Towards Statistically Valid Population Decoding Models
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- Dr Peter Andras
- Dr Stefano Panzeri
- Professor Malcolm Young
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| Author(s) | | Andras P, Panzeri S, Young MP |
| Publication type | | Article |
| 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 |
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| Full text for this publication is not currently held within this repository. Alternative links are provided below where available. |
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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. |
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| Publisher | | Elsevier |
| URL | | http://dx.doi.org/10.1016/S0925-2312(02)00349-1 |
| DOI | | 10.1016/S0925-2312(02)00349-1 |
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