NIMH CMN Machine Learning Talk Series: Alex Huth 2019/07/30 скачать в хорошем качестве

NIMH CMN Machine Learning Talk Series: Alex Huth 2019/07/30

How does the human brain process and represent the meaning of language? We investigate this question by building computational models of language processing and then using those models to predict functional magnetic resonance imaging (fMRI) responses to natural language stimuli. The technique we use, voxel-wise encoding models, provides a sensitive method for probing richly detailed cortical representations. This method also allows us to take advantage of natural stimuli, which elicit stronger, more reliable, and more varied brain responses than tightly controlled experimental stimuli. In this talk I will discuss how we have used these methods to study how the human brain represents the meaning of language, and how those representations are linked to visual representations. The results suggest that the language and visual systems in the human brain might form a single, contiguous map over which meaning is represented. About our speaker Hailing originally from southern California, Alex attended the California Institute of Technology (Caltech), where he earned a BS computational neuroscience in 2007. There he began doing neuroscience research under professor Christof Koch, studying how the brain processes sound in the congenitally blind. Alex stayed in professor Koch’s lab for a year after graduation, and then moved north to UC Berkeley in 2008. Alex's PhD work at Berkeley with professor Jack Gallant focused on using modern computational methods to understand and model how our brains extract meaning from both vision and language. After receiving his PhD in 2013, Alex stayed in professor Gallant’s lab for 3 more years as a postdoc, during which time he published a landmark paper on how the brain processes language. This work received substantial attention from the popular press, and also led to Alex receiving research awards from the Burroughs Wellcome Fund and the Alfred P. Sloan Foundation. In 2017, Alex moved to UT Austin to begin a position as an assistant professor jointly in the departments of Computer Science and Neuroscience. His growing lab at UT aims to use incredibly large brain response datasets collected from single individuals in order to understand how the brain processes language at an unprecedented level of detail.