Publication:
Towards Statistically Valid Population Decoding Models (2002)
Author(s): Andras P, Panzeri S, Young MP
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.
- Date: 22-03-2002
- Journal: Neurocomputing: Special Issue on Computational Neuroscience, Trends in Research
- Volume: 44-46
- Issue: 1-2
- Pages: 269-274
- Publisher: Elsevier
- Publication type: Article
- Bibliographic status: Published
- URL: http://dx.doi.org/10.1016/S0925-2312(02)00349-1
- DOI: 10.1016/S0925-2312(02)00349-1
- ISSN (electronic): 1872-8286
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Dr Peter Andras
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