Linked Life Data

19257076

Source:http://linkedlifedata.com/resource/pubmed/id/19257076

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1 Pt 1
pubmed:dateCreated
2009-3-4
pubmed:abstractText
Many neurons display intrinsic interspike interval correlations in their spike trains. However, the effects of such correlations on information transmission in neural populations are not well understood. We quantified signal processing using linear response theory supported by numerical simulations in networks composed of two different models: One model generates a renewal process where interspike intervals are not correlated while the other generates a nonrenewal process where subsequent interspike intervals are negatively correlated. Our results show that the fractional rate of increase in information rate as a function of network size and stimulus intensity is lower for the nonrenewal model than for the renewal one. We show that this is mostly due to the lower amount of effective noise in the nonrenewal model. We also show the surprising result that coupling has opposite effects in renewal and nonrenewal networks: Excitatory (inhibitory coupling) will decrease (increase) the information rate in renewal networks while inhibitory (excitatory coupling) will decrease (increase) the information rate in nonrenewal networks. We discuss these results and their applicability to other classes of excitable systems.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
1539-3755
pubmed:author
pubmed:issnType
Print
pubmed:volume
79
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
011914
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Noise shaping in neural populations.
pubmed:affiliation
Department of Physics, Centre for Nonlinear Dynamics in Phyiology and Medicine, McGill University, 3655 Sir William Osler, Montréal, Québec, Canada, H3G-1Y6.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't