Recalling properties of non-random patterns in neural networks A Monte-Carlo study

We introduce a neural network with the ability of recalling p non-random patterns displaying a hierarchical distribution of activities for all p⩽ N − 1, N being the number of neurons. The stability of the retrieval states is studied as a function of temperature T and α = p/ N. The temperature below...

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Bibliographic Details
Published in:Physica A 1998-01, Vol.248 (3), p.235-246
Main Authors: Miranda, L.C., Riera, R.
Format: Article
Language:English
Subjects:
Neural networks
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Summary:We introduce a neural network with the ability of recalling p non-random patterns displaying a hierarchical distribution of activities for all p⩽ N − 1, N being the number of neurons. The stability of the retrieval states is studied as a function of temperature T and α = p/ N. The temperature below which the patterns are retrievable states has been determined by computer simulations. The features of the memory stability are related to a weak correlation of the synaptic efficacy distribution.
ISSN:0378-4371
1873-2119
DOI:10.1016/S0378-4371(97)00499-8