Starts at: April 24, 2014 4:30 PM
Ends at: 5:30 PM
Location: Scaife Hall 125
Speaker: Bruno Olshausen
Affiliation: University of California, Berkeley
Refreshments provided: Yes
The idea that the response properties of visual neurons may be characterized in terms of 'receptive fields' is widely accepted in vision science, and it has inspired the computational architecture of computer vision systems (so-called 'deep nets'). Yet a closer examination of how neurons actually respond to time-varying natural scenes, the complex neural architecture of visual cortex, and the biophysical properties of dendritic trees, leads us to question this idea. Here I will present neurophysiological evidence that V1 response properties are not well described in terms of receptive fields, and instead demand a different framework for thinking about what V1 is doing. I shall describe one possible framework based on inferential computations. Neural models of perceptual inference rely heavily upon recurrent computation in which information propagates both within and between levels of representation in a bi-directional manner. The inferential framework shifts us away from thinking of 'receptive fields' and 'tuning' of individual neurons, and instead toward how populations of neurons interact via horizontal and top-down feedback connections to perform collective computations.
Bruno Olshausen received B.S. and M.S. degrees in Electrical Engineering from Stanford University, and a Ph.D. in Computation and Neural Systems from the California Institute of Technology. He did his postdoctoral work in the Department of Psychology at Cornell University, and at the Center for Biological and Computational Learning at the Massachusetts Institute of Technology. He joined the faculty at the University of California at Davis in 1996, and in 2005 joined UC Berkeley, where he is currently Professor of Neuroscience and Optometry. He also directs the Redwood Center for Theoretical Neuroscience, a multidisciplinary group focusing on building mathematical and computational models of brain function.