In this chapter we have attempted to provide an overview of how computational modeling, especially those efforts employing large-scale neurobio-logically realistic models, has in the last few years started to be used in conjunction with functional neuroimaging data. In our view, the major reason why neural modeling has become more central for interpreting functional brain imaging data is that there is a paradigm shift currently underway in cognitive neuroscience (see Fuster 2000), brought about primarily by the increased importance of functional neuroimaging studies. When the main sources of information about the neural basis of human cognitive operations came from the neuropsychological investigation of brain damaged patients and from elec-trophysiological and lesion studies in nonhuman preparations, scientific investigation focused on single brain regions and aimed at the notion of functional segregation (Zeki 1990). Functional brain imaging, especially PET and fMRI, demonstrated the importance of networks and has necessitated the development of network analysis methods. For the future, combined use of the different kinds of functional brain imaging methods - those, like fMRI, that provide good spatial information and those, like MEG, that provide good temporal information - will, in our view, necessitate even more intensive use of computational neural dynamic models.
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