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Towards Statistically Valid Population Decoding Models

Lookup NU author(s): Dr Peter Andras, Dr Stefano Panzeri, Professor Malcolm Young


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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.

Publication metadata

Author(s): Andras P, Panzeri S, Young MP

Publication type: Article

Publication status: Published

Journal: Neurocomputing: Special Issue on Computational Neuroscience, Trends in Research

Year: 2002

Volume: 44-46

Issue: 1-2

Pages: 269-274

ISSN (print): 0925-2312

ISSN (electronic): 1872-8286

Publisher: Elsevier


DOI: 10.1016/S0925-2312(02)00349-1


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