ABSTRACT
Neural connectivity of the prefrontal cortex is essential to working memory. Reduction of prefrontal connectivity and abnormal prefrontal dopamine modulation are common characteristics associated with schizophrenia. Two experiments separately modeled the effects of exaggerated pruning and of synaptic depression to imitate schizophrenic performance in a prefrontal neural network. In the first model, effects of cortical pruning were simulated with a set of scale-free networks of neurons and compared with empirical results from the Sternberg working memory task. The second set of simulations were based on the synaptic theory of working memory. Simulations of this model measured memory duration in relation to synaptic facilitation and depression constants and in relation to the level of neural connectivity. In the first set of simulations, modulating levels of cortical pruning resulted in a gain or loss in accuracy and speed of memory recollection. In the second set of simulations, increased facilitation time constants and decreased inhibitory time constants resulting in longer memory durations, and overly connected networks resulted in very low memory durations. In the first model, the decline in memory performance can be attributed to the emergence of pathological memory behavior brought about by the warping of the basins of attraction. Collectively, the simulations demonstrate that a reduction of prefrontal cortical hubs can lead to schizophrenia like performance in neural networks, and may account for pathological working memory in the disorder.
