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  Gaussian process methods for estimating cortical maps

Macke, J., Gerwinn, S., White, L., Kaschube, M., & Bethge, M. (2011). Gaussian process methods for estimating cortical maps. NeuroImage, 56(2), 570-581. doi:10.1016/j.neuroimage.2010.04.272.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BBAC-C Version Permalink: http://hdl.handle.net/21.11116/0000-0001-B7E3-0
Genre: Journal Article

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 Creators:
Macke, JH1, 2, Author              
Gerwinn, S1, 2, Author              
White, LW, Author
Kaschube, M, Author
Bethge, M1, 2, Author              
Affiliations:
1Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497805              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: A striking feature of cortical organization is that the encoding of many stimulus features, for example orientation or direction selectivity, is arranged into topographic maps. Functional imaging methods such as optical imaging of intrinsic signals, voltage sensitive dye imaging or functional magnetic resonance imaging are important tools for studying the structure of cortical maps. As functional imaging measurements are usually noisy, statistical processing of the data is necessary to extract maps from the imaging data. We here present a probabilistic model of functional imaging data based on Gaussian processes. In comparison to conventional approaches, our model yields superior estimates of cortical maps from smaller amounts of data. In addition, we obtain quantitative uncertainty estimates, i.e. error bars on properties of the estimated map. We use our probabilistic model to study the coding properties of the map and the role of noise-correlations by decoding the stimulus from single trials of an imaging experiment.

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 Dates: 2011-05
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1016/j.neuroimage.2010.04.272
BibTex Citekey: 6516
 Degree: -

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Title: NeuroImage
Source Genre: Journal
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Publ. Info: Orlando, FL : Academic Press
Pages: - Volume / Issue: 56 (2) Sequence Number: - Start / End Page: 570 - 581 Identifier: ISSN: 1053-8119
CoNE: https://pure.mpg.de/cone/journals/resource/954922650166