Kernel methods on spike train space for neuroscience: A tutorial

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

    Research output: Contribution to journalArticleScientificpeer-review

    29 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Article number6530726
    Pages (from-to)149-160
    Number of pages12
    JournalIEEE Signal Processing Magazine
    Volume30
    Issue number4
    DOIs
    Publication statusPublished - 2013
    MoE publication typeA1 Journal article-refereed

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