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General-Purpose Computation with Neural Networks: A Survey of Complexity Theoretic Results

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    SYSNO ASEP0404255
    Document TypeJ - Journal Article
    R&D Document TypeJournal Article
    Subsidiary JČlánek ve WOS
    TitleGeneral-Purpose Computation with Neural Networks: A Survey of Complexity Theoretic Results
    Author(s) Šíma, Jiří (UIVT-O) RID, SAI, ORCID
    Orponen, P. (FI)
    Source TitleNeural Computation - ISSN 0899-7667
    Roč. 15, č. 12 (2003), s. 2727-2778
    Number of pages50 s.
    Languageeng - English
    CountryUS - United States
    Keywordscomputational power ; computational complexity ; perceptrons ; radial basis functions ; spiking neurons ; feedforward networks ; reccurent networks ; probabilistic computation ; analog computation
    Subject RIVBA - General Mathematics
    R&D ProjectsIAB2030007 GA AV ČR - Academy of Sciences of the Czech Republic (AV ČR)
    GA201/02/1456 GA ČR - Czech Science Foundation (CSF)
    CEZ1030915
    UT WOS000186231300001
    EID SCOPUS10744230566
    DOI10.1162/089976603322518731
    AnnotationWe survey and summarize the literature on the computational aspects of neural network models by presenting a detailed taxonomy of the various models according to their complexity theoretic characteristics. The criteria of classification include the architecture of the network (feedforward versus recurrent), time model (discrete versus continuous), state type (binary versus analog), weight constraints (symmetric versus asymmetric), network size (finite nets versus infinite families), and computation type (deterministic vers probabilistic), among others. The underlying results concerning the computational power and complexity issues of perceptron, radial basis function, winner-take-all, and spiking neural networks are briefly surveyed, with pointers to the relevant literature. In our survey, we focus mainly on the digital computation whose inputs and outputs are binary in nature, although their values are quite often encoded as analog neuron states. We omit the important learning issu
    WorkplaceInstitute of Computer Science
    ContactTereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800
    Year of Publishing2004

Number of the records: 1  

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