Abstract

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.

Author affiliations

1. Oxford Centre for Computational Neuroscience, Oxford, UK.
2. Computational Neuroscience, Universitat Pompeu Fabra, 08003 Barcelona, Spain.
3. Institució Catalana de Recerca i Estudis Avançats, 08010 Barcelona, Spain.
4. Laboratory of Neuropsychiatric Research, Department of Psychiatry, Heinrich-Heine University, 40629 Duesseldorf, Germany.
5. Institute of Neurosciences and Biophysics, Juelich Research Centre, 52425 Juelich, Germany.

Correspondence to: Edmund T. Rolls¹ Email: Edmund.Rolls@oxcns.org

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