Linked Life Data

17521281

Source:http://linkedlifedata.com/resource/pubmed/id/17521281

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
7
pubmed:dateCreated
2007-5-24
pubmed:abstractText
With spatially organized neural networks, we examined how bias and noise inputs with spatial structure result in different network states such as bumps, localized oscillations, global oscillations, and localized synchronous firing that may be relevant to, for example, orientation selectivity. To this end, we used networks of McCulloch-Pitts neurons, which allow theoretical predictions, and verified the obtained results with numerical simulations. Spatial inputs, no matter whether they are bias inputs or shared noise inputs, affect only firing activities with resonant spatial frequency. The component of noise that is independent for different neurons increases the linearity of the neural system and gives rise to less spatial mode mixing and less bistability of population activities.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
0899-7667
pubmed:author
pubmed:issnType
Print
pubmed:volume
19
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1854-70
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Filtering of spatial bias and noise inputs by spatially structured neural networks.
pubmed:affiliation
Laboratory for Mathematical Neuroscience, RIKEN Brain Science Institute, Wako, Japan. masuda@mist.i.u-tokyo.ac.jp
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't