Predicting human functional maps with neural net modeling

Formidable difficulties exist in interpreting positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) hemodynamic signals in terms of the underlying neural activity. These include issues of spatial and temporal resolution and problems relating neuronal activity (i.e., action potentials) measured in nonhuman studies by single unit electrodes to hemodynamic measurements reflecting synaptic activity. Also, regional hemodynamic measurements correspond to a mixture of local and afferent synaptic activity. To surmount these difficulties, we propose using large-scale neurobiologically realistic models in which data at various spatial and temporal levels can be simulated and cross-validated by multiple disciplines, including functional neuroimaging. A delayed match-to-sample visual task is used to illustrate this approach.