Toward statistically valid population decoding models

Andras, Peter; Panzeri, Stefano; Young, Malcolm P

Abstract

8We 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.

Citation

Andras, P., Panzeri, S., & Young, M. P. (2002). Toward statistically valid population decoding models. Neurocomputing, 44, 269-274. https://doi.org/10.1016/S0925-2312%2802%2900349-1

• Journal Article Type: Article
• Online Publication Date: Mar 22, 2002
• Publication Date: 2002-06
• Deposit Date: Nov 4, 2021
• Journal: Neurocomputing
• Print ISSN: 0925-2312
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 44
• Pages: 269-274
• DOI: https://doi.org/10.1016/S0925-2312%2802%2900349-1
• Keywords: Bayesian networks, Category decoding, Information, Population code
• Public URL: http://researchrepository.napier.ac.uk/Output/2809077