Analyzing the Neural Dynamics of Emotional Associative Memory Encoding through Data-Driven Modeling

Researchers have been interested in studying the connection between emotion and memory for decades but much remains unknown due to the elusive nature of the human brain. Furthering our understanding of the phenomenon is crucial for improving the treatment of neurological disorders associated with emotion dysregulation, as well as for enhancing...

Bayesian system identification for structures considering spatial and temporal dependencies

Bayesian system identification, including parameter estimation and model selection, is widely used to infer partially known, unobservable parameters of the models of physical systems when measurement data is available. A common assumption in the Bayesian system identification...

Non-Linear Bayesian System Identification of Cortical Responses Using Volterra Series

The human sensorimotor system can be seen as a complex network in which the brain plays an important role, resulting in a difficult-to-understand relation between proprioceptive stimuli and cortical responses. However, understanding this relationship is of added value for understanding various diseases which cause dysfunctionality. In recent...