The Challenges to Coupling Dynamic Geospatial Models

Dynamic Causal Models for phase coupling

This paper presents an extension of the Dynamic Causal Modelling (DCM) framework to the analysis of phase-coupled data. A weakly coupled oscillator approach is used to describe dynamic phase changes in a network of oscillators. The use of Bayesian model comparison allows one to infer the mechanisms underlying synchronization processes in the brain. For example, whether activity is driven by mas...

Geospatial Big Data: Challenges and Opportunities

Dynamic Models for Substrate Coupling in Mixed-Mode Systems

In modern monolithic integrated circuits, substrate coupling is a major concern in mixed-mode systems design. Noise injected into the common substrate by fast switching digital blocks may affect the correct functioning or performance of the overall system. Verification of such systems implies the availability of accurate and simulation-efficient substrate coupling models. For frequencies up to ...

Intelligent Structures and Coupling in Mathematical Models : Examples from Dynamic

Pathogenesis of Epilepsy: Challenges in Animal Models

  Epilepsy is one of the most common chronic disorders affecting individuals of all ages. A greater understanding of pathogenesis in epilepsy will likely provide the basis fundamental for development of new antiepileptic therapies that aim to prevent the epileptogenesis process or modify the progression of epilepsy in addition to treatment of epilepsy symptomatically. Therefore, severa...