Integrating electrophysiological and anatomical experimental data to create a large-scale model that simulates a delayed match-to-sample human brain imaging study

We propose a model that draws together experimental evidence from anatomical, electrophysiological and imaging experiments in order to understand better the neural substrate of human imaging studies using positron electron tomography (PET) and functional magnetic resonance imaging (fMRI). First, we define a simple local circuit that reflects the major role that local connectivity plays in producing PET and fMRI data, which are thought to mainly reflect synaptic activity. Second, in order to account for the role of varying behaviors during the course of a typical imaging experiment, we propose a local circuit that can perform a delayed match-to-sample task. The elements of this circuit behave very much like neurons that have been found in the prefrontal cortex during similar tasks in monkeys. One subpopulation responds selectively only when stimuli are present. Two different populations show the two types of delay-period activity that have been identified, one with high activity both during the cue and the delay period, the other with a rise during the delay period only. Last, a subpopulation shows a brief response only if the second stimulus matches the first, thus mediating the decision about whether the stimuli match. We show that in addition to performing the task, the integrated summed synaptic activities of the model are similar to experimental PET data.