A computational neuroscience approach to schizophrenia and its onset

Computational neuroscience integrate-and-fire attractor network models can be used to understand the factors that alter the stability of cortical networks in the face of noise caused for example by neuronal spiking times. A reduction of the firing rates of cortical neurons caused for example by reduced NMDA receptor function (present in schizophrenia) can lead to instability of the high firing rate attractor states that normally implement short-term memory and attention, contributing to the cognitive and negative symptoms of schizophrenia. Reduced cortical inhibition caused by a reduction of GABA neurotransmission (present in schizophrenia) can lead to instability of the spontaneous firing states of cortical networks, leading to a noise-induced jump to a high firing rate attractor state even in the absence of external inputs, contributing to the positive symptoms of schizophrenia. We consider how effects occurring at the time of late adolescence including synaptic pruning, decreases in grey matter volume, and changes in GABA-mediated inhibition and dopamine may contribute to the onset in some individuals of schizophrenia at this time.