麦戈文脑科学中心学术报告

Title: Computational model-based analysis of learning and memory: stress, genes and prediction

Speaker: Gediminas Luksys, Ph.D.

Postdoctoral fellow in Cognitive Neuroscience&Human Genetics

Time: June 1 2016 (Wed) 15:00-16:00

Venue: Room 1113, Wang Kezhen Building

How we learn, recall our memories, and use them for making decisions depend on our genes as well as on environmental modulators, such as stress and emotion. Cognitive performance is the outcome of several neurobiologically distinct mental processes, some of which are not easily amenable to direct observation. Their roles can, however, be dissociated with computational models. Using examples from animal reinforcement learning under stress and imaging genetics of human memory, I will show how computational models can be used to discover neural and genetic correlates of various cognitive phenomena, and provide their computational explanations. I will also discuss about interpretation, replication and generalization of model-based analysis results as well as how neuroinformatics can facilitate that. Finally, I will propose future directions and applications of this approach, such as automated characterization of individual decision making profiles and individualized cognitive neurotherapeutics.

欢迎各位老师同学积极参加！