"A Bayesian Journey through our sixth sense: From sensors to cortex and beyond" Dora E. Angelaki Ph.D(Department of Neuroscience)-2016.3.18
报告题目：A Bayesian Journey through our sixth sense: From sensors to cortex and beyond
报告人：Dora E. Angelaki Ph.D Department of Neuroscience
报告人简介：Professor, Department of Neuroscience, Baylor College of Medicine; Department of Electrical and Computer Engineering, Rice University. Member of National Academy of Sciences (NAS); Member of American Academy of Arts and Sciences (AAAS). Her research focuses on understanding how multisensory information flows between subcortical and cortical brain areas, as well as the spatial navigation, decision-making and episodic memory circuits, and how internal states modulate this information flow. She and her research group use innovative approaches to explore and understand neural dynamics and network coding of multi-sensory and multi-modal information at multiple stages of processing under diverse naturalistic and perceptual contexts related to navigation, planning and perceptual decisions. They are interested in the neural implementation of canonical neural computations and how they go astray to result in sensory, motor, memory and cognitive deficits in diseases like autism and schizophrenia. The goal is to use this knowledge to understand computational principles of disease, to inspire artificial systems, to aid the development of prosthetics and other tools for understanding and treating deficits of sensory coding, spatial orientation, cognition and action.
报告简介：Navigation and spatial orientation are vital functions in our lives. Sensory information arises from the balance (vestibular) organs in the inner ear, as well as from visual optic flow and other sensory, motor and cognitive cues. As such, a fundamental aspect of our sensory experience is how information from different modalities is often seamlessly integrated into a unified percept. Both theory and behavioral studies have shown that humans and animals combine multiple cues, as well as prior experiences based on the statistics of our environment and our interactions with it, according to a statically optimal scheme derived from Bayesian probability theory. Using spatial orientation and navigation as natural tasks, we study both computational principles and their neural implementations in diverse subcortical and cortical circuits that process visual (optic flow) and vestibular (acceleration) signals.