Towards Statistically Valid Population Decoding Models

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  2. Dr Peter Andras
  3. Dr Stefano Panzeri
  4. Professor Malcolm Young
Author(s)Andras P, Panzeri S, Young MP
Publication type Article
JournalNeurocomputing: Special Issue on Computational Neuroscience, Trends in Research
ISSN (print)0925-2312
ISSN (electronic)1872-8286
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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.
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