Date & Time: Friday, 27 Nov., 3:30-5:30 p.m.
Venue: Rm 202, Centre for Humanities and Social Sciences Education, NYMU
About the themeHow does the brain control behavior? What do neural circuits compute? The field of cognitive neuroscience is thriving thanks to advances in neuroanatomy, neurophysiology, neuroimaging, and cognitive psychology. Given the rich set of empirical observations aggregated from these fields, an emerging challenge is to provide a unified theory that relates different levels of analysis, explaining how neural systems in the brain give rise to intelligent human behavior. Computational cognitive neuroscience addresses the above questions by studying neural representations and mechanisms from a theoretical perspective. It can offer new insights beyond brain-behavior correlations. One example is the application of machine learning/pattern recognition techniques to brain data for understanding neural representations and for developing brain-computer interfaces. Another example is the class of neural models that construct system-level architectures from the first principles, such as local computation and competitive normalization. These computational models can quantitatively simulate cognitive functions using plausible neural mechanisms and explain seemingly conflicting data in a coherent framework. In this presentation, differences among computational approaches in (cognitive) neuroscience will be contrasted. The past, present, and future of computational cognitive neuroscience will also be discussed.
About the speaker
Tsung-Ren Huang (黃從仁)
Department of Psychology Neurobiology and Cognitive Science Center
National Taiwan University