BioCaen : A Causal Qualitative Network for Cerebral Information Propagation Modeling
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Functional brain mapping studies in humans may show contradictory results, as no one to one correspondence can be found between activated cerebral zones and cognitive functions . An explanation could be the networked physical organization of brain zones and the information propagation mechanisms through the network. As we focus on language-related brain subsystems, Al models are the single alternative to animal models . The brain being considered here as a physical, rather ill-defined system, AI qualitative approaches, especially causal methods, fit perfectly our purposes . The major constraint in the approach, i .e . the fact that phenomena related to the system's functioning must be time-ordered, is compatible with our knowledge on brain behavior . This paper presents a tentative two-level model of brain information propagation mechanisms . At the structural level, the brain anatomical structure is represented as a component network whose nodes are cerebral zones connected by propagating or inhibiting anatomical links (axon bundles) . At the functional/behavioral level, each zone is modeled by a causal qualitative network instanciated from a generic model . A component/connection approach derives the global functional model corresponding to a structural network-from the above models . As models must constantly evolve with new hypotheses and findings in brain research, we propose a flexible hypothesis simulator », BioCaen, for implementing them. BioCaen is an offspring of Ca-En (Bousson & Trave-Massuyes, 1993, 1994) that extends its capabilities by : (1) dealing with components, (2) giving more flexibility to time-variation expressions, (3) coding causal network nodes by couples of variables .
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