Nonlinear Factorization in Hopfield-like Neural Networks.
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F01%3A06010177" target="_blank" >RIV/67985807:_____/01:06010177 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Nonlinear Factorization in Hopfield-like Neural Networks.
Original language description
The problem in binary factorization of complex patterns in recurrent Hopfield-like neural network was studied by means of computer simulation. The network ability to perform a factorization was analyzed depending on the number and sparseness of factors mixed in presented patterns. Binary factorization in sparsely encoded Hopfield-like neural network is treated as efficient statistical method and as a functional model of hippocampal CA3 field.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
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Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2001
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Digital Signal Processing and Multimedia Communications.
ISBN
80-89061-49-4
ISSN
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e-ISSN
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Number of pages
4
Pages from-to
98-101
Publisher name
Mercury Smékal Publishing House
Place of publication
Košice
Event location
Košice [SK]
Event date
Nov 27, 2001
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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