A model of working memory: bridging the gap between electrophysiology and human brain imaging

Human neuroimaging methods such as positron emission tomography and functional magnetic resonance imaging have made possible the study of large-scale distributed networks in the behaving human brain. Although many imaging studies support and extend knowledge gained from other experimental modalities such as animal single-cell recordings, there have also been a substantial number of experiments that appear to contradict the animal studies. Part of the reason for this is that neuroimaging is an indirect measure of neuronal firing activity, and thus interpretation is difficult. Computational modeling can help to bridge the gap by providing a substrate for making explicit the assumptions and constraints provided from other sources such as anatomy, physiology and behavior. We describe a large-scale model of working memory that we have used to examine a number of issues relating to the interpretation of imaging data. The gating mechanism that regulates engagement and retention of short-term memory is revised to better reflect hypothesized underlying neuromodulatory mechanisms. It is shown that in addition to imparting better performance for the memory circuit, this mechanism also provides a better match to imaging data from working memory studies.