Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Introduction to Bayesian Networks
Introduction to Bayesian Networks
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Conventional methods used for the interpretation of activation data provided by functional neuroimaging techniques provide useful insights on what the networks of cerebral structures are, and when and how much they activate. However, they do not explain how the activation of these large-scale networks derives from the cerebral information processing mechanisms involved in cognitive functions. At this global level of representation, the human brain can be considered as a dynamic biological system. Dynamic Bayesian networks seem currently the most promising modeling paradigm. Our modeling approach is based on the anatomical connectivity of cerebral regions, the information processing within cerebral areas and the causal influences that connected regions exert on each other. The capabilities of the formalism's current version are illustrated by the modeling of a phonemic categorization process, explaining the different cerebral activations in normal and dyslexic subjects. The simulation data are compared to experimental results [Ruff et al, 2001].