In spite of extensive research on the biological and behavioral correlates of dopamine (DA) function, little is known about the mechanisms by which DA may produce the cognitive deficits observed in schizophrenia. Neural network simulation models offer a framework for exploring how DA effects at the physiological level may influence behavior. We focus on findings suggesting that DA modulates neuronal activity by enhancing the ability of neurons to transmit signals and reduce distortion by noise. These phenomena can be captured in simulations by increasing the gain of individual units of a network. When gain is increased diffusely, improvement in signal detection performance of such a network parallels the improvement in performance of human subjects receiving DA agonists. Furthermore, decreasing gain in a network module supporting a memory function analogous to that of prefrontal cortex results in deterioration of performance equivalent to that of schizophrenic subjects. A test of predictions made by such networks about the performance of schizophrenic patients in a new variant of the Continuous Performance Test is discussed. Preliminary data are consistent with predictions and support the account of schizophrenic cognitive deficits in terms of the theory of DA function suggested by the models.
All Science Journal Classification (ASJC) codes
- Psychiatry and Mental health