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Perhaps the greatest problem facing neuroscience is to understand how phenomena of mind can emerge from activity in the brain. Current cognitive neuroscience, the study of brain systems for cognition, most frequently has adopted network-based computational models when making direct relations between mind and brain. Such models involve processing nodes (ideally physically localizable) interacting in specific patterns within a functional network.
The popularity of computational network models has been motivated in part by the success of hierarchical and modular theories of brain mechanisms, as is best illustrated by the visual system. Here, sensory afferent information is processed serially by distinctly localized subcortical and cortical systems that define features ultimately “bound” together into a specific percept. This model is hierarchical in that perception develops from a partially sequential progression of information-processing steps. The model is modular as it includes distinct processing elements that add definable characteristics to perceptual experience, such as color or motion.
Such models have provided both an explanation for neurological deficits with brain lesions and predictions of possible types of deficits. The hierarchical nature is consistent with the lack of any visual experience with damage to the primary visual cortex, for example. The