Feedforward and feedback processes in visual recognition
Dr. Thomas Serre of Brown University
Wednesday, November 16, 2016 · 1:30PM · HEC 101
Perception involves a complex interaction between feedforward sensory-driven processes and feedback modulatory processes. A mechanistic understanding of feedforward processing, and its limitations, is a necessary first step towards elucidating key aspects of perceptual functions and dysfunctions. Towards this goal, rapid categorization paradigms have been extensively used to study our core perceptual abilities when the visual system is forced to operate under strong time constraints. In this talk, I will present experimental evidence from a recent electrophysiology study with awake behaving monkeys engaged in a rapid natural scene categorization task suggesting that feedforward processes may provide a satisfactory description of rapid visual processes. I will then provide an overview of our ongoing effort to develop an accurate computational model of rapid visual recognition. I will discuss the limitations of such feedforward architectures towards higher level visual reasoning and highlight our recent work extending them with feedback mechanisms.
Dr. Serre is a Manning Assistant Professor in Cognitive Linguistic & Psychological Sciences at Brown University. He received a PhD in computational neuroscience from MIT (Cambridge, MA) in 2006 and an MSc in EECS from Télécom Bretagne (Brest, France) in 2000. His research focuses on understanding the brain mechanisms underlying the recognition of objects and complex visual scenes using a combination of behavioral, imaging and physiological techniques. These experiments fuel the development of quantitative computational models that try not only to mimic the processing of visual information in the cortex but also to match human performance in complex visual tasks. He is the recipient of an NSF early career award and DARPA young faculty award. His research has been featured in the BBC series "Visions from the Future" and appeared in several news articles (The Economist, New Scientist, Scientific American, IEEE Computing in Science and Technology, Technology Review and Slashdot).