We will discuss two types of neural modeling that, used in conjuction with functional brain imaging data (fMRI, MEG), can help elucidate the neural bases of human cognitive function, including language processing.  One type of modeling attempts to determine the brain network interactions that mediate specific cognitive processes. The second type simulates different types of neural data at multiple spatiotemporal scales. To illustrate the latter, we will discuss a large-scale, neurobiologically realistic network model of auditory and visual pattern recognition that relates neuronal dynamics to fMRI and MEG data. Areas included in the model extend from primary sensory cortex to prefrontal cortex.  The electrical activities of the model neuronal units were constrained to agree with data from the neurophysiological literature. An fMRI experiment using stimuli and tasks similar to those used in our simulations was performed.  The regional integrated synaptic activities of the model were used to determine simulated regional fMRI activities, and generally agreed with the experimentally observed fMRI data. Preliminary results also suggest that our model can be used to simulate MEG data.  Our results demonstrate that the model is capable of exhibiting the salient features of both electrophysiological neuronal activities and fMRI and MEG values that are in agreement with empirically observed data.