Cerebral modeling and dynamic Bayesian networks

Cerebral modeling and dynamic Bayesian networks

The understanding and the prediction of the clinical outcomes of focal or degenerative cerebral lesions, as well as the assessment of rehabilitation procedures, necessitate knowing the cerebral substratum of cognitive or sensorimotor functions. This is achieved by activation studies, where subjects are asked to perform a specific task while data of their brain functioning are obtained through f...

Dynamic Bayesian modeling of the cerebral activity

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. A...

Dynamic Bayesian Networks for Language Modeling

Language Modeling Using Dynamic Bayesian Networks

In this paper we propose a new approach to language modeling based on dynamic Bayesian networks. The principle idea of our approach is to find the dependence relations between variables that represent different linguistic units (word, class, concept, ...) that constitutes a language model. In the context of this paper the linguistic units that we consider are syntactic classes and words. Our ap...

Modeling Neuronal Interactivity using Dynamic Bayesian Networks

Functional Magnetic Resonance Imaging (fMRI) has enabled scientists to look into the active brain. However, interactivity between functional brain regions, is still little studied. In this paper, we contribute a novel framework for modeling the interactions between multiple active brain regions, using Dynamic Bayesian Networks (DBNs) as generative models for brain activation patterns. This fram...