Pair-correlated patterns in Hopfield model of neural networks

1991 ◽  
Vol 62 (1-2) ◽  
pp. 473-480 ◽  
Author(s):  
Francisco A. Tamarit ◽  
Evaldo M. F. Curado
Keyword(s):  
Neural Networks ◽  
Hopfield Model ◽  
Correlated Patterns

Storage capacity of neural networks storing spatially correlated patterns

1994 ◽  
Vol 49 (2) ◽  
pp. 1690-1698 ◽  
Author(s):  
Mathias Schlüter ◽  
Friedrich Wagner
Keyword(s):  
Neural Networks ◽  
Storage Capacity ◽  
Spatially Correlated ◽  
Correlated Patterns

Learning, Storing, and Disentangling Correlated Patterns in Neural Networks

2018 ◽  
pp. 506-514
Author(s):  
Xiaolong Zou ◽  
Zilong Ji ◽  
Xiao Liu ◽  
Tiejun Huang ◽  
Yuanyuan Mi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Correlated Patterns

scholarly journals Redes de neurônios

1992 ◽  
Vol 14 (14) ◽  
pp. 07
Author(s):  
Rita M. C. de Almeida
Keyword(s):  
Neural Networks ◽  
Spin Glass ◽  
Physical Problem ◽  
Information Storage ◽  
Hopfield Model ◽  
The Brain ◽  
Scientific Advances ◽  
The Way

In the last ten years many scientific advances regarding neurons and the way they are interconnected has mad o it possible to study the dynamics of storage and Processing of information in the brain. In particular, the physicist J. J. Hopfield proposed a formal minimalist model to these neural networks reducing the problem to a particular case of a well – defined physical problem – the spin glass. Although the problem í s well defined, its solution is far from being trivial.Here we introduce the problem, describe Hopfield model, with its achievements and limitations, and present our contribution to the description of information storage in neural networks.

Information and Topology in Attractor Neural Networks

2007 ◽  
Vol 19 (4) ◽  
pp. 956-973 ◽  
Author(s):  
D. Dominguez ◽  
K. Koroutchev ◽  
E. Serrano ◽  
F. B. Rodríguez
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mutual Information ◽  
Path Length ◽  
Basin Of Attraction ◽  
Small World ◽  
Hopfield Model ◽  
Optimal Topology ◽  
And Topology ◽  
Wide Range

A wide range of networks, including those with small-world topology, can be modeled by the connectivity ratio and randomness of the links. Both learning and attractor abilities of a neural network can be measured by the mutual information (MI) as a function of the load and the overlap between patterns and retrieval states. In this letter, we use MI to search for the optimal topology with regard to the storage and attractor properties of the network in an Amari-Hopfield model. We find that while an optimal storage implies an extremely diluted topology, a large basin of attraction leads to moderate levels of connectivity. This optimal topology is related to the clustering and path length of the network. We also build a diagram for the dynamical phases with random or local initial overlap and show that very diluted networks lose their attractor ability.

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