Linked Life Data

20472075

Source:http://linkedlifedata.com/resource/pubmed/id/20472075

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2011-4-25
pubmed:abstractText
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.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
1095-9572
pubmed:author
pubmed:copyrightInfo
Copyright © 2010 Elsevier Inc. All rights reserved.
pubmed:issnType
Electronic
pubmed:day
15
pubmed:volume
56
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
570-81
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
Gaussian process methods for estimating cortical maps.
pubmed:affiliation
Gatsby Computational Neuroscience Unit, University College London, London, UK. jakob@gatsby.ucl.ac.uk
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural