Edmund T Rolls,
Oxford Centre for Computational Neuroscience, Oxford, UK and
University of Warwick, Department of Computer Science, Coventry, UK
A computational neuroscience approach to the symptoms, mechanisms of, and treatments for schizophrenia and obsessive-compulsive disorder is described. The approach is based on a stochastic neurodynamical framework in which the stability of attractor networks in the brain is analyzed. The stability is influenced by statistical fluctuations in populations of neurons caused by the neuronal spiking time randomness for a given mean firing rate. The stability of the high firing rate attractor state which implements effects such as short-term memory and attention is increased if the firing rates are sufficiently high to dominate the spiking related noise. The stability of the low, spontaneous, firing rate state in the absence of input must also be maintained, and GABA-mediated inhibition is important for this.
In schizophrenia, the approach suggests that 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.
For obsessive-compulsive disorder the approach suggests that an increased depth in the basins of attraction of attractor neuronal network states in the brain makes each state too stable, so that it tends to remain locked in that state, and can not easily be moved on to another state.