Seminar: Representation of Natural Images in V4

SpeakerBin Yu
AffiliationDepartments of Statistics and EECS, University of California, Berkeley
DateFriday, 30 Nov 2012
Time12:00 - 13:00
LocationChadwick B05 LT
Event seriesComputer Science Seminars
Description

www.stat.berkeley.edu/~binyu

The functional organization of area V4 in the mammalian ventral visual
pathway is far from being well understood. V4 is believed to play an
important role in the recognition of shapes and objects and in visual
attention, but the complexity of this cortical area makes it hard to
analyze. In particular, no current model of V4 has shown good predictions
for neuronal responses to natural images and there is no consensus on
the primary role of V4.

In this talk, we present analysis of electrophysiological data
on the response of V4 neurons to natural images.
We propose a new computational model that achieves comparable prediction
performance for V4 as for V1 neurons. Our model does not rely on any
pre-defined image features but only on invariance and sparse coding
principles. We interpret our model using sparse principal component
analysis and discover two groups of neurons: those selective to texture
versus those selective to contours. This supports the thesis that one
primary role of V4 is to extract objects from background in the
visual field. Moreover, our study also confirms the diversity of V4
neurons. Among those selective to contours, some of them are selective
to orientation, others to acute curvature features.
(This is joint work with J. Mairal, Y. Benjamini, B. Willmore, M. Oliver
and J. Gallant.)

Bio:
Bin Yu is Chancellor's Professor in the Departments of Statistics
and of Electrical Engineering & Computer Science at UC Berkeley.
She has published over 100 scientific papers in premier journals
in Statistics, EECS, remote sensing and neuroscience, in a wide range
of research areas including empirical process theory, information theory
(MDL), MCMC methods, signal processing, machine learning, high dimensional
data inference (boosting and Lasso and sparse modeling in general), and
interdisciplinary data problems. She has served on many editorial boards
for journals such as Annals of Statistics, Journal of American Statistical
Association, and Journal of Machine Learning Research.

She was a 2006 Guggenheim Fellow, co-recipient of the Best Paper Award
of IEEE Signal Processing Society in 2006, and the 2012 Tukey Memorial
Lecturer of the Bernoulli Society (selected every four years).
She is a Fellow of AAAS, IEEE, IMS (Institute of Mathematical Statistics)
and ASA (American Statistical Association).

She is currently President-Elect of IMS (Institute of Mathematical
Statistics), and serving on the Scientific Advisory Board of IPAM
(Institute for Pure and Applied Mathematics) and on the Board of
Mathematical Sciences and Applications of NAS. She was co-chair of the
National Scientific Committee of SAMSI (Statistical and Applied
Mathematical Sciences Institute), and on the Board of
Governors of the IEEE-Information Theory Society.

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