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Toward statistically valid population decoding models
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DOI：10.1016/S0925-2312(02)00349-1.png)
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Abstract
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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. (C) 2002 Elsevier Science B.V. All rights reserved.
Keywords:
Bayesian networks
category decoding
information
population code
Journal
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6.5
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2.5W
Citations:
6.5W
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