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231222s1999 xx ||||| 00| ||eng c |
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|e rakwb
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|a eng
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| 100 |
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|a Krebs, P R
|e verfasserin
|4 aut
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|a Categorization in the symmetrically dilute Hopfield network
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|c 1999
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|a Text
|b txt
|2 rdacontent
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|a ohne Hilfsmittel zu benutzen
|b n
|2 rdamedia
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|a Band
|b nc
|2 rdacarrier
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|a Date Completed 16.07.2002
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|a Date Revised 28.07.2019
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|a published: Print
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|a Citation Status MEDLINE
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|a A symmetrically dilute Hopfield model with a Hebbian learning rule is used to study the effects of gradual dilution and of synaptic noise on the categorization ability of an attractor neural network with hierarchically correlated patterns in a two-level structure of ancestors and descendants. Categorization consists in recognizing the ancestors when the network has been trained exclusively with the descendants. We consider a macroscopic number of ancestors, each with a finite number of descendants, and take into account the stochastic noise produced by the former in an equilibrium study of the network, by means of replica-symmetric mean-field theory. Phase diagrams are obtained that exhibit a categorization, a spin-glass, and a paramagnetic phase, as well as the dependence of the order parameters on the relevant quantities. The de Almeida-Thouless lines that limit the validity of the replica-symmetric results are also obtained. It is shown that gradual dilution increases considerably the region where a stable categorization phase may be found
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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|a Theumann, W K
|e verfasserin
|4 aut
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|i Enthalten in
|t Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics
|d 1993
|g 60(1999), 4 Pt B vom: 30. Okt., Seite 4580-7
|w (DE-627)NLM098226002
|x 1063-651X
|7 nnns
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| 773 |
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|g volume:60
|g year:1999
|g number:4 Pt B
|g day:30
|g month:10
|g pages:4580-7
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|d 60
|j 1999
|e 4 Pt B
|b 30
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|h 4580-7
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