期刊
NATURE REVIEWS NEUROSCIENCE
卷 9, 期 9, 页码 696-709出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/nrn2462
关键词
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资金
- European Union [EC005-024]
- Spanish Research Project [BFU2007-61710/BFI]
- CONSOLIDER INGENIO
- German Research Foundation (Deutsche Forschungsgemeinschaft) [Wi 1316/2-1, Wi1316/2-2]
- Fogarty Foundation
- Oxford McDonnell Centre for Cognitive Neuroscience
- Boehringer Ingelheim Fonds
- ICREA Funding Source: Custom
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.
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