| pubmed-article:19409272 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:19409272 | lifeskim:mentions | umls-concept:C0086418 | lld:lifeskim |
| pubmed-article:19409272 | lifeskim:mentions | umls-concept:C0035647 | lld:lifeskim |
| pubmed-article:19409272 | lifeskim:mentions | umls-concept:C0347997 | lld:lifeskim |
| pubmed-article:19409272 | lifeskim:mentions | umls-concept:C0042817 | lld:lifeskim |
| pubmed-article:19409272 | lifeskim:mentions | umls-concept:C0524466 | lld:lifeskim |
| pubmed-article:19409272 | lifeskim:mentions | umls-concept:C0449243 | lld:lifeskim |
| pubmed-article:19409272 | lifeskim:mentions | umls-concept:C0456962 | lld:lifeskim |
| pubmed-article:19409272 | lifeskim:mentions | umls-concept:C1533716 | lld:lifeskim |
| pubmed-article:19409272 | lifeskim:mentions | umls-concept:C1521738 | lld:lifeskim |
| pubmed-article:19409272 | pubmed:issue | 2 | lld:pubmed |
| pubmed-article:19409272 | pubmed:dateCreated | 2009-5-4 | lld:pubmed |
| pubmed-article:19409272 | pubmed:abstractText | The difficulty of visual recognition stems from the need to achieve high selectivity while maintaining robustness to object transformations within hundreds of milliseconds. Theories of visual recognition differ in whether the neuronal circuits invoke recurrent feedback connections or not. The timing of neurophysiological responses in visual cortex plays a key role in distinguishing between bottom-up and top-down theories. Here, we quantified at millisecond resolution the amount of visual information conveyed by intracranial field potentials from 912 electrodes in 11 human subjects. We could decode object category information from human visual cortex in single trials as early as 100 ms poststimulus. Decoding performance was robust to depth rotation and scale changes. The results suggest that physiological activity in the temporal lobe can account for key properties of visual recognition. The fast decoding in single trials is compatible with feedforward theories and provides strong constraints for computational models of human vision. | lld:pubmed |
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| pubmed-article:19409272 | pubmed:language | eng | lld:pubmed |
| pubmed-article:19409272 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:19409272 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:19409272 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:19409272 | pubmed:month | Apr | lld:pubmed |
| pubmed-article:19409272 | pubmed:issn | 1097-4199 | lld:pubmed |
| pubmed-article:19409272 | pubmed:author | pubmed-author:KreimanGabrie... | lld:pubmed |
| pubmed-article:19409272 | pubmed:author | pubmed-author:MadsenJoseph... | lld:pubmed |
| pubmed-article:19409272 | pubmed:author | pubmed-author:LiuHeshengH | lld:pubmed |
| pubmed-article:19409272 | pubmed:author | pubmed-author:AgamYigalY | lld:pubmed |
| pubmed-article:19409272 | pubmed:issnType | Electronic | lld:pubmed |
| pubmed-article:19409272 | pubmed:day | 30 | lld:pubmed |
| pubmed-article:19409272 | pubmed:volume | 62 | lld:pubmed |
| pubmed-article:19409272 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:19409272 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:19409272 | pubmed:pagination | 281-90 | lld:pubmed |
| pubmed-article:19409272 | pubmed:dateRevised | 2011-6-14 | lld:pubmed |
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