Abstract: Perception involves a complex interaction between feedforward (bottom-up) sensory-driven inputs and feedback (top-down) attention and memory-driven processes. A mechanistic understanding of feedforward processing, and its limitations, is a necessary first step towards elucidating key aspects of perceptual functions and dysfunctions.

In this talk, I will review our ongoing effort towards the development of a large-scale, neurophysiologically accurate computational model of feedforward visual processing in the primate cortex. I will present experimental evidence from a recent electrophysiology study with awake behaving monkeys engaged in a rapid natural scene categorization task. The results suggest that bottom-up processes may provide a satisfactory description of the very first pass of information in the visual cortex. I will then survey recent work extending a feedforward hierarchical model from the processing of 2D shape to motion, depth and color. I will show that this bio-inspired approach to computer vision performs on par with, or better than state-of-the-art computer vision systems in several real-world applications. This demonstrates that neuroscience may contribute powerful new ideas and approaches to computer science and artificial intelligence.

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.