Toward statistically valid population decoding models

Péter András

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

2002-06-01

PRE

Péter András *

Stefano Panzeri

Malcolm P. Young

DOI：10.1016/S0925-2312(02)00349-1

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摘要

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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.

Keyword:

Bayesian networks

category decoding

information

population code