Towards statistically valid population decoding models

Peter Andras, Stefano Panzeri, Malcolm P. Young

    Research output: Contribution to journalArticlepeer-review

    Abstract

    We focus in this paper on the methodology of building statistically valid population code read-out models for spike train data. A new method is explored, which uses Bayesian networks to formalize the read-out model, Monte Carlo validation to check the statistical validity of the model and scrambled quasi-random vectors to speed up the validation process. This procedure avoids imposing usual additional constraints on the data. We present the method through an application in the context of non-metric categorical vision-related data. © 2002 Elsevier Science B.V. All rights reserved.
    Original languageEnglish
    Pages (from-to)269-274
    Number of pages5
    JournalNeurocomputing
    Volume44-46
    DOIs
    Publication statusPublished - 2002

    Keywords

    • Bayesian networks
    • Category decoding
    • Information
    • Population code

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