On the Storage Capacity of the Hopfield Model

Author(s):  
Matthias Löwe
Author(s):  
Viola Folli ◽  
Marco Leonetti ◽  
Giancarlo Ruocco

1992 ◽  
Vol 69 (3-4) ◽  
pp. 597-627 ◽  
Author(s):  
Anton Bovier ◽  
V�ronique Gayrard

2018 ◽  
Vol 2018 ◽  
pp. 1-5 ◽  
Author(s):  
Masaki Kobayashi

A twin-multistate quaternion Hopfield neural network (TMQHNN) is a multistate Hopfield model and can store multilevel information, such as image data. Storage capacity is an important problem of Hopfield neural networks. Jankowski et al. approximated the crosstalk terms of complex-valued Hopfield neural networks (CHNNs) by the 2-dimensional normal distributions and evaluated their storage capacities. In this work, we evaluate the storage capacities of TMQHNNs based on their idea.


2004 ◽  
Vol 15 (03) ◽  
pp. 393-401
Author(s):  
BURCU AKCAN ◽  
YİĞİT GÜNDÜÇ

The storage capacity of the extremely diluted Hopfield Model is studied by using Monte Carlo techniques. In this work, instead of diluting the synapses according to a given distribution, the dilution of the synapses is obtained systematically by retaining only the synapses with dominant contributions. It is observed that by using the prescribed dilution method the critical storage capacity of the system increases with decreasing number of synapses per neuron reaching almost the value obtained from mean-field calculations. It is also shown that the increase of the storage capacity of the diluted system depends on the storage capacity of the fully connected Hopfield Model and the fraction of the diluted synapses.


1987 ◽  
Vol 01 (01) ◽  
pp. 51-68 ◽  
Author(s):  
M.V. FEIGELMAN ◽  
L.B. IOFFE

The asymmetric modification of the Hopfield model of the associative memory is considered. It is shown that the asymmetry does not change the main properties of the model, but leads to the internal nonthermal noise. The modification of the Hopfield algorithm is proposed which can be used for storing the correlated patterns and its storage capacity is estimated. The hierarchical memory model is proposed and studied.


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