Kernel methods on spike train space for neuroscience: A tutorial

Il Memming Park, Sohan Seth, Lin Li, Jose C. Principe

    Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

    38 Sitaatiot (Scopus)

    Abstrakti

    Over the last decade, several positive-definite kernels have been proposed to treat spike trains as objects in Hilbert space. However, for the most part, such attempts still remain a mere curiosity for both computational neuroscientists and signal processing experts. This tutorial illustrates why kernel methods can, and have already started to, change the way spike trains are analyzed and processed. The presentation incorporates simple mathematical analogies and convincing practical examples in an attempt to show the yet unexplored potential of positive definite functions to quantify point processes. It also provides a detailed overview of the current state of the art and future challenges with the hope of engaging the readers in active participation.

    AlkuperäiskieliEnglanti
    Artikkeli6530726
    Sivut149-160
    Sivumäärä12
    JulkaisuIEEE Signal Processing Magazine
    Vuosikerta30
    Numero4
    DOI - pysyväislinkit
    TilaJulkaistu - 2013
    OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

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