Linked Life Data

18714326

Source:http://linkedlifedata.com/resource/pubmed/id/18714326

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
9
pubmed:dateCreated
2009-1-14
pubmed:abstractText
Computational neuroscience models can be used to understand the diminished stability and noisy neurodynamical behaviour of prefrontal cortex networks in schizophrenia. These neurodynamical properties can be captured by simulated neural networks with randomly spiking neurons that introduce noise into the system and produce trial-by-trial variation of postsynaptic potentials. Theoretical and experimental studies have aimed to understand schizophrenia in relation to noise and signal-to-noise ratio, which are promising concepts for understanding the symptoms that characterize this heterogeneous illness. Simulations of biologically realistic neural networks show how the functioning of NMDA (N-methyl-D-aspartate), GABA (gamma-aminobutyric acid) and dopamine receptors is connected to the concepts of noise and variability, and to related neurophysiological findings and clinical symptoms in schizophrenia.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Sep
pubmed:issn
1471-003X
pubmed:author
pubmed:issnType
Print
pubmed:volume
9
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
696-709
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Computational models of schizophrenia and dopamine modulation in the prefrontal cortex.
pubmed:affiliation
Oxford Centre for Computational Neuroscience, Oxford, UK. Edmund.Rolls@oxcns.org
pubmed:publicationType
Journal Article, Review, Research Support, Non-U.S. Gov't