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Connections to the Center for Vision Research

Why Isn’t There a Seam on the Color Wheel?   Creative Mind at Brown University

What do Science and Art have to offer each other?  Everett company stages school

RISD Museum presents Spencer Finch: Painting Air: Co-sponsored by Center for Vision Research

 
 

working at

 

the interface of disciplines

 

Perception

 

Computational

Vision

 

Neurobiology

 

Ophthalmology

 

Machine

Vision

 

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This was a very popular & well attended lecture given by Anthony

Barnhart during the month of November. Left to right:  Anthony

Barnhart, Michael Paradiso, John Davenport.

Photo credit: Tracey Maroni

 
 

The Center for Vision Research

at the

Brown Institute for Brain Science

 

 

CVR 2014-2015 Lecture Series 

Wed, April  8

Bruno Olshausen, PhD

Helen Wills Neuroscience Institute & School of Optometry

University of CA, Berkeley

Redwood Center for Theoretical Neuroscience

3:30 reception | 4:00pm lecture

Marcuvitz Auditorium Sidney Frank Hall

"Do V1 neurons have receptive fields?"

Abstract:

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.

Bio:

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 was a postdoctoral fellow in the Department of Psychology at

Cornell University, and in the Center for Biological and

Computational Learning at the Massachusetts Institute of

Technology.   From 1996-2005 he was Assistant and

subsequently Associate Professor in the Departments of

Psychology and Neurobiology, Physiology and Behavior at UC

Davis.  He is currently Professor of Neuroscience and Optometry,

and Director of the Redwood Center for Theoretical

Neuroscience at UC Berkeley.  Olshausen’s research focuses on

understanding the information-processing strategies employed

by the visual system for tasks such as object recognition and

scene analysis.