstract: Graph Neural Networks (GNNs), neural network architectures targeted to learning representations of graphs, have become a popular ... [ReadMore..]