Linked Life Data

12929917

Source:http://linkedlifedata.com/resource/pubmed/id/12929917

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1-2
pubmed:dateCreated
2003-8-21
pubmed:abstractText
Many biological and artificial neural networks require the parallel extraction of multiple features, and meet this requirement with distinct populations of neurons that are selective to one property of the stimulus while being non-selective to another property. In this way, several populations can resolve a set of features independently of each other, and thus achieve a parallel mode of processing. This raises the question how an initially homogeneous population of neurons segregates into groups with distinct and complementary response properties. Using a colour image sequence recorded from a camera mounted on the head of a freely behaving cat, we train a network of neurons to achieve optimally stable responses, that is, responses that change minimally over time. This objective leads to the development of colour-selective neurons. Adding a second objective, decorrelating activity within the network, a subpopulation of neurons develops with achromatic response properties. Colour selective neurons tend to be non-oriented while achromatic neurons are orientation-tuned. The proposed objective thus successfully leads to the segregation of neurons into complementary populations that are either selective for colour or orientation.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
0334-1763
pubmed:author
pubmed:issnType
Print
pubmed:volume
14
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
43-52
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2003
pubmed:articleTitle
Learning distinct and complementary feature selectivities from natural colour videos.
pubmed:affiliation
Institute of Neuroinformatics (UNI/ETH Zürich), Zürich, Switzerland. weinhaeu@ini.phys.ethz.ch
pubmed:publicationType
Journal Article, Comparative Study, Research Support, Non-U.S. Gov't