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Computational Maps in the Visual Cortex
For machine vision systems to truly approach human capabilities, it is
crucial that we understand the specific computations and algorithms
performed by biological visual systems. This talk will present
results from a detailed, large-scale computational model that makes
significant advances in understanding how biological visual systems
develop and how they function. Simulations show how newborn
processing for orientation, eye preference, and motion direction can
be constructed using prenatal training patterns and learning
algorithms, and how postnatal learning from natural scenes can ensure
that the architecture is a good match to typical visual
patterns. These results explain how newborns can have an orientation
map at birth yet adapt to the visual environment, and they provide
concrete and novel predictions about lateral connection patterns and
visual illusions that can be tested in future experiments. They also
suggest that generating training patterns artificially is an efficient
way to develop a complex, adaptive device like a visual system.
Bio
James Bednar is a postdoctoral researcher in the Department of
Computer Science of the University of Texas at Austin. He completed
his Ph.D. in Computer Science at UT in spring 2002, and also has an
M.A. in Computer Science, a B.A. in Philosophy, and a B.S. in
Electrical Engineering. His research focuses on computational
modeling of cortical map development. Dr. Bednar is the lead author
of the forthcoming Topographica modeling software package, under
development through a Human Brain Project grant from the National
Institutes of Health.
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