Computational models of schizophrenia and dopamine modulation in the prefrontal cortex
Rolls, Edmund T., Loh, Marco, Deco, Gustavo and Winterer, Georg. (2008) Computational models of schizophrenia and dopamine modulation in the prefrontal cortex. Nature Reviews Neuroscience, Vol.9 (No.9). pp. 696-709. ISSN 1471-003XFull text not available from this repository.
Official URL: http://dx.doi.org/10.1038/nrn2462
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 (g-aminobutyric acid) and dopamine receptors is connected to the concepts of noise and variability, and to related neurophysiological findings and clinical symptoms in schizophrenia.
|Item Type:||Journal Article|
|Subjects:||Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software|
|Divisions:||Faculty of Science > Computer Science|
|Journal or Publication Title:||Nature Reviews Neuroscience|
|Publisher:||Nature Publishing Group|
|Page Range:||pp. 696-709|
|Access rights to Published version:||Restricted or Subscription Access|
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