Research Output
Toward statistically valid population decoding models
  8We 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.

  • Type:

    Article

  • Date:

    22 March 2002

  • Publication Status:

    Published

  • Publisher

    Elsevier

  • DOI:

    10.1016/S0925-2312(02)00349-1

  • ISSN:

    0925-2312

  • Funders:

    Historic Funder (pre-Worktribe)

Citation

Andras, P., Panzeri, S., & Young, M. P. (2002). Toward statistically valid population decoding models. Neurocomputing, 44, 269-274. https://doi.org/10.1016/S0925-2312%2802%2900349-1

Authors

Keywords

Bayesian networks, Category decoding, Information, Population code

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