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T

ype of Talk: UTCS Colloquium/AI

Speaker Name/Affiliation: Yoonsuck

Choe/Texas A&M University

Date/Time: Friday October 10 2008 11:0

0 a.m.

Location: ACES 2.402

Host: Risto Miikkulainen

Talk Title:
Motor System''s Role in Grounding Development and Recogni

tion in Vision

Talk Abstract:
Vision is basically a sensory modal

ity so it is no surprise
that the investigation into the brain''s visu

al functions has
been focused on its sensory aspect. Thus questions li

ke
(1) how can external geometric properties represented in
the int

ernal state of the visual system be grounded
(2) how do the visual cor

tical receptive fields (RFs) form
and (3) how can visual shapes be rec

ognized have all been
addressed within the framework of sensory informa

tion
processing. However this view is being challenged on
multiple
fronts with an increasing emphasis on the motor
aspect of visual func

tion. In this talk I will review works that
implicate the important ro

le of motor function in vision and
discuss our latest results touching
upon the issues of
grounding RF development and shape recognition. O

ur
main findings are that (1) motor primitives play a fundamental
r

ole in grounding (2) RF learning can be biased and enhanced
by the mot

or system and (3) shape recognition is easier with
motor-based represe

ntations than with sensor-based
representations. The insights we gained
here will help us better
understand visual cortical function. Also we
expect the motor-
oriented view of visual cortical function to be gener

alizable to
other sensory cortices such as somatosensory and auditory c

ortex.

Speaker Bio:
Yoonsuck Choe is an associate professor of C

omputer Science
and the director of the Brain Networks Laboratory at Te

xas A&M
University. He received his B.S. degree from Yonsei University

Korea (1993) and his M.A. and Ph.D. from the University of Texas

at Austin (1995 2001 respectively) all in Computer Science. His
rese

arch interests are in computational neuroscience computational
neuroana

tomy biologically inspired vision and neural networks.