Statistical Fluctuations in Attractor Networks Related to Schizophrenia

Abstract

We present a hypothesis of how the positive, negative, and cognitive symptoms of schizophrenia could be related to alterations in the stability of cortical networks which lead to a reduced signal-to-noise ratio. We analyze using integrate-and-fire simulations of attractor networks how some of the symptoms of schizophrenia could be related to a reduced depth of basins of attraction, produced by for example a decrease in the NMDA receptor conductances, and to statistical fluctuations caused by stochastic spike firing of neurons. Both of these processes contribute to instability in short term memory, attentional, and semantic neuronal networks. The cognitive symptoms such as distractibility, working memory deficits or poor attention could be caused by this instability of attractor states in prefrontal cortical networks. Lower firing rates are also produced, and in the orbitofrontal and anterior cingulate cortex could account for the negative symptoms including a reduction of emotions. If the decrease in NMDA conductances, and the statistical fluctuations, are combined with a reduction of GABA conductances, this causes the network to switch between the attractor states, and to jump from spontaneous activity into one of the attractors. We relate this to the positive symptoms of schizophrenia including delusions, paranoia, and hallucinations, which may arise because the basins of attraction are shallow and there is instability in temporal lobe semantic memory networks, leading thoughts to move too freely round the attractor energy landscape.